From 15157451872678373ce96833a9d16923cb0d4d82 Mon Sep 17 00:00:00 2001
From: Yao Lu <luyao11175@gmail.com>
Date: Fri, 3 May 2024 00:56:14 -0700
Subject: [PATCH] vila1.5 release

---
 README.md                                     |  152 +-
 data_prepare/README.md                        |  121 +-
 demo_images/av.png                            |  Bin
 demo_trt_llm/README.md                        |  121 ++
 demo_trt_llm/apply_patch.sh                   |   14 +
 demo_trt_llm/av.png                           |  Bin 0 -> 383825 bytes
 demo_trt_llm/build_visual_engine.py           |  300 +++
 demo_trt_llm/convert_checkpoint.py            |  468 +++++
 demo_trt_llm/quantization/quantize.py         |   64 +
 demo_trt_llm/quantization/quantize_by_ammo.py |  433 +++++
 demo_trt_llm/run.py                           |  513 ++++++
 environment_setup.sh                          |   16 +-
 inference_test/inference_test.py              |   67 +-
 inference_test/test_data/car_blocker.png      |  Bin 0 -> 383825 bytes
 inference_test/test_data/car_safety.jpg       |  Bin 0 -> 164214 bytes
 inference_test/test_data/count_dog_2.png      |  Bin 0 -> 279426 bytes
 inference_test/test_data/count_giraff_4.png   |  Bin 0 -> 992519 bytes
 inference_test/test_data/count_panda_3.png    |  Bin 0 -> 406857 bytes
 inference_test/test_data/factory.jpg          |  Bin 0 -> 108623 bytes
 inference_test/test_data/factory_count_1.jpg  |  Bin 0 -> 102684 bytes
 inference_test/test_data/factory_count_2.jpg  |  Bin 0 -> 122020 bytes
 inference_test/test_data/factory_count_3.jpg  |  Bin 0 -> 138574 bytes
 inference_test/test_data/factory_count_4.jpg  |  Bin 0 -> 79769 bytes
 inference_test/test_data/factory_count_5.jpg  |  Bin 0 -> 73313 bytes
 inference_test/test_data/factory_count_6.jpg  |  Bin 0 -> 63018 bytes
 inference_test/test_data/factory_count_7.jpg  |  Bin 0 -> 58049 bytes
 inference_test/test_data/factory_count_8.jpg  |  Bin 0 -> 53240 bytes
 inference_test/test_data/flamingo_1.png       |  Bin 542011 -> 699830 bytes
 inference_test/test_data/fuyu-capm.png        |  Bin 0 -> 639903 bytes
 inference_test/test_data/fuyu-heavy-table.png |  Bin 0 -> 205200 bytes
 inference_test/test_data/menu_1.png           |  Bin 0 -> 1973710 bytes
 inference_test/test_data/menu_2.png           |  Bin 0 -> 1731194 bytes
 inference_test/test_data/orange_price.png     |  Bin 0 -> 219437 bytes
 inference_test/test_data/parking_sign.png     |  Bin 0 -> 1106945 bytes
 inference_test/test_data/princess_1.png       |  Bin 216390 -> 281183 bytes
 inference_test/test_data/princess_2.png       |  Bin 227817 -> 290297 bytes
 inference_test/test_data/tow_car.png          |  Bin 0 -> 271157 bytes
 inference_test/test_data/visual_inference.png |  Bin 0 -> 2250344 bytes
 llava/__init__.py                             |    2 +-
 llava/conversation.py                         |   39 +
 llava/data/__init__.py                        |    3 +-
 llava/data/dataset.py                         | 1115 +++++++----
 llava/data/dataset_impl/coyo_recap.py         |   76 +
 llava/data/dataset_impl/hiertext.py           |  191 ++
 llava/data/dataset_impl/sam.py                |  185 ++
 llava/data/dataset_impl/textocr.py            |  280 +++
 llava/data/dataset_tar.py                     |  246 +++
 llava/data/datasets_mixture.py                |  235 ++-
 llava/data/simple_video_dataset.py            |  269 +++
 llava/data/simple_vila_webdataset.py          |  322 ++++
 llava/data/utils.py                           |   22 +
 llava/eval/eval_gpt_review.py                 |    0
 llava/eval/eval_gpt_review_bench.py           |    0
 llava/eval/eval_gpt_review_visual.py          |    0
 llava/eval/eval_mmmu.py                       |    0
 llava/eval/eval_mmvet.py                      |    0
 llava/eval/eval_pope.py                       |    0
 llava/eval/eval_science_qa.py                 |    3 +
 llava/eval/eval_science_qa_gpt4.py            |    0
 llava/eval/eval_science_qa_gpt4_requery.py    |    0
 llava/eval/eval_textvqa.py                    |    0
 llava/eval/evaluate_vqa.py                    |  147 ++
 llava/eval/evaluate_vqa_score.py              |  143 ++
 .../eval/generate_webpage_data_from_table.py  |    0
 llava/eval/m4c_evaluator.py                   |    0
 llava/eval/mathvista_utils/calculate_score.py |  204 +++
 llava/eval/mathvista_utils/extract_answer.py  |   81 +
 llava/eval/mathvista_utils/prompts/ext_ans.py |   42 +
 llava/eval/mathvista_utils/utilities.py       |  213 +++
 llava/eval/mmmu_utils/configs/llava1.5.yaml   |    0
 llava/eval/mmmu_utils/data_utils.py           |  199 +-
 llava/eval/mmmu_utils/eval_utils.py           |    0
 llava/eval/mmmu_utils/merge_jsons.py          |   22 +
 llava/eval/mmmu_utils/model_utils.py          |   65 +-
 llava/eval/model_qa.py                        |    1 +
 llava/eval/model_vqa.py                       |    8 +-
 llava/eval/model_vqa_loader.py                |    8 +-
 llava/eval/model_vqa_mmbench.py               |    8 +-
 llava/eval/model_vqa_mmmu.py                  |   58 +-
 llava/eval/model_vqa_mmmu_sharded.py          |  162 ++
 llava/eval/model_vqa_nextqa.py                |  199 ++
 llava/eval/model_vqa_qbench.py                |    8 +-
 llava/eval/model_vqa_science.py               |   14 +-
 llava/eval/model_vqa_video.py                 |   83 +-
 llava/eval/model_vqa_videodemo.py             |  187 ++
 llava/eval/model_vqa_videoperception.py       |  174 ++
 llava/eval/qa_baseline_gpt35.py               |    0
 llava/eval/{run_llava.py => run_vila.py}      |  102 +-
 llava/eval/summarize_gpt_review.py            |    0
 .../eval/table/answer/answer_alpaca-13b.jsonl |   80 -
 llava/eval/table/answer/answer_bard.jsonl     |   80 -
 llava/eval/table/answer/answer_gpt35.jsonl    |   80 -
 .../eval/table/answer/answer_llama-13b.jsonl  |   80 -
 .../eval/table/answer/answer_vicuna-13b.jsonl |   80 -
 .../table/caps_boxes_coco2014_val_80.jsonl    |   80 -
 llava/eval/table/model.jsonl                  |    5 -
 llava/eval/table/prompt.jsonl                 |    4 -
 llava/eval/table/question.jsonl               |   80 -
 .../review/review_alpaca-13b_vicuna-13b.jsonl |   80 -
 .../table/review/review_bard_vicuna-13b.jsonl |   80 -
 .../review/review_gpt35_vicuna-13b.jsonl      |   80 -
 .../review/review_llama-13b_vicuna-13b.jsonl  |   80 -
 llava/eval/table/reviewer.jsonl               |    4 -
 .../video/eval_benchmark_1_correctness.py     |    0
 .../eval_benchmark_2_detailed_orientation.py  |    0
 llava/eval/video/eval_benchmark_3_context.py  |    0
 llava/eval/video/eval_benchmark_4_temporal.py |    0
 .../video/eval_benchmark_5_consistency.py     |    0
 llava/eval/video/eval_video_nextqa.py         |  163 ++
 llava/eval/video/eval_video_perception.py     |   37 +
 llava/eval/video/eval_video_qa.py             |   11 +-
 llava/mm_utils.py                             |  228 ++-
 llava/model/__init__.py                       |    4 +-
 llava/model/apply_delta.py                    |   20 +-
 llava/model/builder.py                        |  241 ++-
 llava/model/configuration_llava.py            |   51 +
 llava/model/language_model/builder.py         |  111 ++
 .../{llava_mixtral.py => llava_gemma.py}      |   81 +-
 llava/model/language_model/llava_llama.py     |  148 +-
 llava/model/language_model/llava_mistral.py   |    7 +-
 llava/model/language_model/llava_mpt.py       |    2 +-
 llava/model/llava_arch.py                     |  542 ++++--
 llava/model/make_delta.py                     |   23 +-
 llava/model/multimodal_encoder/builder.py     |   54 +-
 .../model/multimodal_encoder/clip_encoder.py  |   39 +-
 .../multimodal_encoder/image_processor.py     |  546 ++++++
 .../intern/configuration_intern_vit.py        |  119 ++
 .../intern/flash_attention.py                 |   76 +
 .../intern/modeling_intern_vit.py             |  547 ++++++
 .../multimodal_encoder/intern_encoder.py      |   53 +
 .../model/multimodal_encoder/radio_encoder.py |  317 ++++
 .../multimodal_encoder/siglip/__init__.py     |   94 +
 .../siglip/configuration_siglip.py            |  302 +++
 .../siglip/convert_siglip_to_hf.py            |  413 +++++
 .../siglip/image_processing_siglip.py         |  302 +++
 .../siglip/modeling_siglip.py                 | 1186 ++++++++++++
 .../siglip/processing_siglip.py               |  143 ++
 .../siglip/tokenization_siglip.py             |  389 ++++
 .../multimodal_encoder/siglip_encoder.py      |   39 +
 .../multimodal_encoder/vision_encoder.py      |  146 +-
 .../multimodal_encoder/visualize_features.py  |  351 ++++
 .../multimodal_projector/base_projector.py    |  100 +
 llava/model/multimodal_projector/builder.py   |   67 +-
 llava/model/utils.py                          |   60 +-
 llava/train/args.py                           |   62 +-
 llava/train/callbacks/autoresume_callback.py  |   61 +
 llava/train/llava_trainer.py                  |  184 +-
 llava/train/short_video_filter.py             |   25 +
 llava/train/slurm_utils.py                    |   78 +
 llava/train/train.py                          |  437 +++--
 .../models/gemma/__init__.py                  |  121 ++
 .../models/gemma/configuration_gemma.py       |  147 ++
 .../models/gemma/modeling_gemma.py            | 1322 ++++++++++++++
 .../{ => models}/llama/configuring_llama.py   |    0
 .../{ => models}/llama/modeling_llama.py      |    1 -
 .../models/llama/tokenization_llama.py        |  482 +++++
 .../models/mistral/__init__.py                |   62 +
 .../models/mistral/configuration_mistral.py   |  152 ++
 .../models/mistral/modeling_mistral.py        | 1364 ++++++++++++++
 .../models/mixtral/__init__.py                |   62 +
 .../models/mixtral/configuration_mixtral.py   |  169 ++
 .../models/mixtral/modeling_mixtral.py        | 1627 +++++++++++++++++
 llava/train/utils.py                          |  115 ++
 llava/unit_test_utils.py                      |  102 ++
 llava/wids/wids.py                            |   18 +-
 pyproject.toml                                |   11 +-
 scripts/convert_gqa_for_eval.py               |   16 +
 scripts/convert_mmbench_for_submission.py     |   18 +-
 scripts/convert_mmvet_for_eval.py             |   16 +-
 scripts/convert_seed_for_submission.py        |   16 +-
 scripts/convert_sqa_to_llava.py               |   16 +-
 scripts/convert_sqa_to_llava_base_prompt.py   |   16 +-
 scripts/convert_vizwiz_for_submission.py      |   16 +-
 scripts/convert_vqav2_for_submission.py       |   21 +-
 scripts/extract_mm_projector.py               |   49 +-
 scripts/merge_lora_weights.py                 |   24 -
 scripts/v1_5/eval/ai2d.sh                     |   16 +
 scripts/v1_5/eval/ai2d_sharded.sh             |   41 +
 scripts/v1_5/eval/chartqa.sh                  |   25 +
 scripts/v1_5/eval/chartqa_sharded.sh          |   73 +
 scripts/v1_5/eval/copy_predictions.py         |   51 +
 scripts/v1_5/eval/docvqa.sh                   |   26 +
 scripts/v1_5/eval/eval_all.sh                 |   26 +-
 scripts/v1_5/eval/gqa.sh                      |   20 +-
 scripts/v1_5/eval/gqa_sharded.sh              |   49 +
 scripts/v1_5/eval/llavabench.sh               |   18 +-
 scripts/v1_5/eval/llavabench_sharded.sh       |   55 +
 scripts/v1_5/eval/mmbench.sh                  |   14 +-
 scripts/v1_5/eval/mmbench_cn.sh               |   16 +-
 scripts/v1_5/eval/mmbench_cn_sharded.sh       |   50 +
 scripts/v1_5/eval/mmbench_sharded.sh          |   53 +
 scripts/v1_5/eval/mme.sh                      |   19 +-
 scripts/v1_5/eval/mme_sharded.sh              |   47 +
 scripts/v1_5/eval/mmmu.sh                     |   13 +-
 scripts/v1_5/eval/mmmu_sharded.sh             |   45 +
 scripts/v1_5/eval/mmvet.sh                    |   18 +-
 scripts/v1_5/eval/mmvet_sharded.sh            |   49 +
 scripts/v1_5/eval/pope.sh                     |   14 +-
 scripts/v1_5/eval/pope_sharded.sh             |   45 +
 scripts/v1_5/eval/qbench.sh                   |    2 +-
 scripts/v1_5/eval/qbench_zh.sh                |    0
 scripts/v1_5/eval/seed.sh                     |   14 +-
 scripts/v1_5/eval/seed_sharded.sh             |   48 +
 scripts/v1_5/eval/sqa.sh                      |   19 +-
 scripts/v1_5/eval/sqa_sharded.sh              |   88 +
 scripts/v1_5/eval/textvqa.sh                  |   14 +-
 scripts/v1_5/eval/textvqa_sharded.sh          |   46 +
 scripts/v1_5/eval/video_chatgpt/eval_all.sh   |   22 +
 .../eval_benchmark_1_correctness.sh           |   17 +
 .../video_chatgpt/eval_benchmark_2_detail.sh  |   18 +
 .../eval_benchmark_3_contextual.sh            |   18 +
 .../eval_benchmark_4_temporal.sh              |   18 +
 .../eval_benchmark_5_consistency.sh           |   18 +
 .../eval/video_chatgpt/eval_qa_activitynet.sh |   13 +
 .../v1_5/eval/video_chatgpt/eval_qa_msrvtt.sh |   14 +
 .../v1_5/eval/video_chatgpt/eval_qa_msvd.sh   |   13 +
 .../v1_5/eval/video_chatgpt/eval_qa_nextqa.sh |   15 +
 .../eval/video_chatgpt/eval_qa_perception.sh  |   11 +
 .../v1_5/eval/video_chatgpt/eval_qa_tgif.sh   |   14 +
 scripts/v1_5/eval/video_chatgpt/run_all.sh    |   18 +
 .../run_benchmark_1_correctness.sh            |   18 +
 .../video_chatgpt/run_benchmark_2_detail.sh   |   18 +
 .../run_benchmark_3_contextual.sh             |   18 +
 .../video_chatgpt/run_benchmark_4_temporal.sh |   17 +
 .../run_benchmark_5_consistency.sh            |   18 +
 .../eval/video_chatgpt/run_qa_activitynet.sh  |   49 +
 .../run_qa_activitynet_sharded.sh             |   50 +
 .../v1_5/eval/video_chatgpt/run_qa_msrvtt.sh  |   48 +
 .../video_chatgpt/run_qa_msrvtt_sharded.sh    |   51 +
 .../v1_5/eval/video_chatgpt/run_qa_msvd.sh    |   49 +
 .../eval/video_chatgpt/run_qa_msvd_sharded.sh |   52 +
 .../v1_5/eval/video_chatgpt/run_qa_nextqa.sh  |   48 +
 .../video_chatgpt/run_qa_nextqa_sharded.sh    |   51 +
 .../eval/video_chatgpt/run_qa_perception.sh   |   48 +
 .../run_qa_perception_sharded.sh              |   51 +
 .../v1_5/eval/video_chatgpt/run_qa_tgif.sh    |   47 +
 .../eval/video_chatgpt/run_qa_tgif_sharded.sh |   50 +
 scripts/v1_5/eval/vizwiz.sh                   |   14 +-
 scripts/v1_5/eval/vizwiz_multigpu.sh          |   42 +
 scripts/v1_5/eval/vizwiz_sharded.sh           |   44 +
 scripts/v1_5/eval/vqav2.sh                    |   16 +-
 scripts/v1_5/eval/vqav2_sharded.sh            |   48 +
 .../v1_5/{paper => release/13b}/1_mm_align.sh |   18 +-
 .../13b/2_pretrain.sh}                        |   25 +-
 scripts/v1_5/{paper => release/13b}/3_sft.sh  |   25 +-
 scripts/v1_5/release/3b-s2/1_mm_align.sh      |   62 +
 scripts/v1_5/release/3b-s2/2_pretrain.sh      |   71 +
 scripts/v1_5/release/3b-s2/3_sft.sh           |   61 +
 scripts/v1_5/release/3b/1_mm_align.sh         |   59 +
 scripts/v1_5/release/3b/2_pretrain.sh         |   68 +
 scripts/v1_5/release/3b/3_sft.sh              |   58 +
 scripts/v1_5/release/40b/1_mm_align.sh        |   58 +
 scripts/v1_5/release/40b/2_pretrain.sh        |   67 +
 scripts/v1_5/release/40b/3_sft.sh             |   57 +
 scripts/v1_5/release/8b/1_mm_align.sh         |   59 +
 scripts/v1_5/release/8b/2_pretrain.sh         |   68 +
 scripts/v1_5/release/8b/3_sft.sh              |   58 +
 257 files changed, 22378 insertions(+), 2606 deletions(-)
 mode change 100755 => 100644 demo_images/av.png
 create mode 100644 demo_trt_llm/README.md
 create mode 100644 demo_trt_llm/apply_patch.sh
 create mode 100755 demo_trt_llm/av.png
 create mode 100644 demo_trt_llm/build_visual_engine.py
 create mode 100644 demo_trt_llm/convert_checkpoint.py
 create mode 100644 demo_trt_llm/quantization/quantize.py
 create mode 100644 demo_trt_llm/quantization/quantize_by_ammo.py
 create mode 100644 demo_trt_llm/run.py
 create mode 100755 inference_test/test_data/car_blocker.png
 create mode 100644 inference_test/test_data/car_safety.jpg
 create mode 100644 inference_test/test_data/count_dog_2.png
 create mode 100644 inference_test/test_data/count_giraff_4.png
 create mode 100644 inference_test/test_data/count_panda_3.png
 create mode 100644 inference_test/test_data/factory.jpg
 create mode 100644 inference_test/test_data/factory_count_1.jpg
 create mode 100644 inference_test/test_data/factory_count_2.jpg
 create mode 100644 inference_test/test_data/factory_count_3.jpg
 create mode 100644 inference_test/test_data/factory_count_4.jpg
 create mode 100644 inference_test/test_data/factory_count_5.jpg
 create mode 100644 inference_test/test_data/factory_count_6.jpg
 create mode 100644 inference_test/test_data/factory_count_7.jpg
 create mode 100644 inference_test/test_data/factory_count_8.jpg
 create mode 100644 inference_test/test_data/fuyu-capm.png
 create mode 100644 inference_test/test_data/fuyu-heavy-table.png
 create mode 100644 inference_test/test_data/menu_1.png
 create mode 100644 inference_test/test_data/menu_2.png
 create mode 100644 inference_test/test_data/orange_price.png
 create mode 100644 inference_test/test_data/parking_sign.png
 create mode 100644 inference_test/test_data/tow_car.png
 create mode 100644 inference_test/test_data/visual_inference.png
 create mode 100644 llava/data/dataset_impl/coyo_recap.py
 create mode 100644 llava/data/dataset_impl/hiertext.py
 create mode 100644 llava/data/dataset_impl/sam.py
 create mode 100644 llava/data/dataset_impl/textocr.py
 create mode 100644 llava/data/dataset_tar.py
 create mode 100644 llava/data/simple_video_dataset.py
 create mode 100644 llava/data/simple_vila_webdataset.py
 create mode 100644 llava/data/utils.py
 mode change 100644 => 100755 llava/eval/eval_gpt_review.py
 mode change 100644 => 100755 llava/eval/eval_gpt_review_bench.py
 mode change 100644 => 100755 llava/eval/eval_gpt_review_visual.py
 mode change 100644 => 100755 llava/eval/eval_mmmu.py
 mode change 100644 => 100755 llava/eval/eval_mmvet.py
 mode change 100644 => 100755 llava/eval/eval_pope.py
 mode change 100644 => 100755 llava/eval/eval_science_qa.py
 mode change 100644 => 100755 llava/eval/eval_science_qa_gpt4.py
 mode change 100644 => 100755 llava/eval/eval_science_qa_gpt4_requery.py
 mode change 100644 => 100755 llava/eval/eval_textvqa.py
 create mode 100755 llava/eval/evaluate_vqa.py
 create mode 100755 llava/eval/evaluate_vqa_score.py
 mode change 100644 => 100755 llava/eval/generate_webpage_data_from_table.py
 mode change 100644 => 100755 llava/eval/m4c_evaluator.py
 create mode 100755 llava/eval/mathvista_utils/calculate_score.py
 create mode 100755 llava/eval/mathvista_utils/extract_answer.py
 create mode 100755 llava/eval/mathvista_utils/prompts/ext_ans.py
 create mode 100755 llava/eval/mathvista_utils/utilities.py
 mode change 100644 => 100755 llava/eval/mmmu_utils/configs/llava1.5.yaml
 mode change 100644 => 100755 llava/eval/mmmu_utils/data_utils.py
 mode change 100644 => 100755 llava/eval/mmmu_utils/eval_utils.py
 create mode 100644 llava/eval/mmmu_utils/merge_jsons.py
 mode change 100644 => 100755 llava/eval/mmmu_utils/model_utils.py
 mode change 100644 => 100755 llava/eval/model_qa.py
 mode change 100644 => 100755 llava/eval/model_vqa.py
 mode change 100644 => 100755 llava/eval/model_vqa_loader.py
 mode change 100644 => 100755 llava/eval/model_vqa_mmbench.py
 mode change 100644 => 100755 llava/eval/model_vqa_mmmu.py
 create mode 100644 llava/eval/model_vqa_mmmu_sharded.py
 create mode 100755 llava/eval/model_vqa_nextqa.py
 mode change 100644 => 100755 llava/eval/model_vqa_qbench.py
 mode change 100644 => 100755 llava/eval/model_vqa_science.py
 mode change 100644 => 100755 llava/eval/model_vqa_video.py
 create mode 100755 llava/eval/model_vqa_videodemo.py
 create mode 100755 llava/eval/model_vqa_videoperception.py
 mode change 100644 => 100755 llava/eval/qa_baseline_gpt35.py
 rename llava/eval/{run_llava.py => run_vila.py} (61%)
 mode change 100644 => 100755
 mode change 100644 => 100755 llava/eval/summarize_gpt_review.py
 delete mode 100644 llava/eval/table/answer/answer_alpaca-13b.jsonl
 delete mode 100644 llava/eval/table/answer/answer_bard.jsonl
 delete mode 100644 llava/eval/table/answer/answer_gpt35.jsonl
 delete mode 100644 llava/eval/table/answer/answer_llama-13b.jsonl
 delete mode 100644 llava/eval/table/answer/answer_vicuna-13b.jsonl
 delete mode 100644 llava/eval/table/caps_boxes_coco2014_val_80.jsonl
 delete mode 100644 llava/eval/table/model.jsonl
 delete mode 100644 llava/eval/table/prompt.jsonl
 delete mode 100644 llava/eval/table/question.jsonl
 delete mode 100644 llava/eval/table/review/review_alpaca-13b_vicuna-13b.jsonl
 delete mode 100644 llava/eval/table/review/review_bard_vicuna-13b.jsonl
 delete mode 100644 llava/eval/table/review/review_gpt35_vicuna-13b.jsonl
 delete mode 100644 llava/eval/table/review/review_llama-13b_vicuna-13b.jsonl
 delete mode 100644 llava/eval/table/reviewer.jsonl
 mode change 100644 => 100755 llava/eval/video/eval_benchmark_1_correctness.py
 mode change 100644 => 100755 llava/eval/video/eval_benchmark_2_detailed_orientation.py
 mode change 100644 => 100755 llava/eval/video/eval_benchmark_3_context.py
 mode change 100644 => 100755 llava/eval/video/eval_benchmark_4_temporal.py
 mode change 100644 => 100755 llava/eval/video/eval_benchmark_5_consistency.py
 create mode 100644 llava/eval/video/eval_video_nextqa.py
 create mode 100644 llava/eval/video/eval_video_perception.py
 mode change 100644 => 100755 llava/eval/video/eval_video_qa.py
 create mode 100644 llava/model/configuration_llava.py
 create mode 100644 llava/model/language_model/builder.py
 rename llava/model/language_model/{llava_mixtral.py => llava_gemma.py} (56%)
 create mode 100644 llava/model/multimodal_encoder/image_processor.py
 create mode 100644 llava/model/multimodal_encoder/intern/configuration_intern_vit.py
 create mode 100644 llava/model/multimodal_encoder/intern/flash_attention.py
 create mode 100644 llava/model/multimodal_encoder/intern/modeling_intern_vit.py
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diff --git a/README.md b/README.md
index 077b9852..a55efa8b 100644
--- a/README.md
+++ b/README.md
@@ -12,39 +12,76 @@
 [VILA arxiv](https://arxiv.org/abs/2312.07533) / [VILA Demo](https://vila-demo.hanlab.ai/) / [VILA Huggingface](https://huggingface.co/collections/Efficient-Large-Model/vila-on-pre-training-for-visual-language-models-65d8022a3a52cd9bcd62698e)
 
 ## 💡 Introduction
-VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by [AWQ](https://arxiv.org/pdf/2306.00978.pdf) 4bit quantization through [TinyChat](https://github.com/mit-han-lab/llm-awq/tree/main/tinychat) framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge. 
+VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling **multi-image** VLM and **video understanding** capabilities. VILA is deployable on the edge, including Jetson Orin and laptop by [AWQ](https://arxiv.org/pdf/2306.00978.pdf) 4bit quantization through [TinyChat](https://github.com/mit-han-lab/llm-awq/tree/main/tinychat) framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge. 
 
  
 ## 💡 News
-- [2024/03] [VILA-2.7B](https://huggingface.co/Efficient-Large-Model/VILA-2.7b) is released! It can run on NVIDIA Jetson Orin Nano ([Tutorial](https://www.jetson-ai-lab.com/tutorial_nano-vlm.html)) and appeared at GTC 2024!
-- [2024/03] VILA is accepted by CVPR 2024!
+- [2024/05] We release [AWQ](https://arxiv.org/pdf/2306.00978.pdf)-quantized 4bit VILA-1.5 models supported by [TinyChat](https://github.com/mit-han-lab/llm-awq/tree/main/tinychat) and [TensorRT-LLM](demo_trt_llm) backends. 
+- [2024/05] We release VILA-1.5, which comes with four model sizes (3B/8B/13B/40B) and offers native support for multi-image and video understanding.
 - [2024/02] We release [AWQ](https://arxiv.org/pdf/2306.00978.pdf)-quantized 4bit VILA models, deployable on Jetson Orin and laptops through [TinyChat](https://github.com/mit-han-lab/llm-awq/tree/main/tinychat) and [TinyChatEngine](https://github.com/mit-han-lab/TinyChatEngine).
 - [2024/02] VILA is released. We propose interleaved image-text pretraining that enables multi-image VLM. VILA comes with impressive in-context learning capabilities. We open source everything: including training code, evaluation code, datasets, model ckpts.
 - [2023/12] [Paper](https://arxiv.org/abs/2312.07533) is on Arxiv!
 
 ## Performance
 
-| $~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~$ | Prec. | VQAv2 | GQA  | VizWiz  | SQA-I  | VQA-T | POPE  | MME     | MMB  | MMB-CN | SEED | llava-bench | MM-Vet | Average (w/o MME) |
-| ----------------- | ---------------- | ---------------- | ---------- | ----------- | ----------- | ----- | ----- | ------- | ---- | ------ | ---- | ----------- | ------ | ----------------- |
-| VILA-7B           | fp16 | 80.3 | 63.1 | 59.6 | 68.0    | 62.6  | 86.3  | 1489.4 | 69.8 | 61.0     | 61.7 | 75.2        | 35.1   | 65.7              |
-| VILA-7B-AWQ       | int4 | 80.1             | 63.0 | 57.8              | 68.0    | 61.9  | 85.3  | 1486.3 | 68.8 | 59.0     | 61.3 | 75.8        | 35.9   | 65.2              |
-| VILA-13B          | fp16| 80.5             | 63.6 | 63.1              | 70.5  | 64.0    | 86.3  | 1553.6  | 73.8 | 66.7   | 62.8 | 78.3        | 42.6   | 68.4              |
-| VILA-13B-AWQ      | int4 | 80.4             | 63.6 | 63.0              | 71.2  | 63.5  | 87.0 | 1552.9  | 73.6 | 66.3   | 62.2 | 77.6        | 42.0   | 68.2              |
+### Image QA Benchmarks
+
+| $~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~$ | Prec. | VQAv2 | GQA  | VizWiz | SQA-I | VQA-T | POPE | MME     | MMB  | MMB-CN | SEED | SEED-I | MMMU | llava-bench | MM-Vet | Average (w/o MME) |
+| -------------------------------- | ----- | ----- | ---- | ------ | ----- | ----- | ---- | ------- | ---- | ------ | ---- | ------ | ---- | ----------- | ------ | ----------------- |
+| VILA1.5-3B                     | fp16  | 80.4  | 61.5 | 53.5   | 69.0  | 60.4  | 85.9 | 1442.44 | 63.4 | 52.7   | 60.9 | 67.9   | 33.3 | 75.9        | 35.4   | 61.6              |
+| VILA1.5-3B-AWQ                 | int4  | 80.0  | 61.1 | 53.8   | 67.8  | 60.4  | 85.9 | 1437.34 | 63.3 | 51.4   | 59.8 | 66.6   | 32.7 | 75.0        | 37.3   | 61.2              |
+| VILA1.5-3B-S2                  | fp16  | 79.8  | 61.4 | 61.3   | 69.6  | 63.4  | 85.3 | 1431.65 | 62.8 | 52.2   | 60.0 | 66.4   | 32.8 | 76.7        | 38.6   | 62.3              |
+| VILA1.5-3B-S2-AWQ              | int4  | 79.4  | 61.3 | 62.3   | 69.2  | 63.0  | 85.8 | 1417.06 | 61.6 | 51.5   | 59.1 | 65.7   | 33.4 | 77.1        | 36.7   | 62.0              |
+| Llama-3-VILA1.5-8B               | fp16  | 80.9  | 61.9 | 58.7   | 79.9  | 66.3  | 84.4 | 1577.01 | 72.3 | 66.2   | 64.2 | 71.4   | 36.9 | 80.0        | 38.3   | 66.3              |
+| Llama-3-VILA1.5-8B-AWQ           | int4  | 80.3  | 61.7 | 59.3   | 79.0  | 65.4  | 82.9 | 1593.65 | 71.0 | 64.9   | 64.0 | 71.1   | 36.0 | 79.0        | 37.2   | 65.5              |
+| VILA1.5-13B                      | fp16  | 82.8  | 64.3 | 62.6   | 80.1  | 65.0  | 86.3 | 1569.55 | 74.9 | 66.3   | 65.1 | 72.6   | 37.9 | 80.8        | 44.3   | 67.9              |
+| VILA1.5-13B-AWQ                  | int4  | 82.7  | 64.5 | 63.3   | 79.7  | 64.7  | 86.7 | 1531.35 | 74.7 | 66.7   | 65.1 | 72.6   | 37.8 | 81.9        | 46.4   | 68.2              |
+| VILA1.5-40B                      | fp16  | 84.3  | 64.6 | 62.2   | 87.2  | 73.6  | 87.3 | 1726.82 | 82.4 | 80.2   | 69.1 | 75.8   | 51.9 | 81.3        | 53.0   | 73.3              |
+| VILA1.5-40B-AWQ                  | int   | 84.1  | 64.4 | 61.3   | 86.7  | 73.2  | 88.2 | 1714.79 | 83.2 | 79.6   | 68.9 | 75.6   | 49.3 | 83.0        | 51.4   | 73.0              |
+
+
+<sup>NOTE: VQAV2 and VizWiz are test-dev, for MMMU we report the validation set accuracy.</sup>
+
+### Video QA Benchmarks
+
+| $~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~$ | Prec. | Perception Test | ActivityNet  | MSVD | MSRVTT | TGIF |
+| -------------------------------- | ----- | ----- | ---- | ------ | ----- | ----- | 
+| VILA1.5-3B                     | fp16  | 39.3  | 50.2 | 76.6  | 57.5  | 51.7  |
+| VILA1.5-3B-S2                  | fp16  | 39  | 50.7 | 76.9  | 57.6 | 51.7 |
+| Llama-3-VILA1.5-8B               | fp16  | 41.8  | 54.3 | 78.3   | 60.1  | 54.1 |
+| VILA1.5-13B                      | fp16  | 39.3  | 54.7 | 77.9   | 60.2  | 56  | 
+| VILA1.5-40B                      | fp16  | 41.7  | 58 | 80.1  | 63 | 58.2 | 
+
 
-<sup>NOTE: The benchmark results are slightly different from what we report in the paper due to refactoring of the codebase based on LLava-1.5 and re-train the model. VQAV2 and VizWiz are test-dev.</sup>
 
 ### Inference speed ( Token/sec )
 
-| $~~~~~~$ | Precision |  A100 | 4090 | Orin |
-| --- | --- |--- | --- | --- |
-| VILA-7B | fp16 | 81.6 | 58.5 | 11.5 |
-| VILA-7B-AWQ| int4  |155.3| 168.1| 35.6 |
-| VILA-13B | fp16 | 48.5 | OOM | 6.1 |
-| VILA-13B-AWQ | int4  | 102.1| 99.0| 17.5 |
+| $~~~~~~$               | Precision | A100  | 4090  | Orin |
+| ---------------------- | --------- | ----- | ----- | ---- |
+| VILA1.5-3B           | fp16      | 104.6 | 137.6 | 25.4 |
+| VILA1.5-3B-AWQ       | int4      | 182.8 | 215.5 | 42.5 |
+| VILA1.5-3B-S2        | fp16      | 104.3 | 137.2 | 24.6 |
+| VILA1.5-3B-S2-AWQ    | int4      | 180.2 | 219.3 | 40.1 |
+| Llama-3-VILA1.5-8B     | fp16      | 74.9  | 57.4  | 10.2 |
+| Llama-3-VILA1.5-8B-AWQ | int4      | 168.9 | 150.2 | 28.7 |
+| VILA1.5-13B            | fp16      | 50.9  | OOM   | 6.1  |
+| VILA1.5-13B-AWQ        | int4      | 115.9 | 105.7 | 20.6 |
+| VILA1.5-40B            | fp16      | OOM  | OOM   | --  |
+| VILA1.5-40B-AWQ        | int4      | 57.0 | OOM | -- |
+
+<sup>NOTE: Measured using the [TinyChat](https://github.com/mit-han-lab/llm-awq/tinychat) backend at batch size = 1.</sup>
 
 
 ## VILA Examples
 
+### Video captioning
+
+https://github.com/Efficient-Large-Model/VILA/assets/156256291/c9520943-2478-4f97-bc95-121d625018a6
+
+Prompt: Elaborate on the visual and narrative elements of the video in detail.
+
+Caption: The video shows a person's hands working on a white surface. They are folding a piece of fabric with a checkered pattern in shades of blue and white. The fabric is being folded into a smaller, more compact shape. The person's fingernails are painted red, and they are wearing a black and red garment. There are also a ruler and a pencil on the surface, suggesting that measurements and precision are involved in the process.
+
 ### In context learning
 <img src="demo_images/demo_img_1.png" height="239">
 <img src="demo_images/demo_img_2.png" height="250">
@@ -76,18 +113,21 @@ conda create -n vila python=3.10 -y
 conda activate vila
 
 pip install --upgrade pip  # enable PEP 660 support
+# this is optional if you prefer to system built-in nvcc.
+conda install -c nvidia cuda-toolkit -y
 wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.4.2/flash_attn-2.4.2+cu118torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
 pip install flash_attn-2.4.2+cu118torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
 pip install -e .
 pip install -e ".[train]"
 
 pip install git+https://github.com/huggingface/transformers@v4.36.2
-cp -rv ./llava/train/transformers_replace/* ~/anaconda3/envs/vila/lib/python3.10/site-packages/transformers/models/
+site_pkg_path=$(python -c 'import site; print(site.getsitepackages()[0])')
+cp -rv ./llava/train/transformers_replace/* $site_pkg_path/transformers/
 ```
 
 ## Training
 
-VILA training contains three steps
+VILA training contains three steps, for specific hyperparameters, please check out the [scripts/v1_5](scripts/v1_5) folder:
 
 ### Step-1: Alignment
 We utilize LLaVA-CC3M-Pretrain-595K dataset to align the textual and visual modalities.
@@ -98,12 +138,6 @@ The stage 1 script takes in two parameters and it can run on a single 8xA100 nod
 bash scripts/v1_5/paper/1_mm_align.sh [BASE_MODEL_PATH] [OUTPUT_NAME]
 ```
 
-| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
-| --- | ---: | ---: | ---: | ---: | ---: |
-| VILA-7B | 256 | 2e-5 | 1 | 4096 | 0 |
-| VILA-13B | 256 | 2e-5 | 1 | 4096 | 0 |
-
-
 ### Step-2: Pretraining
 We use MMC4 and Coyo dataset to train VLM with interleaved image-text pairs.
 
@@ -113,10 +147,6 @@ bash scripts/v1_5/paper/2_pretrain_mmc4_coyo.sh [CODE_PATH] [BASE_MODEL_PATH] [S
 
 The stage 2 script takes in four arguments. `CODE_PATH` is the absolute path to our VILA codebase, `BASE_MODEL_PATH` has similar meaning to what is presented in the stage 1 script. `STAGE1_PATH` points to the `OUTPUT_NAME` of stage 1 (i.e. where the stage 1 checkpoint is stored). `OUTPUT_NAME` is the desired folder name under `checkpoints` that saves the pretraining checkpoint. The script we provided for this stage is executed on slurm, and we expect it to execute on 16 nodes (128 GPUs).
 
-| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
-| --- | ---: | ---: | ---: | ---: | ---: |
-| VILA-7B | 1024 | 5e-5 | 1 | 4096 | 0 |
-| VILA-13B | 1024 | 5e-5 | 1 | 4096 | 0 |
 
 ### Step-3: Supervised fine-tuning
 This is the last stage of VILA training, in which we tune the model to follow multimodal instructions on a subset of M3IT, FLAN and ShareGPT4V. This stage runs on a 8xA100 node.
@@ -126,26 +156,27 @@ bash scripts/v1_5/paper/3_sft.sh [STAGE2_PATH] [OUTPUT_NAME]
 ```
 The stage 3 script takes in two arguments. `STAGE2_PATH` points to the `OUTPUT_NAME` of the stage 2 script (i.e. where the stage 2 checkpoint is stored). `OUTPUT_NAME` is the desired folder name under `checkpoints` that stores the final checkpoint.
 
-| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
-| --- | ---: | ---: | ---: | ---: | ---: |
-| VILA-7B | 128 | 2e-5 | 1 | 4096 | 0 |
-| VILA-13B | 128 | 2e-5 | 1 | 4096 | 0 |
+## Evaluations
 
-### Training with fewer GPUs
-To train with fewer GPUs/nodes, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly.  As long as the global batch size same (`per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`) are kept the same, the training precision will not be affected.
+### Image Benchmarks
+You can follow [Llava1.5 eval](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md) to download all datasets. After downloading all datasets, please put them under `playground/data/eval`.
 
-Stage 1 completes within 3.5 (7B) - 5.5 (13B) hours on 8xA100, Stage 2 completes within 30 hours on 128xA100 for VILA-7B, and stage 3 completes in 25 (7B) - 40 (13B) hours on 8xA100.
+Please make the following changes to the MME evaluation script. Please search for:
 
-See [data_prepare/README.md](data_prepare/README.md) for more information about how to prepare datasets.
+```python
+data_path='MME_Benchmark_release_version'
+``` 
 
-## Evaluations
+and replace it with:
 
-You can follow [Llava1.5 eval](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md) to download all datasets. After downloading all datasets, please put them under `playground/data/eval`.
+```python
+data_path=os.path.join(script_dir, 'MME_Benchmark_release_version')
+```
 
 We provide a push-the-button script to perform evaluation on all 10 datasets that do not require GPT-assisted evaluation:
 
 ```bash
-./scripts/v1_5/eval/eval_all.sh [CHECKPOINT_PATH] [MODEL_NAME]
+./scripts/v1_5/eval/eval_all.sh [CHECKPOINT_PATH] [MODEL_NAME] [CONV_MODE]
 ```
 
 This script takes in two parameters, `CHECKPOINT_PATH` points to the stage 3 model checkpoint, and `MODEL_NAME` will be the name of evaluation results.
@@ -163,26 +194,37 @@ python scripts/v1_5/eval/copy_predictions.py [MODEL_NAME]
 
 You will be able to find the predictions under `playground/data/predictions_upload/[MODEL_NAME]` after executing this script.
 
+
+### Video Benchmarks
+
+Please follow the evaluation steps in [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/main/TRAIN_AND_VALIDATE.md#data-for-validating) for dataset preparation.
+
+```bash
+./scripts/v1_5/eval/video_chatgpt/run_all.sh [CHECKPOINT_PATH] [MODEL_NAME] [CONV_MODE]
+./scripts/v1_5/eval/video_chatgpt/eval_all.sh [MODEL_NAME]
+```
+
+
 ## Inference
 
 We provide snippets for quick inference with user prompts and images.
 
-VILA-7B inference:
+Llama-3-VILA1.5-8B inference:
 ```bash
-python -W ignore llava/eval/run_llava.py \
-    --model-path Efficient-Large-Model/VILA-7B \
+python -W ignore llava/eval/run_vila.py \
+    --model-path Efficient-Large-Model/Llama-3-VILA1.5-8b \
     --conv-mode vicuna_v1 \
     --query "<image>\n Please describe the traffic condition." \
-    --image-file "demo_images/av.png"
+    --image-file "av.png"
 ```
 
-VILA-13B inference:
+VILA1.5-3B video inference:
 ```bash
-python -W ignore llava/eval/run_llava.py \
-    --model-path Efficient-Large-Model/VILA-13B \
+python -W ignore llava/eval/run_vila.py \
+    --model-path Efficient-Large-Model/VILA1.5-3b \
     --conv-mode vicuna_v1 \
-    --query "<image>\n Please describe the traffic condition." \
-    --image-file "demo_images/av.png"
+    --query "<video>\n Please describe this video." \
+    --image-file "demo.mp4"
 ```
 
 ## Quantization and Deployment
@@ -201,25 +243,26 @@ We further support our AWQ-quantized 4bit VILA models on various CPU platforms w
 
 ## Checkpoints
 
-We release [VILA-7B](https://hf.co/Efficient-Large-Model/VILA-7b), [VILA-13B](https://hf.co/Efficient-Large-Model/VILA-13b), [VILA-7B-4bit-AWQ](https://hf.co/Efficient-Large-Model/VILA-7b-4bit-awq) and [VILA-13B-4bit-AWQ](https://hf.co/Efficient-Large-Model/VILA-13b-4bit-awq).
+We release [VILA1.5-3B](https://hf.co/Efficient-Large-Model/VILA1.5-3b), [Llama-3-VILA1.5-8B](https://hf.co/Efficient-Large-Model/Llama-3-VILA1.5-8b), [VILA1.5-13B](https://hf.co/Efficient-Large-Model/VILA1.5-13b), [VILA1.5-40B](https://hf.co/Efficient-Large-Model/VILA1.5-40b) and the 4-bit [AWQ](https://arxiv.org/abs/2306.00978)-quantized models [VILA1.5-3B-AWQ](https://hf.co/Efficient-Large-Model/VILA1.5-3b-AWQ), [Llama-3-VILA1.5-8B-AWQ](https://hf.co/Efficient-Large-Model/Llama-3-VILA1.5-8b-AWQ), [VILA1.5-13B-AWQ](https://hf.co/Efficient-Large-Model/VILA1.5-13b-AWQ), [VILA1.5-40B-AWQ](https://hf.co/Efficient-Large-Model/VILA1.5-40b-AWQ).
 
 ## 🔒 License
 - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
 - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
 - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
-    - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
+    - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA. For LLAMA3-VILA checkpoints terms of use, please refer to the [LLAMA3 License](https://llama.meta.com/llama3/license/) for additional details.
     - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
     - [Dataset Licenses](./data_prepare/LICENSE) for each one used during training.
 
 ## Team
 | | | |  
 | --- | --- | ---|
-[*Ji Lin](https://www.linji.me/): OpenAI (work done at Nvidia and MIT) |  [*Hongxu Yin](https://hongxu-yin.github.io/): Nvidia |  [*Yao Lu](https://scholar.google.com/citations?user=OI7zFmwAAAAJ&hl=en): Nvidia
+[*Yao Lu](https://scholar.google.com/citations?user=OI7zFmwAAAAJ&hl=en): Nvidia|  [*Hongxu Yin](https://hongxu-yin.github.io/): Nvidia |  [*Ji Lin](https://www.linji.me/): OpenAI (work done at Nvidia and MIT) 
 [Wei Ping](https://scholar.google.com/citations?user=6gKEYRgAAAAJ&hl=en): Nvidia |   [Pavlo Molchanov](https://www.pmolchanov.com/): Nvidia |  [Andrew Tao](https://scholar.google.com/citations?user=Wel9l1wAAAAJ&hl=en): Nvidia |  
 [Haotian Tang](http://kentang.net/): MIT |  [Shang Yang](https://ys-2020.github.io/): MIT |  [Ligeng Zhu](https://lzhu.me/): Nvidia, MIT |  
 [Wei-Chen Wang](https://weichenwang.me/): MIT |  [Fuzhao Xue](https://xuefuzhao.github.io/): Nvidia, NUS |  [Yunhao Fang](https://seerkfang.github.io/): Nvidia, UCSD |  
-[Yukang Chen](https://yukangchen.com/): Nvidia, CUHK |  [Yue Shen](https://www.linkedin.com/in/yue-james-shen/): Nvidia | [Huizi Mao](https://scholar.google.com/citations?user=r5WezOYAAAAJ&hl=zh-CN): Nvidia |  
-[Jan Kautz](https://jankautz.com/): Nvidia  |   [Mohammad Shoeybi](https://scholar.google.com/citations?user=62ElavIAAAAJ&hl=en): Nvidia |  [Song Han](http://songhan.mit.edu/): Nvidia, MIT
+[Yukang Chen](https://yukangchen.com/): Nvidia, CUHK | [Zhuoyang Zhang](https://openreview.net/profile?id=~Zhuoyang_Zhang1): Nvidia, Tsinghua Univ. | [Yue Shen](https://www.linkedin.com/in/yue-james-shen/): Nvidia | 
+[Wei-Ming Chen](https://scholar.google.com/citations?user=6xFvyJwAAAAJ&hl=en): Nvidia |  [Huizi Mao](https://scholar.google.com/citations?user=r5WezOYAAAAJ&hl=zh-CN): Nvidia | [Baifeng Shi](https://bfshi.github.io/): Nvidia, UC Berkeley | 
+[Jan Kautz](https://jankautz.com/): Nvidia | [Mohammad Shoeybi](https://scholar.google.com/citations?user=62ElavIAAAAJ&hl=en): Nvidia | [Song Han](http://songhan.mit.edu/): Nvidia, MIT
 
 
 ## Citations
@@ -237,6 +280,7 @@ We release [VILA-7B](https://hf.co/Efficient-Large-Model/VILA-7b), [VILA-13B](ht
 
 # Acknowledgement
 - [LLaVA](https://github.com/haotian-liu/LLaVA): the codebase we built upon. Thanks for their wonderful work.
+- [InternVL](https://github.com/OpenGVLab/InternVL): for open-sourcing InternViT (used in VILA1.5-40b) and the [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) data blend (inspired by LLaVA-1.6) used in all VILA1.5 models.
 - [Vicuna](https://github.com/lm-sys/FastChat): the amazing open-sourced large language model!
 - [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT): we borrowed video evaluation script from this repository.
-- [MMC4](https://github.com/allenai/mmc4), [COYO-700M](https://github.com/kakaobrain/coyo-dataset), [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT), [OpenORCA/FLAN](https://huggingface.co/datasets/Open-Orca/FLAN), [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V) for providing datasets used in this research.
+- [MMC4](https://github.com/allenai/mmc4), [COYO-700M](https://github.com/kakaobrain/coyo-dataset), [M3IT](https://huggingface.co/datasets/MMInstruction/M3IT), [OpenORCA/FLAN](https://huggingface.co/datasets/Open-Orca/FLAN), [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V), [WIT](google-research-datasets/wit), [GSM8K-ScRel](https://github.com/OFA-Sys/gsm8k-ScRel/blob/main/data/train_use.jsonl), [VisualGenome](https://visualgenome.org/api/v0/api_home.html), [VCR](https://visualcommonsense.com/download/), [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA), [Shot2Story](https://github.com/bytedance/Shot2Story/blob/master/DATA.md), [Youcook2](http://youcook2.eecs.umich.edu/), [Vatex](https://eric-xw.github.io/vatex-website/download.html), [ShareGPT-Video](https://huggingface.co/datasets/ShareGPTVideo/train_video_and_instruction) for providing datasets used in this research.
diff --git a/data_prepare/README.md b/data_prepare/README.md
index 13c2032a..05baf87d 100644
--- a/data_prepare/README.md
+++ b/data_prepare/README.md
@@ -1,41 +1,34 @@
-
 # Data Preparation for Training VILA
 
 To train VILA, we used the following datasets:
 
-| Stage                   | Datasets                    |
-| ----------------------- | --------------------------- |
-| 1. Initialize projector | CC3M                         |
-| 2. Pre-training         | MMC4-core, COYO-700M subset |
-| 3. SFT                  | LLaVA-1.5, VFLAN, ShareGPT, TextFLAN      |
+| Stage                   | Datasets                                                                         |
+| ----------------------- | -------------------------------------------------------------------------------- |
+| 1. Initialize projector | CC3M                                                                             |
+| 2. Pre-training         | MMC4-core, COYO-700M, ShreGPT4V_pretrain                                                      |
+| 3. SFT                  | LLaVA-Next mixture, VFLAN, WIT, GSM8K-ScRel-SFT, Sherlock, ScienceQA, Shot2story, Video_ChatGPT, Youcook2, Vatex, ShareGPT_Video |
 
-### LLaVa-CC3M-Pretrain
-We use [LLaVA-CC3M-Pretrain-595K](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/chat.json) to train the visual language projector
 
-```bash
-mkdir -p ./playground/data/LLaVA-Pretrain
-cd ./playground/data/LLaVA-Pretrain
 
-# download chat.json and process
-huggingface-cli download liuhaotian/LLaVA-CC3M-Pretrain-595K chat.json --repo-type dataset --local-dir . --local-dir-use-symlinks False
-mv chat.json LLaVA-CC3M-Pretrain-595K.json
 
-# download images.zip and process
-huggingface-cli download liuhaotian/LLaVA-CC3M-Pretrain-595K images.zip --repo-type dataset --local-dir . --local-dir-use-symlinks False
-unzip images.zip -d images
-```
+### LLaVa-CC3M-Pretrain
+
+We use [LLaVA-CC3M-Pretrain-595K](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/chat.json) to train the visual language projector
 
 ### MMC4-Core Dataset
-Due to the limit of compute, we pre-train VILA on the smaller core set of MMC4 instead of the full set. 
 
-1. Firstly, download the annotations of the MMC4-core dataset here: https://github.com/allenai/mmc4. We used the non-fewer-face split, and you may need to request the access [here](https://forms.gle/VYtcNY8aYaUANK9f8). 
+Due to the limit of compute, we pre-train VILA on the smaller core set of MMC4 instead of the full set.
+
+1. Firstly, download the annotations of the MMC4-core dataset here: https://github.com/allenai/mmc4. We used the non-fewer-face split, and you may need to request the access [here](https://forms.gle/VYtcNY8aYaUANK9f8).
 
 2. Now modify the input and output path in `mmc4_downloader.py` and run the following script to scrawl the MMC4 images:
+
 ```bash
 cd mmc4
 python mmc4_downloader.py
 ```
-Note that due to the expiration of image urls, you may end up getting a subset of the entire corpus. 
+
+Note that due to the expiration of image urls, you may end up getting a subset of the entire corpus.
 
 The scrawling may take a long time. Optionally, you can also shard the workload over multiple jobs/machines concurrently to speed up the process:
 
@@ -59,39 +52,43 @@ python mmc4_merger.py
 ```
 
 ### COYO-700M Dataset
+
 1. Download the metadata of COYO-700M:
+
 ```bash
 huggingface-cli download kakaobrain/coyo-700m --repo-type dataset --local-dir coyo-700m --local-dir-use-symlinks False
 ```
 
-2. Scrawl the COYO images. Note that here we only keep a 20% subset in each shard with the highest CLIP similarity, to balance compute budget and data quality. 
+2. Scrawl the COYO images. Note that here we only keep a 20% subset in each shard with the highest CLIP similarity, to balance compute budget and data quality.
 
 There are totally 128 shards of annotations. Now download each one with the script:
+
 ```bash
 cd coyo
 for SHARD in {0..127}; do
-    python coyo_downloader.py $SHARD  
+    python coyo_downloader.py $SHARD
 done
 ```
 
 3. Split downloaded COYO data into multiple shards:
+
 ```bash
 python coyo_splitter.py
 ```
 
-###  LLaVA-1.5 Instruction Data
+### LLaVA-1.5 Instruction Data
 
 We use this [file](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json) in our experiments. Please download this dataset from LLaVA authors.
 
 ```bash
-mkdir -p ./playground/data/LLaVA-Pretrain
-cd ./playground/data/LLaVA-Pretrain
 huggingface-cli download liuhaotian/LLaVA-Instruct-150K llava_v1_5_mix665k.json --repo-type dataset
 ```
 
-
 ### VFlan dataset
-1. Download FLAN  datasets:
+
+#### TextFLAN
+
+1. Download FLAN datasets:
 
 ```bash
 huggingface-cli download Open-Orca/FLAN --repo-type dataset --local-dir FLAN --local-dir-use-symlinks False
@@ -104,7 +101,8 @@ cd sft
 python preprocess_flan.py
 ```
 
-### M3IT Dataset
+#### M3IT Dataset
+
 1. Download M3IT datasets:
 
 ```bash
@@ -123,11 +121,68 @@ python preprocess_m3it.py
 python split_vflan.py
 ```
 
-### ShareGPT4v
+### LLaVA-Next mixture
+
+You can follow this [page](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) to prepare the data mixture that is proposed by LLaVA-Next.
+
+### Shot2story
+
+Please follow this [page](https://github.com/bytedance/Shot2Story/blob/master/DATA.md) to download the videos. The JSON file can be downloaded with
+
+```bash
+huggingface-cli download mit-han-lab/vila-dataset shot2story_shotonly.json
+ --repo-type dataset --local-dir shot2story --local-dir-use-symlinks False
+```
+
 
-The ShareGPT data can be obtained [mit-han-lab/ShareGPT4V](https://huggingface.co/datasets/mit-han-lab/ShareGPT4V).
-    * Note the original ShareGPT4v dataset contains some samples with file ids (sa_XXXX) and repeative response. We filter those bad examples and reduced the samples from 100K -> 96K (for caption) and 1.2m -> 1.17m (for pretraining). Then we re-combine them into a single file.
+### Video_ChatGPT
+
+You can follow this [page](https://github.com/mbzuai-oryx/Video-ChatGPT/blob/main/README.md#video-instruction-dataset-open_file_folder) to prepare Video_ChatGPT dataset.
+
+### Youcook2
+
+Please follow this [page](http://youcook2.eecs.umich.edu/) to download the videos. The JSON file can be downloaded with
+
+```bash
+huggingface-cli download mit-han-lab/vila-dataset youcook_filtered_v3.json --repo-type dataset --local-dir youcook2 --local-dir-use-symlinks False
+```
+
+### Vatex
+
+Please follow this [page](https://eric-xw.github.io/vatex-website/download.html) to download the videos. The JSON file can be downloaded with
+
+```bash
+huggingface-cli download mit-han-lab/vila-dataset vatex_filtered_v3.json --repo-type dataset --local-dir vatex --local-dir-use-symlinks False
+```
+
+### ShareGPT_Video
+
+You can follow this [page](https://huggingface.co/datasets/ShareGPTVideo/train_video_and_instruction) to prepare ShareGPT_Video dataset.
+
+### WIT
+
+The original WIT data can be obtained [google-research-datasets/wit](https://github.com/google-research-datasets/wit/tree/main). \* We subsample ~538K english data from the original WIT dataset and curate a llava conversation format JSON file.
 
 ```bash
-huggingface-cli download mit-han-lab/ShareGPT4V --repo-type dataset --local-dir coyo-700m --local-dir-use-symlinks False
+huggingface-cli download mit-han-lab/vila-dataset wit_processed_538k.json --repo-type dataset --local-dir WIT --local-dir-use-symlinks False
 ```
+
+### GSM8K-ScRel-SFT
+
+We add some math data [gsm8k-ScRel](https://github.com/OFA-Sys/gsm8k-ScRel/blob/main/data/train_use.jsonl) to our SFT stage.
+
+### Sherlock
+
+The image files of Sherlock can be obtained from [VisualGenome](https://visualgenome.org/api/v0/api_home.html) and [VCR](https://visualcommonsense.com/download/) separately. The llava conversation format JSON file can be downloaded with
+
+```bash
+huggingface-cli download mit-han-lab/vila-dataset sherlock_317k.json --repo-type dataset --local-dir sherlock --local-dir-use-symlinks False
+```
+
+### ScienceQA 
+
+We use the train split of ScienceQA. The image data of the train split can be obtained from [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA) or their [huggingface repo](https://huggingface.co/datasets/derek-thomas/ScienceQA). The llava conversation format JSON file can be downloaded with
+
+```bash
+huggingface-cli download mit-han-lab/vila-dataset scienceqa_train_12k.json --repo-type dataset --local-dir scienceqa --local-dir-use-symlinks False
+```
\ No newline at end of file
diff --git a/demo_images/av.png b/demo_images/av.png
old mode 100755
new mode 100644
diff --git a/demo_trt_llm/README.md b/demo_trt_llm/README.md
new file mode 100644
index 00000000..066abd6e
--- /dev/null
+++ b/demo_trt_llm/README.md
@@ -0,0 +1,121 @@
+# Run VILA demo on x86_64 machine
+
+## Build TensorRT-LLM
+The first step to build TensorRT-LLM is to fetch the sources:
+```bash
+# TensorRT-LLM uses git-lfs, which needs to be installed in advance.
+apt-get update && apt-get -y install git git-lfs
+git lfs install
+
+git clone https://github.com/NVIDIA/TensorRT-LLM.git 
+cd TensorRT-LLM
+git checkout 66ef1df492f7bc9c8eeb01d7e14db01838e3f0bd
+git submodule update --init --recursive
+git lfs pull
+```
+Create a TensorRT-LLM Docker image and approximate disk space required to build the image is 63 GB:
+```bash
+make -C docker release_build
+```
+
+After launching the docker image, please install the following dependency:
+```bash
+pip install git+https://github.com/bfshi/scaling_on_scales.git
+pip install git+https://github.com/huggingface/transformers@v4.36.2
+```
+## Build TensorRT engine of VILA model
+
+### For Vila 1.0:
+
+Please refer to the [documentation from TRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/multimodal#llava-and-vila) to deploy the model.
+
+### For Vila 1.5:
+
+1. Setup
+```bash
+# clone vila
+git clone https://github.com/Efficient-Large-Model/VILA.git
+
+# enter the demo folder
+cd <VILA-repo>/demo_trt_llm
+
+# apply patch to /usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py for vila1.5
+sh apply_patch.sh
+
+# download vila checkpoint
+export MODEL_NAME="vila1.5-2.7b"
+git clone https://huggingface.co/Efficient-Large-Model/${MODEL_NAME} tmp/hf_models/${MODEL_NAME}
+```
+
+2. TensorRT Engine building using `FP16` and inference
+
+Build TensorRT engine for LLaMA part of VILA from HF checkpoint using `FP16`:
+```bash
+python convert_checkpoint.py \
+    --model_dir tmp/hf_models/${MODEL_NAME} \
+    --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
+    --dtype float16
+
+trtllm-build \
+    --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \
+    --output_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \
+    --gemm_plugin float16 \
+    --use_fused_mlp \
+    --max_batch_size 1 \
+    --max_input_len 2048 \
+    --max_output_len 512 \
+    --max_multimodal_len 4096
+```
+
+3. Build TensorRT engines for visual components
+
+```bash
+python build_visual_engine.py --model_path tmp/hf_models/${MODEL_NAME} --model_type vila --vila_path ../
+```
+
+4. Run the example script
+```bash
+python run.py  \
+    --max_new_tokens 100 \
+    --hf_model_dir tmp/hf_models/${MODEL_NAME} \
+    --visual_engine_dir visual_engines/${MODEL_NAME} \
+    --llm_engine_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \
+    --image_file=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \
+    --input_text="<image>\n<image>\n Please elaborate what you see in the images?" \
+    --run_profiling
+
+# example output:
+...
+[Q] <image>\n<image>\n Please elaborate what you see in the images?
+[04/30/2024-21:32:11] [TRT-LLM] [I] 
+[A] ['The first image shows a busy street scene with a car driving through a crosswalk. There are several people walking on the sidewalk, and a cyclist is also visible. The second image captures a beautiful sunset with the iconic Merlion statue spouting water into the water body in the foreground. The Merlion statue is a famous landmark in Singapore, and the water spout is a popular feature of the statue.']
+...
+```
+
+5. (Optional) One can also use VILA with other quantization options, like SmoothQuant and INT4 AWQ, that are supported by LLaMA. Instructions in LLaMA README to enable SmoothQuant and INT4 AWQ can be re-used to generate quantized TRT engines for LLM component of VILA.
+```bash
+python quantization/quantize.py \
+     --model_dir tmp/hf_models/${MODEL_NAME} \
+     --output_dir tmp/trt_models/${MODEL_NAME}/int4_awq/1-gpu \
+     --dtype float16 \
+     --qformat int4_awq \
+     --calib_size 32
+
+ trtllm-build \
+     --checkpoint_dir tmp/trt_models/${MODEL_NAME}/int4_awq/1-gpu \
+     --output_dir trt_engines/${MODEL_NAME}/int4_awq/1-gpu \
+     --gemm_plugin float16 \
+     --max_batch_size 1 \
+     --max_input_len 2048 \
+     --max_output_len 512 \
+     --max_multimodal_len 4096
+
+python run.py  \
+    --max_new_tokens 100 \
+    --hf_model_dir tmp/hf_models/${MODEL_NAME} \
+    --visual_engine_dir visual_engines/${MODEL_NAME} \
+    --llm_engine_dir trt_engines/${MODEL_NAME}/int4_awq/1-gpu \
+    --image_file=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \
+    --input_text="<image>\n<image>\n Please elaborate what you see in the images?" \
+    --run_profiling
+```
\ No newline at end of file
diff --git a/demo_trt_llm/apply_patch.sh b/demo_trt_llm/apply_patch.sh
new file mode 100644
index 00000000..63ed9220
--- /dev/null
+++ b/demo_trt_llm/apply_patch.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+# Define the file to be modified
+FILE_PATH="/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py"
+
+# Backup the original file before modification
+cp $FILE_PATH "${FILE_PATH}.bak"
+
+# Replace the strings
+# sed -i ':a;N;$!ba;s|hf_config = LlavaConfig.from_pretrained(hf_model).text_config|hf_config = LlavaConfig.from_pretrained(hf_model).text_config\n    if hf_config.model_type == "llava_llama":\n        hf_config.llm_cfg["architecture"] = hf_config.llm_cfg["architectures"]\n        hf_config.llm_cfg["dtype"] = hf_config.llm_cfg["torch_dtype"]\n        hf_config = PretrainedConfig.from_dict(hf_config.llm_cfg)|g' $FILE_PATH
+sed -i ':a;N;$!ba;s|if "vila" in model_dir:\n        sys.path.append(model_dir + "/../VILA")\n        from llava.model import LlavaConfig, LlavaLlamaForCausalLM\n        AutoConfig.register("llava_llama", LlavaConfig)\n        AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)|# if "vila" in model_dir:\n#     sys.path.append(model_dir + "/../VILA")\n#     from llava.model import LlavaConfig, LlavaLlamaForCausalLM\n#     AutoConfig.register("llava_llama", LlavaConfig)\n#     AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)|g' $FILE_PATH
+
+# Inform the user
+echo "Replacement done. Original file backed up as ${FILE_PATH}.bak"
diff --git a/demo_trt_llm/av.png b/demo_trt_llm/av.png
new file mode 100755
index 0000000000000000000000000000000000000000..354c46768a880d2e1b226660a4441c21191b518e
GIT binary patch
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diff --git a/demo_trt_llm/build_visual_engine.py b/demo_trt_llm/build_visual_engine.py
new file mode 100644
index 00000000..a6f25437
--- /dev/null
+++ b/demo_trt_llm/build_visual_engine.py
@@ -0,0 +1,300 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import os
+import shutil
+import sys
+from time import time
+
+# isort: off
+import torch
+import tensorrt as trt
+from tensorrt_llm.builder import Builder
+# isort: on
+
+from PIL import Image
+from transformers import (AutoProcessor, Blip2ForConditionalGeneration,
+                          Blip2Processor, LlavaForConditionalGeneration,
+                          NougatProcessor, VisionEncoderDecoderModel)
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_type',
+                        type=str,
+                        default=None,
+                        choices=[
+                            'opt-2.7b', 'opt-6.7b', 'flan-t5-xl', 'flan-t5-xxl',
+                            'llava', 'vila', 'nougat'
+                        ],
+                        help="Model type")
+    parser.add_argument('--model_path',
+                        type=str,
+                        default=None,
+                        help="Huggingface repo or local directory with weights")
+    parser.add_argument('--vila_path',
+                        type=str,
+                        default=None,
+                        help="Path to VILA source code directory")
+    parser.add_argument('--output_dir',
+                        type=str,
+                        default=None,
+                        help="Directory where visual TRT engines are saved")
+    parser.add_argument('--max_batch_size',
+                        type=int,
+                        default=4,
+                        help="Maximum batch size for input images")
+    return parser.parse_args()
+
+
+class VisionEngineBuilder:
+
+    def __init__(self, args):
+        args.device = torch.device(
+            "cuda") if torch.cuda.is_available() else "cpu"
+        if args.output_dir is None:
+            args.output_dir = 'visual_engines/%s' % (
+                args.model_path.split('/')[-1])
+        if not os.path.exists(args.output_dir):
+            os.makedirs(args.output_dir)
+
+        self.args = args
+
+    def build(self):
+        args = self.args
+        if 'opt' in args.model_type or 't5' in args.model_type:
+            build_blip2_engine(args)
+        elif args.model_type == 'llava':
+            build_llava_engine(args)
+        elif args.model_type == 'vila':
+            assert args.vila_path is not None, "Please clone and provide VILA source code path"
+            build_vila_engine(args)
+        elif args.model_type == 'nougat':
+            build_nougat_engine(args)
+        else:
+            raise RuntimeError(f"Invalid model type {args.model_type}")
+
+
+def export_visual_wrapper_onnx(visual_wrapper, image, output_dir):
+    logger.log(trt.Logger.INFO, "Exporting onnx")
+    os.makedirs(f'{output_dir}/onnx', exist_ok=True)
+    torch.onnx.export(visual_wrapper,
+                      image,
+                      f'{output_dir}/onnx/visual_encoder.onnx',
+                      opset_version=17,
+                      input_names=['input'],
+                      output_names=['output'],
+                      dynamic_axes={'input': {
+                          0: 'batch'
+                      }})
+
+
+def build_trt_engine(model_type, img_height, img_width, output_dir,
+                     max_batch_size):
+    part_name = 'visual_encoder'
+    onnx_file = '%s/onnx/%s.onnx' % (output_dir, part_name)
+    engine_file = '%s/%s.engine' % (output_dir, part_name)
+    config_file = '%s/%s' % (output_dir, "config.json")
+    logger.log(trt.Logger.INFO, "Building TRT engine for %s" % part_name)
+
+    builder = trt.Builder(logger)
+    network = builder.create_network(
+        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
+    profile = builder.create_optimization_profile()
+    config_wrapper = Builder().create_builder_config(precision="float16",
+                                                     model_type=model_type)
+    config = config_wrapper.trt_builder_config
+
+    parser = trt.OnnxParser(network, logger)
+
+    with open(onnx_file, 'rb') as model:
+        if not parser.parse(model.read(), os.path.abspath(onnx_file)):
+            logger.log(trt.Logger.ERROR, "Failed parsing %s" % onnx_file)
+            for error in range(parser.num_errors):
+                logger.log(trt.Logger.ERROR, parser.get_error(error))
+        logger.log(trt.Logger.INFO, "Succeeded parsing %s" % onnx_file)
+
+    # Delete onnx files since we don't need them now
+    shutil.rmtree(f'{output_dir}/onnx')
+
+    nBS = -1
+    nMinBS = 1
+    nOptBS = max(nMinBS, int(max_batch_size / 2))
+    nMaxBS = max_batch_size
+
+    logger.log(trt.Logger.INFO,
+               f"Processed image dims {img_height}x{img_width}")
+    H, W = img_height, img_width
+    inputT = network.get_input(0)
+    inputT.shape = [nBS, 3, H, W]
+    profile.set_shape(inputT.name, [nMinBS, 3, H, W], [nOptBS, 3, H, W],
+                      [nMaxBS, 3, H, W])
+    config.add_optimization_profile(profile)
+
+    t0 = time()
+    engine_string = builder.build_serialized_network(network, config)
+    t1 = time()
+    if engine_string is None:
+        raise RuntimeError("Failed building %s" % (engine_file))
+    else:
+        logger.log(trt.Logger.INFO,
+                   "Succeeded building %s in %d s" % (engine_file, t1 - t0))
+        with open(engine_file, 'wb') as f:
+            f.write(engine_string)
+
+    Builder.save_config(config_wrapper, config_file)
+
+
+def build_blip2_engine(args):
+    model_type = 'Salesforce/blip2-' + args.model_type
+    processor = Blip2Processor.from_pretrained(model_type)
+
+    raw_image = Image.new('RGB', [10, 10])  # dummy image
+    prompt = "Question: what is this? Answer:"
+    inputs = processor(raw_image, prompt,
+                       return_tensors="pt").to(args.device, torch.float16)
+    image = inputs['pixel_values']
+
+    class Blip2VisionWrapper(torch.nn.Module):
+
+        def __init__(self, vision_model, qformer, projector, query_tokens):
+            super().__init__()
+            self.vision_model = vision_model
+            self.qformer = qformer
+            self.projector = projector
+            self.query_tokens = query_tokens
+
+        def forward(self, image):
+            features = self.vision_model(image)[0]
+            qformer_output = self.qformer(query_embeds=self.query_tokens,
+                                          encoder_hidden_states=features,
+                                          return_dict=True)
+            return self.projector(qformer_output.last_hidden_state)
+
+    model = Blip2ForConditionalGeneration.from_pretrained(
+        model_type, torch_dtype=torch.float16)
+    wrapper = Blip2VisionWrapper(model.vision_model, model.qformer,
+                                 model.language_projection, model.query_tokens)
+    wrapper.to(args.device)
+
+    export_visual_wrapper_onnx(wrapper, image, args.output_dir)
+    build_trt_engine(model_type, image.shape[2], image.shape[3],
+                     args.output_dir, args.max_batch_size)
+
+
+def build_llava_engine(args):
+    processor = AutoProcessor.from_pretrained(args.model_path)
+    raw_image = Image.new('RGB', [10, 10])  # dummy image
+    image = processor(text="dummy", images=raw_image,
+                      return_tensors="pt")['pixel_values'].to(
+                          args.device, torch.float16)
+
+    class LlavaVisionWrapper(torch.nn.Module):
+
+        def __init__(self, tower, projector, feature_layer):
+            super().__init__()
+            self.tower = tower
+            self.projector = projector
+            self.feature_layer = feature_layer
+
+        def forward(self, image):
+            all_hidden_states = self.tower(
+                image, output_hidden_states=True).hidden_states
+            features = all_hidden_states[self.feature_layer][:, 1:]
+            return self.projector(features)
+
+    model = LlavaForConditionalGeneration.from_pretrained(
+        args.model_path, torch_dtype=torch.float16)
+    wrapper = LlavaVisionWrapper(model.vision_tower.to(args.device),
+                                 model.multi_modal_projector.to(args.device),
+                                 model.config.vision_feature_layer)
+
+    export_visual_wrapper_onnx(wrapper, image, args.output_dir)
+    build_trt_engine(args.model_type, image.shape[2], image.shape[3],
+                     args.output_dir, args.max_batch_size)
+
+
+sys.path.append("../")
+from llava.model import *
+def build_vila_engine(args):
+    # Note: VILA model is not in public HF model zoo yet. We need to explicitly import from the git repo
+    from transformers import AutoModel
+    model = AutoModel.from_pretrained(args.model_path,
+        device_map='auto',
+    )
+
+    vision_tower = model.get_vision_tower()
+    image_processor = vision_tower.image_processor
+    raw_image = Image.new('RGB', [10, 10])  # dummy image
+    image = image_processor(images=raw_image,
+                            return_tensors="pt")['pixel_values'].to(
+                                args.device, torch.float16)
+
+    class VilaVisionWrapper(torch.nn.Module):
+
+        def __init__(self, tower, projector):
+            super().__init__()
+            self.tower = tower
+            self.projector = projector
+
+        def forward(self, image):
+            features = self.tower(image)
+            return self.projector(features)
+
+    model = AutoModel.from_pretrained(args.model_path,
+        device_map='auto',
+    )
+
+    wrapper = VilaVisionWrapper(
+        model.get_vision_tower().to(args.device),
+        model.mm_projector.to(args.device))
+
+    export_visual_wrapper_onnx(wrapper, image, args.output_dir)
+    build_trt_engine(args.model_type, image.shape[2], image.shape[3],
+                     args.output_dir, args.max_batch_size)
+
+
+def build_nougat_engine(args):
+    processor = NougatProcessor.from_pretrained(args.model_path)
+    raw_image = Image.new('RGB', [10, 10])  # dummy image
+    image = processor(raw_image, return_tensors="pt")['pixel_values'].to(
+        args.device, torch.float16)
+
+    class SwinEncoderWrapper(torch.nn.Module):
+
+        def __init__(self, encoder):
+            super().__init__()
+            self.encoder = encoder
+
+        def forward(self, image):
+            return self.encoder(image).last_hidden_state
+
+    model = VisionEncoderDecoderModel.from_pretrained(args.model_path,
+                                                      torch_dtype=torch.float16)
+    swin_encoder = model.get_encoder().to(args.device)
+    wrapper = SwinEncoderWrapper(swin_encoder)
+
+    export_visual_wrapper_onnx(wrapper, image, args.output_dir)
+    build_trt_engine(args.model_type, image.shape[2], image.shape[3],
+                     args.output_dir, args.max_batch_size)
+
+
+if __name__ == '__main__':
+    logger = trt.Logger(trt.Logger.INFO)
+    args = parse_arguments()
+    builder = VisionEngineBuilder(args)
+    builder.build()
diff --git a/demo_trt_llm/convert_checkpoint.py b/demo_trt_llm/convert_checkpoint.py
new file mode 100644
index 00000000..125901f6
--- /dev/null
+++ b/demo_trt_llm/convert_checkpoint.py
@@ -0,0 +1,468 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import json
+import os
+import sys
+import time
+import traceback
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import tensorrt_llm
+from tensorrt_llm._utils import release_gc
+from tensorrt_llm.layers import MoeConfig
+from tensorrt_llm.mapping import Mapping
+from tensorrt_llm.models import LLaMAForCausalLM
+from tensorrt_llm.models.llama.weight import load_from_gptq_llama
+from tensorrt_llm.models.modeling_utils import QuantConfig
+from tensorrt_llm.quantization import QuantAlgo
+
+# Path to vila lib
+VILA_PATH = "../"
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_dir', type=str, default=None)
+    parser.add_argument('--meta_ckpt_dir', type=str, default=None)
+
+    parser.add_argument('--tp_size',
+                        type=int,
+                        default=1,
+                        help='N-way tensor parallelism size')
+    parser.add_argument('--pp_size',
+                        type=int,
+                        default=1,
+                        help='N-way pipeline parallelism size')
+    parser.add_argument('--dtype',
+                        type=str,
+                        default='float16',
+                        choices=['float32', 'bfloat16', 'float16'])
+    parser.add_argument('--vocab_size', type=int, default=32000)
+    parser.add_argument('--n_positions', type=int, default=2048)
+    parser.add_argument('--n_layer', type=int, default=32)
+    parser.add_argument('--n_head', type=int, default=32)
+    parser.add_argument('--n_kv_head', type=int, default=None)
+    parser.add_argument('--n_embd', type=int, default=4096)
+    parser.add_argument('--inter_size', type=int, default=11008)
+    parser.add_argument('--rms_norm_eps', type=float, default=1e-06)
+
+    parser.add_argument(
+        '--use_weight_only',
+        default=False,
+        action="store_true",
+        help='Quantize weights for the various GEMMs to INT4/INT8.'
+        'See --weight_only_precision to set the precision')
+    parser.add_argument(
+        '--disable_weight_only_quant_plugin',
+        default=False,
+        action="store_true",
+        help=
+        'By default, using plugin implementation for weight quantization. Enabling disable_weight_only_quant_plugin flag will use ootb implementation instead of plugin.'
+        'You must also use --use_weight_only for that argument to have an impact.'
+    )
+    parser.add_argument(
+        '--weight_only_precision',
+        const='int8',
+        type=str,
+        nargs='?',
+        default='int8',
+        choices=['int8', 'int4', 'int4_gptq'],
+        help=
+        'Define the precision for the weights when using weight-only quantization.'
+        'You must also use --use_weight_only for that argument to have an impact.'
+    )
+    parser.add_argument(
+        "--smoothquant",
+        "-sq",
+        type=float,
+        default=None,
+        help="Set the α parameter (see https://arxiv.org/pdf/2211.10438.pdf)"
+        " to Smoothquant the model, and output int8 weights."
+        " A good first try is 0.5. Must be in [0, 1]")
+    parser.add_argument(
+        '--per_channel',
+        action="store_true",
+        default=False,
+        help=
+        'By default, we use a single static scaling factor for the GEMM\'s result. '
+        'per_channel instead uses a different static scaling factor for each channel. '
+        'The latter is usually more accurate, but a little slower.')
+    parser.add_argument(
+        '--per_token',
+        action="store_true",
+        default=False,
+        help=
+        'By default, we use a single static scaling factor to scale activations in the int8 range. '
+        'per_token chooses at run time, and for each token, a custom scaling factor. '
+        'The latter is usually more accurate, but a little slower.')
+    parser.add_argument(
+        '--int8_kv_cache',
+        default=False,
+        action="store_true",
+        help=
+        'By default, we use dtype for KV cache. int8_kv_cache chooses int8 quantization for KV'
+    )
+    parser.add_argument(
+        '--ammo_quant_ckpt_path',
+        type=str,
+        default=None,
+        help='Path of a quantized model checkpoint in .npz format')
+
+    parser.add_argument(
+        '--per_group',
+        default=False,
+        action="store_true",
+        help=
+        'By default, we use a single static scaling factor to scale weights in the int4 range. '
+        'per_group chooses at run time, and for each group, a custom scaling factor. '
+        'The flag is built for GPTQ/AWQ quantization.')
+
+    parser.add_argument('--load_by_shard',
+                        action='store_true',
+                        help='Load a pretrained model shard-by-shard.')
+    parser.add_argument('--hidden_act', type=str, default='silu')
+
+    parser.add_argument('--rotary_base', type=float, default=10000.0)
+
+    parser.add_argument('--group_size',
+                        type=int,
+                        default=128,
+                        help='Group size used in GPTQ quantization.'
+                        )  # AWQ is only supported by quantize.py script
+
+    parser.add_argument("--dataset-cache-dir",
+                        type=str,
+                        default=None,
+                        help="cache dir to load the hugging face dataset")
+    parser.add_argument("--load_model_on_cpu", action="store_true")
+    parser.add_argument(
+        '--use_parallel_embedding',
+        action="store_true",
+        default=False,
+        help=
+        'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled'
+    )
+    parser.add_argument(
+        '--embedding_sharding_dim',
+        type=int,
+        default=0,
+        choices=[0, 1],
+        help=
+        'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). '
+        'To shard it along hidden dimension, set embedding_sharding_dim=1'
+        'Note: embedding sharing is only enabled when embedding_sharding_dim = 0'
+    )
+    parser.add_argument(
+        '--use_embedding_sharing',
+        action="store_true",
+        default=False,
+        help=
+        'Try to reduce the engine size by sharing the embedding lookup table between two layers.'
+        'Note: the flag might not take effect when the criteria are not met.')
+    parser.add_argument('--output_dir',
+                        type=str,
+                        default='tllm_checkpoint',
+                        help='The path to save the TensorRT-LLM checkpoint')
+    parser.add_argument(
+        '--workers',
+        type=int,
+        default=1,
+        help='The number of workers for converting checkpoint in parallel')
+    parser.add_argument(
+        '--moe_num_experts',
+        default=0,
+        type=int,
+        help='Specify the number of experts to use for MOE layers')
+    parser.add_argument(
+        '--moe_top_k',
+        default=0,
+        type=int,
+        help=
+        'Specify the top_k value to use for MOE layers. Default to 1 if --moe_num_experts is set'
+    )
+    parser.add_argument(
+        '--moe_tp_mode',
+        default=MoeConfig.ParallelismMode.TENSOR_PARALLEL,
+        type=int,
+        help=
+        'Controls how to distribute experts in TP. Check layers/moe.py for accepted values',
+    )
+    parser.add_argument(
+        '--moe_renorm_mode',
+        default=MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE,
+        type=int,
+        help=
+        'Controls renormalization after gate logits. Check layers/moe.py for accepted values',
+    )
+    parser.add_argument(
+        '--save_config_only',
+        action="store_true",
+        default=False,
+        help=
+        'Only save the model config w/o read and converting weights, be careful, this is for debug only'
+    )
+
+    args = parser.parse_args()
+    # changing the default to be consistent as the cli help said.
+    if args.moe_num_experts and args.moe_top_k == 0:
+        args.moe_top_k = 1
+    return args
+
+
+def args_to_quantization(args: argparse.Namespace) -> QuantConfig:
+    '''return config dict with quantization info based on the command line args
+    '''
+    quant_config = QuantConfig()
+    quant_config.exclude_modules = ['lm_head']
+    if args.use_weight_only:
+        if args.weight_only_precision == 'int8':
+            quant_config.quant_algo = QuantAlgo.W8A16
+        elif args.weight_only_precision == 'int4':
+            quant_config.quant_algo = QuantAlgo.W4A16
+    elif args.smoothquant:
+        quant_config.smoothquant_val = args.smoothquant
+        if args.per_channel:
+            if args.per_token:
+                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN
+            else:
+                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN
+        else:
+            if args.per_token:
+                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN
+            else:
+                quant_config.quant_algo = QuantAlgo.W8A8_SQ_PER_TENSOR_PLUGIN
+
+    if args.int8_kv_cache:
+        quant_config.kv_cache_quant_algo = QuantAlgo.INT8
+
+    if args.weight_only_precision == 'int4_gptq':
+        quant_config.group_size = args.group_size
+        quant_config.has_zero_point = True
+        quant_config.pre_quant_scale = False
+        quant_config.quant_algo = QuantAlgo.W4A16_GPTQ
+
+    return quant_config
+
+
+def convert_and_save_meta(args, rank):
+    mapping = Mapping(world_size=args.tp_size * args.pp_size,
+                      tp_size=args.tp_size,
+                      pp_size=args.pp_size,
+                      rank=rank)
+    assert not args_to_quantization(args).quant_mode.has_any_quant(), \
+        "quantization from meta checkpoint or empty model were never supported"
+    llama = LLaMAForCausalLM.from_meta_ckpt(
+        args.meta_ckpt_dir,
+        args.dtype,
+        mapping,
+        use_parallel_embedding=args.use_parallel_embedding,
+        embedding_sharding_dim=args.embedding_sharding_dim)
+    llama.save_checkpoint(args.output_dir, save_config=(rank == 0))
+
+
+def args_to_build_options(args):
+    return {
+        'use_parallel_embedding': args.use_parallel_embedding,
+        'embedding_sharding_dim': args.embedding_sharding_dim,
+        'share_embedding_table': args.use_embedding_sharing,
+        'disable_weight_only_quant_plugin':
+        args.disable_weight_only_quant_plugin
+    }
+
+
+def from_cli_args(args):
+    n_kv_head = args.n_kv_head if args.n_kv_head is not None else args.n_head
+    config = {
+        'architecture': "LlamaForCausalLM",
+        'dtype': args.dtype,
+        'logits_dtype': 'float32',
+        'num_hidden_layers': args.n_layer,
+        'num_attention_heads': args.n_head,
+        'hidden_size': args.n_embd,
+        'intermediate_size': args.inter_size,
+        'num_key_value_heads': n_kv_head,
+        'vocab_size': args.vocab_size,
+        'position_embedding_type': 'rope_gpt_neox',
+        'max_position_embeddings': args.n_positions,
+        'hidden_act': args.hidden_act,
+        'rotary_base': args.rotary_base,
+        'norm_epsilon': args.rms_norm_eps,
+        'moe_num_experts': args.moe_num_experts,
+        'moe_top_k': args.moe_top_k,
+        'moe_tp_mode': args.moe_tp_mode,
+        'moe_normalization_mode': args.moe_renorm_mode,
+        'mapping': {
+            'world_size': args.tp_size * args.pp_size,
+            'tp_size': args.tp_size,
+            'pp_size': args.pp_size
+        },
+        'quantization': args_to_quantization(args).asdict()
+    }
+    config.update(args_to_build_options(args))
+    return config
+
+
+def preload_model(model_dir):
+    sys.path.append(f"{VILA_PATH}")
+    from llava.model import LlavaLlamaModel, LlavaLlamaConfig
+    from transformers import AutoConfig, AutoModelForCausalLM
+    if "vila" in model_dir:
+        from transformers import AutoModel
+        model = AutoModel.from_pretrained(model_dir,
+            device_map='auto',
+            trust_remote_code=True,
+        )
+        return model.llm
+
+    hf_config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
+    if hf_config.model_type == "llava":
+        from transformers import LlavaForConditionalGeneration
+        hf_llava = LlavaForConditionalGeneration.from_pretrained(
+            model_dir, torch_dtype="auto")
+        model = hf_llava.language_model
+    else:
+        model = AutoModelForCausalLM.from_pretrained(
+            model_dir,
+            device_map='auto',
+            torch_dtype='auto',
+            trust_remote_code=True,
+        )
+    return model
+
+
+def convert_and_save_hf(args):
+    model_dir = args.model_dir
+    load_model_on_cpu = args.load_model_on_cpu
+    load_by_shard = args.load_by_shard
+    world_size = args.tp_size * args.pp_size
+    # Need to convert the cli args to the kay-value pairs and override them in the generate config dict.
+    # Ideally these fields will be moved out of the config and pass them into build API, keep them here for compatibility purpose for now,
+    # before the refactor is done.
+    override_fields = {'moe_tp_mode': args.moe_tp_mode}
+    quantization = args_to_quantization(args)
+    override_fields.update(args_to_build_options(args))
+
+    if args.smoothquant is not None or args.int8_kv_cache:
+        assert not args.load_by_shard, "When using quantization, TRT-LLM needs to load the whole HF model, thus load by shard not supported"
+        assert not args.load_model_on_cpu, "When using quantization, TRT-LLM needs to load the model to GPU"
+        mapping = Mapping(
+            world_size=world_size,
+            rank=-1,  #intentinoally make -1 to avoid mistake
+            tp_size=args.tp_size,
+            pp_size=args.pp_size)
+        LLaMAForCausalLM.quantize(args.model_dir,
+                                  args.output_dir,
+                                  quantization,
+                                  dtype=args.dtype,
+                                  mapping=mapping,
+                                  override_fields=override_fields,
+                                  dataset_cache_dir=args.dataset_cache_dir)
+    else:
+        # When not loading by shard, preload one complete model and then slice per rank weights from this
+        # this saves the disk reloading time
+        hf_model = preload_model(model_dir) if not args.load_by_shard else None
+
+        def convert_and_save_rank(args, rank):
+            mapping = Mapping(world_size=world_size,
+                              rank=rank,
+                              tp_size=args.tp_size,
+                              pp_size=args.pp_size)
+            llama = LLaMAForCausalLM.from_hugging_face(
+                model_dir,
+                args.dtype,
+                mapping=mapping,
+                quantization=quantization,
+                load_by_shard=load_by_shard,
+                load_model_on_cpu=load_model_on_cpu,
+                override_fields=override_fields,
+                preloaded_model=hf_model)
+            llama.save_checkpoint(args.output_dir, save_config=(rank == 0))
+            del llama
+            release_gc()
+
+        execute(args.workers, [convert_and_save_rank] * world_size, args)
+
+
+def convert_and_save_gptq(args, rank):
+    mapping = Mapping(world_size=args.tp_size * args.pp_size,
+                      tp_size=args.tp_size,
+                      rank=rank,
+                      pp_size=args.pp_size)
+    llama = LLaMAForCausalLM.from_hugging_face(
+        args.model_dir,
+        args.dtype,
+        mapping=mapping,
+        quantization=args_to_quantization(args),
+        skip_loading_weights=True)
+    weights = load_from_gptq_llama(llama.config, args.ammo_quant_ckpt_path)
+    llama.load(weights)
+    llama.save_checkpoint(args.output_dir, rank == 0)
+
+
+def execute(workers, func, args):
+    if workers == 1:
+        for rank, f in enumerate(func):
+            f(args, rank)
+    else:
+        with ThreadPoolExecutor(max_workers=workers) as p:
+            futures = [p.submit(f, args, rank) for rank, f in enumerate(func)]
+            exceptions = []
+            for future in as_completed(futures):
+                try:
+                    future.result()
+                except Exception as e:
+                    traceback.print_exc()
+                    exceptions.append(e)
+            assert len(
+                exceptions
+            ) == 0, "Checkpoint conversion failed, please check error log."
+
+
+def main():
+    print(tensorrt_llm.__version__)
+    args = parse_arguments()
+
+    world_size = args.tp_size * args.pp_size
+    tik = time.time()
+
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    if (args.model_dir is None
+            and args.meta_ckpt_dir is None):  # generate fake config.json
+        config = from_cli_args(args)
+        with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
+            json.dump(config, f, indent=4)
+    elif args.meta_ckpt_dir is not None:
+        assert args.model_dir is None, "Shall not specify both meta checkpoint dir and hugging face dir"
+        execute(args.workers, [convert_and_save_meta] * world_size, args)
+    elif args.weight_only_precision == 'int4_gptq':
+        assert args.model_dir is not None
+        assert args.ammo_quant_ckpt_path is not None
+        execute(args.workers, [convert_and_save_gptq] * world_size, args)
+    else:  # all other non-gptq paths from hf model
+        assert args.model_dir is not None
+        assert args.ammo_quant_ckpt_path is None, "only gptq weights only needs this option"
+        convert_and_save_hf(args)
+
+    tok = time.time()
+    t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
+    print(f'Total time of converting checkpoints: {t}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo_trt_llm/quantization/quantize.py b/demo_trt_llm/quantization/quantize.py
new file mode 100644
index 00000000..c9f84472
--- /dev/null
+++ b/demo_trt_llm/quantization/quantize.py
@@ -0,0 +1,64 @@
+import argparse
+
+from quantize_by_ammo import quantize_and_export
+
+if __name__ == "__main__":
+    DEFAULT_RAND_SEED = 1234
+    DEFAULT_MAX_SEQ_LEN = 2048
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--model_dir",
+                        help="Specify where the HuggingFace model is",
+                        required=True)
+    parser.add_argument("--device", default="cuda")
+    parser.add_argument("--dtype", help="Model data type.", default="float16")
+    parser.add_argument(
+        "--qformat",
+        help="Quantization format.",
+        default="full_prec",
+        choices=[
+            "fp8", "int8_sq", "int4_awq", "w4a8_awq", "int8_wo", "int4_wo",
+            "full_prec"
+        ],
+    )
+    parser.add_argument(
+        "--seed",
+        help="Seed the generate random numbers, the value will be used to call"
+        "random.seed(value) and numpy.random.seed(value)",
+        type=int,
+        default=DEFAULT_RAND_SEED)
+    parser.add_argument("--max_seq_length",
+                        help="Max sequence length to init the tokenizers",
+                        type=int,
+                        default=DEFAULT_MAX_SEQ_LEN)
+
+    parser.add_argument("--batch_size",
+                        help="Batch size for calibration.",
+                        type=int,
+                        default=1)
+    parser.add_argument("--calib_size",
+                        help="Number of samples for calibration.",
+                        type=int,
+                        default=512)
+    parser.add_argument("--output_dir", default="exported_model")
+    parser.add_argument("--tp_size", type=int, default=1)
+    parser.add_argument("--pp_size", type=int, default=1)
+    parser.add_argument("--awq_block_size", type=int, default=128)
+    parser.add_argument("--kv_cache_dtype",
+                        help="KV Cache dtype.",
+                        default=None,
+                        choices=["int8", "fp8", None])
+    args = parser.parse_args()
+
+    quantize_and_export(model_dir=args.model_dir,
+                        dtype=args.dtype,
+                        output_dir=args.output_dir,
+                        device=args.device,
+                        tp_size=args.tp_size,
+                        pp_size=args.pp_size,
+                        qformat=args.qformat,
+                        kv_cache_dtype=args.kv_cache_dtype,
+                        calib_size=args.calib_size,
+                        batch_size=args.batch_size,
+                        awq_block_size=args.awq_block_size,
+                        seed=args.seed,
+                        max_seq_length=args.max_seq_length)
diff --git a/demo_trt_llm/quantization/quantize_by_ammo.py b/demo_trt_llm/quantization/quantize_by_ammo.py
new file mode 100644
index 00000000..b23933e5
--- /dev/null
+++ b/demo_trt_llm/quantization/quantize_by_ammo.py
@@ -0,0 +1,433 @@
+# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Adapted from examples/quantization/hf_ptq.py
+"""
+
+import copy
+import json
+import random
+import sys
+import time
+
+import numpy as np
+import safetensors
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+from transformers import AutoConfig, AutoTokenizer
+from enum import auto
+from strenum import StrEnum
+
+from tensorrt_llm._utils import BaseEnumMeta
+class QuantAlgo(StrEnum, metaclass=BaseEnumMeta):
+    W8A16 = auto()
+    W4A16 = auto()
+    W4A16_AWQ = auto()
+    W4A8_AWQ = auto()
+    W4A16_GPTQ = auto()
+    W8A8_SQ_PER_CHANNEL = auto()
+    W8A8_SQ_PER_TENSOR_PLUGIN = auto()
+    W8A8_SQ_PER_CHANNEL_PER_TOKEN_PLUGIN = auto()
+    W8A8_SQ_PER_CHANNEL_PER_TENSOR_PLUGIN = auto()
+    W8A8_SQ_PER_TENSOR_PER_TOKEN_PLUGIN = auto()
+    FP8 = auto()
+    INT8 = auto()
+
+EMPTY_CFG = {
+    "quant_cfg": {
+        "*weight_quantizer": {
+            "enable": False,
+        },
+        "*input_quantizer": {
+            "enable": False
+        },
+        "*lm_head*": {
+            "enable": False
+        },
+        "*output_layer*": {
+            "enable": False
+        },
+        "default": {
+            "enable": False
+        },
+    },
+    "algorithm": "max",
+}
+
+KV_CACHE_CFG = {
+    "*.query_key_value.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+    "*.Wqkv.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+    "*.W_pack.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+    "*.c_attn.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+    "*.k_proj.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+    "*.v_proj.output_quantizer": {
+        "num_bits": 8,
+        "axis": None,
+        "enable": True
+    },
+}
+
+
+def quant_cfg_choices():
+    import ammo.torch.quantization as atq
+    QUANT_CFG_CHOICES = {
+        "int8_sq": atq.INT8_SMOOTHQUANT_CFG,
+        "fp8": atq.FP8_DEFAULT_CFG,
+        "int4_awq": atq.INT4_AWQ_CFG,
+        "w4a8_awq": atq.W4A8_AWQ_BETA_CFG,
+        "int8_wo": EMPTY_CFG,
+        "int4_wo": EMPTY_CFG,
+        "full_prec": EMPTY_CFG,
+    }
+    return QUANT_CFG_CHOICES
+
+
+MODEL_NAME_PATTERN_MAP = {
+    "GPT2": "gpt2",
+    "Xverse": "llama",
+    "Llama": "llama",
+    "Mistral": "llama",
+    "GPTJ": "gptj",
+    "FalconForCausalLM": "falcon",
+    "RWForCausalLM": "falcon",
+    "baichuan": "baichuan",
+    "MPT": "mpt",
+    "Bloom": "bloom",
+    "ChatGLM": "chatglm",
+    "QWen": "qwen",
+    "Gemma": "gemma",
+    "MixtralForCausalLM": "llama",
+}
+
+
+def get_tokenizer(ckpt_path, max_seq_length, model_type=None):
+    print(f"Initializing tokenizer from {ckpt_path}")
+    tokenizer = AutoTokenizer.from_pretrained(
+        ckpt_path,
+        model_max_length=max_seq_length,
+        padding_side="left",
+        trust_remote_code=True,
+    )
+    if model_type and model_type == "qwen":
+        # qwen use token id 151643 as pad and eos tokens
+        tokenizer.pad_token = tokenizer.convert_ids_to_tokens(151643)
+        tokenizer.eos_token = tokenizer.convert_ids_to_tokens(151643)
+
+    # can't set attribute 'pad_token' for "<unk>"
+    if tokenizer.pad_token != "<unk>":  # nosec B105
+        tokenizer.pad_token = tokenizer.eos_token
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    assert tokenizer.pad_token is not None, f"Pad token for {model_type} cannot be set!"
+
+    return tokenizer
+
+def _get_vila_model(model_dir):
+    VILA_PATH = "../"
+    sys.path.append(f"{VILA_PATH}")
+    from llava.model import LlavaLlamaModel, LlavaLlamaConfig
+    from transformers import AutoConfig, AutoModelForCausalLM
+    if "vila" in model_dir:
+        from transformers import AutoModel
+        model = AutoModel.from_pretrained(model_dir,
+            device_map='auto',
+            trust_remote_code=True,
+        )
+        return model.llm
+
+def get_model(ckpt_path, dtype="fp16", device="cuda"):
+    print(f"Initializing model from {ckpt_path}")
+    if dtype == "bf16" or dtype == "bfloat16":
+        dtype = torch.bfloat16
+    elif dtype == "fp16" or dtype == "float16":
+        dtype = torch.float16
+    elif dtype == "fp32" or dtype == "float32":
+        dtype = torch.float32
+    else:
+        raise NotImplementedError(f"Unknown dtype {dtype}")
+
+    model = _get_vila_model(ckpt_path)
+    model.eval()
+
+    model_dtype = next(model.parameters()).dtype
+    if dtype != model_dtype:
+        print(
+            f"[TensorRT-LLM][WARNING] The manually set model data type is {dtype}, "
+            f"but the data type of the HuggingFace model is {model_dtype}.")
+
+    return model
+
+
+def get_model_type(model):
+    for k, v in MODEL_NAME_PATTERN_MAP.items():
+        if k.lower() in type(model).__name__.lower():
+            return v
+    return None
+
+
+def get_calib_dataloader(data="cnn_dailymail",
+                         tokenizer=None,
+                         batch_size=1,
+                         calib_size=512,
+                         block_size=512,
+                         device=None):
+    print("Loading calibration dataset")
+    if data == "pileval":
+        dataset = load_dataset(
+            "json",
+            data_files="https://the-eye.eu/public/AI/pile/val.jsonl.zst",
+            split="train")
+        dataset = dataset["text"][:calib_size]
+    elif data == "cnn_dailymail":
+        dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train")
+        dataset = dataset["article"][:calib_size]
+    else:
+        raise NotImplementedError
+
+    batch_encoded = tokenizer.batch_encode_plus(dataset,
+                                                return_tensors="pt",
+                                                padding=True,
+                                                truncation=True,
+                                                max_length=block_size)
+    if device:
+        batch_encoded = batch_encoded.to(device)
+    batch_encoded = batch_encoded["input_ids"]
+
+    calib_dataloader = DataLoader(batch_encoded,
+                                  batch_size=batch_size,
+                                  shuffle=False)
+
+    return calib_dataloader
+
+
+def quantize_model(model, quant_cfg, calib_dataloader=None):
+    import ammo.torch.quantization as atq
+
+    def calibrate_loop():
+        if calib_dataloader is None:
+            return
+        """Adjusts weights and scaling factors based on selected algorithms."""
+        for idx, data in enumerate(calib_dataloader):
+            print(f"Calibrating batch {idx}")
+            # model might be mapped to different device because the device_map is auto
+            data = data.to(model.device)
+            model(data)
+
+    print("Starting quantization...")
+    start_time = time.time()
+    atq.quantize(model, quant_cfg, forward_loop=calibrate_loop)
+    end_time = time.time()
+    print("Quantization done. Total time used: {:.2f} s.".format(end_time -
+                                                                 start_time))
+
+    return model
+
+
+def quantize_and_export(*, model_dir, dtype, device, qformat, kv_cache_dtype,
+                        calib_size, batch_size, awq_block_size, output_dir,
+                        tp_size, pp_size, seed, max_seq_length):
+    '''
+        Load model from the model_dir, call AMMO to quantize the model, and then export
+        the quantized model as TRT-LLM checkpoint
+    '''
+    try:
+        import ammo  # noqa
+    except ImportError as e:
+        print(
+            "Failed to import ammo, pls check the AMMO installation. Currently it is known to be unsupported on Windows OS"
+        )
+        raise e
+    from ammo.torch.export import export_tensorrt_llm_checkpoint
+    from ammo.torch.export.tensorrt_llm_utils import MODEL_NAME_TO_HF_ARCH_MAP
+    MODEL_NAME_TO_HF_ARCH_MAP.update({"gpt2": "GPTForCausalLM"})
+
+    if not torch.cuda.is_available():
+        raise EnvironmentError("GPU is required for inference.")
+
+    random.seed(seed)
+    np.random.seed(seed)
+
+    model = get_model(model_dir, dtype, device)
+    model_type = get_model_type(model)
+    tokenizer = get_tokenizer(model_dir + "/llm",
+                              max_seq_length=max_seq_length,
+                              model_type=model_type)
+
+    if qformat in ["full_prec", "int8_wo", "int4_wo"
+                   ] and kv_cache_dtype is None:
+        print(f"No quantization applied, export {dtype} model")
+    else:
+        if "awq" in qformat:
+            if calib_size > 32:
+                print(
+                    f"AWQ calibration could take longer with calib_size = {calib_size}, Using"
+                    " calib_size=32 instead")
+                calib_size = 32
+            print(
+                "\nAWQ calibration could take longer than other calibration methods. Please"
+                " increase the batch size to speed up the calibration process. Batch size can be"
+                " set by adding the argument --batch_size <batch_size> to the command line.\n"
+            )
+
+        calib_dataloader = get_calib_dataloader(
+            tokenizer=tokenizer,
+            batch_size=batch_size,
+            calib_size=calib_size,
+            device=device,
+        )
+
+        if qformat in quant_cfg_choices():
+            quant_cfg = quant_cfg_choices()[qformat]
+        else:
+            raise ValueError(f"Unsupported quantization format: {qformat}")
+
+        if "awq" in qformat:
+            quant_cfg = copy.deepcopy(quant_cfg_choices()[qformat])
+            weight_quantizer = quant_cfg["quant_cfg"][
+                "*weight_quantizer"]  # type: ignore
+            if isinstance(weight_quantizer, list):
+                weight_quantizer = weight_quantizer[0]
+            weight_quantizer["block_sizes"][-1] = awq_block_size
+
+        if kv_cache_dtype is not None:
+            if kv_cache_dtype == "fp8":
+                for value in KV_CACHE_CFG.values():
+                    value.update({"num_bits": (4, 3)})  # type: ignore
+            quant_cfg["quant_cfg"].update(KV_CACHE_CFG)  # type: ignore
+
+        model = quantize_model(model, quant_cfg, calib_dataloader)
+
+    with torch.inference_mode():
+        if model_type is None:
+            print(
+                f"Unknown model type {type(model).__name__}. Continue exporting..."
+            )
+            model_type = f"unknown:{type(model).__name__}"
+
+        export_path = output_dir
+        start_time = time.time()
+
+        export_tensorrt_llm_checkpoint(model,
+                                       model_type,
+                                       getattr(torch, dtype),
+                                       export_dir=export_path,
+                                       inference_tensor_parallel=tp_size,
+                                       inference_pipeline_parallel=pp_size)
+
+        with open(f"{export_path}/config.json", "r") as f:
+            tensorrt_llm_config = json.load(f)
+
+        # Workaround for wo quantization
+        if qformat in ["int8_wo", "int4_wo", "full_prec"]:
+            if qformat == "int8_wo":
+                tensorrt_llm_config["quantization"][
+                    "quant_algo"] = QuantAlgo.W8A16
+            elif qformat == "int4_wo":
+                tensorrt_llm_config["quantization"][
+                    "quant_algo"] = QuantAlgo.W4A16
+            else:
+                tensorrt_llm_config["quantization"]["quant_algo"] = None
+
+        # Workaround for MOE router quantization
+        if "moe_num_experts" in tensorrt_llm_config and qformat != "full_prec":
+            if "exclude_modules" not in tensorrt_llm_config["quantization"]:
+                # Append router and lm_head because we need both excluded
+                tensorrt_llm_config["quantization"]["exclude_modules"] = [
+                    "router", "lm_head"
+                ]
+            else:
+                tensorrt_llm_config["quantization"]["exclude_modules"].append(
+                    "router")
+
+        with open(f"{export_path}/config.json", "w") as f:
+            json.dump(tensorrt_llm_config, f, indent=4)
+
+        # Workaround for AMMO 0.9.x fp8_kv_cache knob issue
+        if qformat == 'fp8' and kv_cache_dtype is None:
+            with open(f"{export_path}/config.json", "r") as f:
+                tensorrt_llm_config = json.load(f)
+            tensorrt_llm_config["quantization"]["kv_cache_quant_algo"] = None
+            with open(f"{export_path}/config.json", "w") as f:
+                json.dump(tensorrt_llm_config, f, indent=4)
+
+        # Workaround for share_embedding_table
+        if pp_size == 1:
+            with safetensors.safe_open(f"{export_path}/rank0.safetensors",
+                                       framework='pt',
+                                       device='cpu') as f:
+                share_embedding_table = 'lm_head.weight' not in f.keys()
+            if share_embedding_table:
+                with open(f"{export_path}/config.json", "r") as f:
+                    tensorrt_llm_config = json.load(f)
+                tensorrt_llm_config["share_embedding_table"] = True
+                with open(f"{export_path}/config.json", "w") as f:
+                    json.dump(tensorrt_llm_config, f, indent=4)
+
+        # Workaround for gpt2 position embedding
+        if model_type == 'gpt2':
+            for rank in range(tp_size):
+                weights = {}
+                with safetensors.safe_open(
+                        f"{export_path}/rank{rank}.safetensors",
+                        framework='pt',
+                        device='cpu') as f:
+                    for key in f.keys():
+                        weights[key] = f.get_tensor(key)
+                if 'transformer.positional_embedding.weight' in weights:
+                    weights[
+                        'transformer.position_embedding.weight'] = weights.pop(
+                            'transformer.positional_embedding.weight')
+                safetensors.torch.save_file(
+                    weights, f"{export_path}/rank{rank}.safetensors")
+
+        # Workaround for qwen version
+        if model_type == 'qwen':
+            with open(f"{export_path}/config.json", "r") as f:
+                tensorrt_llm_config = json.load(f)
+            qwen_config = AutoConfig.from_pretrained(model_dir,
+                                                     trust_remote_code=True)
+            tensorrt_llm_config["qwen_type"] = qwen_config.model_type
+            tensorrt_llm_config[
+                "intermediate_size"] = qwen_config.intermediate_size
+            with open(f"{export_path}/config.json", "w") as f:
+                json.dump(tensorrt_llm_config, f, indent=4)
+
+        torch.cuda.empty_cache(
+        )  # otherwise torch is keeping using GPU, other routine like build engine has less free GPU to use
+        end_time = time.time()
+        print(
+            "Quantized model exported to {} \nTotal time used {:.2f} s.".format(
+                export_path, end_time - start_time))
diff --git a/demo_trt_llm/run.py b/demo_trt_llm/run.py
new file mode 100644
index 00000000..b330a2f6
--- /dev/null
+++ b/demo_trt_llm/run.py
@@ -0,0 +1,513 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import json
+import os
+import sys
+from pathlib import Path
+from io import BytesIO
+
+import requests
+
+# isort: off
+import torch
+import tensorrt as trt
+# isort: on
+
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from transformers import (AutoConfig, AutoProcessor, AutoTokenizer,
+                          Blip2Processor, NougatProcessor, NougatTokenizerFast)
+
+import tensorrt_llm
+import tensorrt_llm.profiler as profiler
+from tensorrt_llm import logger
+from tensorrt_llm.runtime import ModelRunner, Session, TensorInfo
+
+TRT_LLM_EXAMPLE_PATH="/app/tensorrt_llm/examples"
+VILA_PATH = "../"
+
+sys.path.append(TRT_LLM_EXAMPLE_PATH)
+from enc_dec.run import TRTLLMEncDecModel
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--max_new_tokens', type=int, default=30)
+    parser.add_argument('--batch_size', type=int, default=1)
+    parser.add_argument('--log_level', type=str, default='info')
+    parser.add_argument('--visual_engine_dir',
+                        type=str,
+                        default=None,
+                        help='Directory containing visual TRT engines')
+    parser.add_argument('--llm_engine_dir',
+                        type=str,
+                        default=None,
+                        help='Directory containing TRT-LLM engines')
+    parser.add_argument('--hf_model_dir',
+                        type=str,
+                        default=None,
+                        help="Directory containing tokenizer")
+    parser.add_argument('--input_text',
+                        type=str,
+                        default=None,
+                        help='Text prompt to LLM')
+    parser.add_argument('--num_beams',
+                        type=int,
+                        help="Use beam search if num_beams >1",
+                        default=1)
+    parser.add_argument('--top_k', type=int, default=1)
+    parser.add_argument('--run_profiling',
+                        action='store_true',
+                        help='Profile runtime over several iterations')
+    parser.add_argument('--check_accuracy',
+                        action='store_true',
+                        help='Check correctness of text output')
+    parser.add_argument("--image_file", type=str, default=None)
+    parser.add_argument("--sep", type=str, default=",")
+
+    return parser.parse_args()
+
+
+def trt_dtype_to_torch(dtype):
+    if dtype == trt.float16:
+        return torch.float16
+    elif dtype == trt.float32:
+        return torch.float32
+    elif dtype == trt.int32:
+        return torch.int32
+    else:
+        raise TypeError("%s is not supported" % dtype)
+
+
+class MultimodalModelRunner:
+
+    def __init__(self, args):
+        self.args = args
+
+        self.runtime_rank = tensorrt_llm.mpi_rank()
+        device_id = self.runtime_rank % torch.cuda.device_count()
+        torch.cuda.set_device(device_id)
+        self.device = "cuda:%d" % (device_id)
+
+        self.stream = torch.cuda.Stream(torch.cuda.current_device())
+        torch.cuda.set_stream(self.stream)
+
+        # parse model type from visual engine config
+        with open(os.path.join(self.args.visual_engine_dir, "config.json"),
+                  "r") as f:
+            config = json.load(f)
+        self.model_type = config['builder_config']['model_type']
+        self.decoder_llm = not (
+            't5' in self.model_type or 'nougat' in self.model_type
+        )  # BLIP2-T5 and Nougat are using encoder-decoder models as LLMs
+
+        self.profiling_iterations = 20
+
+        self.init_image_encoder()
+        self.init_tokenizer()
+        self.init_llm()
+
+    def init_tokenizer(self):
+        if self.model_type == 'nougat':
+            self.tokenizer = NougatTokenizerFast.from_pretrained(
+                self.args.hf_model_dir)
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                self.args.hf_model_dir + "/llm", use_fast=False, use_legacy=False)
+
+        self.tokenizer.padding_side = "right"
+
+    def init_image_encoder(self):
+        vision_encoder_path = os.path.join(self.args.visual_engine_dir,
+                                           'visual_encoder.engine')
+        logger.info(f'Loading engine from {vision_encoder_path}')
+        with open(vision_encoder_path, 'rb') as f:
+            engine_buffer = f.read()
+        logger.info(f'Creating session from engine {vision_encoder_path}')
+        self.visual_encoder_session = Session.from_serialized_engine(
+            engine_buffer)
+
+    def init_llm(self):
+        if self.decoder_llm:
+            self.model = ModelRunner.from_dir(self.args.llm_engine_dir,
+                                              rank=tensorrt_llm.mpi_rank(),
+                                              debug_mode=False,
+                                              stream=self.stream)
+            self.model_config = self.model.session._model_config
+            self.runtime_mapping = self.model.session.mapping
+        else:
+            self.model = TRTLLMEncDecModel.from_engine(
+                os.path.basename(self.args.hf_model_dir),
+                self.args.llm_engine_dir,
+                skip_encoder=(self.model_type == 'nougat'),
+                debug_mode=False,
+                stream=self.stream)
+
+            if self.model_type == 'nougat':
+                self.model_config = self.model.decoder_model_config
+                self.runtime_mapping = self.model.decoder_runtime_mapping
+            else:
+                self.model_config = self.model.encoder_model_config
+                self.runtime_mapping = self.model.encoder_runtime_mapping
+
+    @staticmethod
+    def tokenizer_image_token(
+        prompt, tokenizer, image_token_index=-200
+    ):
+        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("<image>")]
+
+        def insert_separator(X, sep):
+            return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
+
+        input_ids = []
+        offset = 0
+        if (
+            len(prompt_chunks) > 0
+            and len(prompt_chunks[0]) > 0
+            and prompt_chunks[0][0] == tokenizer.bos_token_id
+        ):
+            offset = 1
+            input_ids.append(prompt_chunks[0][0])
+
+        for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
+            input_ids.extend(x[offset:])
+
+        input_ids =  torch.tensor(input_ids, dtype=torch.long)
+        input_ids[input_ids == image_token_index] = 0
+        return input_ids
+
+    def split_prompt_by_images(self, tensor):
+        batch_splits = []
+        for batch in tensor:
+            # Find indices where value is zero (<image>)
+            zero_indices = (batch == 0).nonzero(as_tuple=False).squeeze(0)
+            # Add starting point for slicing
+            start_idx = 0
+            splits = []
+            for idx in zero_indices:
+                if start_idx != idx:  # Ensure not slicing zero-length tensors
+                    splits.append(batch[start_idx:idx].unsqueeze(0))
+                start_idx = idx + 1  # Move start index past the zero
+            if start_idx < len(batch):  # Handle last segment if it's not zero-ending
+                splits.append(batch[start_idx:].unsqueeze(0))
+            # Remove empty tensors resulting from consecutive zeros
+            splits = [split for split in splits if split.numel() > 0]
+            batch_splits.append(splits)
+
+        return batch_splits
+
+    def generate(self, pre_prompt, post_prompt, images, decoder_input_ids,
+                 max_new_tokens, warmup):
+        if not warmup:
+            profiler.start("Generate")
+            profiler.start("Vision")
+        visual_features, visual_atts = self.get_visual_features(images)
+        if not warmup:
+            profiler.stop("Vision")
+
+        batch_size = len(pre_prompt)
+        assert batch_size == 1, "batching support is not implemented yet."
+        
+        # TODO: check if this support batching
+        # input_ids = (batch_size, #tokens)
+        input_ids = self.tokenizer_image_token(pre_prompt[0] + post_prompt[0], self.tokenizer).unsqueeze(0)
+        batch_split_prompts = self.split_prompt_by_images(input_ids)
+        first_batch_split_prompts = batch_split_prompts[0]
+        length = visual_atts.shape[0] * visual_atts.shape[1]
+        for ids in first_batch_split_prompts:
+            length += ids.shape[1]
+        
+        input_lengths = torch.IntTensor([length] * args.batch_size).to(
+            torch.int32)
+        input_ids, ptuning_args = self.setup_fake_prompts(
+            visual_features, first_batch_split_prompts, input_lengths)
+
+        if warmup: return None
+
+        profiler.start("LLM")
+        if self.decoder_llm:
+            end_id = self.tokenizer.eos_token_id
+            if 'opt' in self.model_type and 'blip2' in self.model_type:
+                # For BLIP2-OPT, model outputs a "\n" at the end.
+                # we avoid it by using newline as the end token
+                end_id = self.tokenizer.encode("\n",
+                                               add_special_tokens=False)[0]
+
+            ptuning_args[0] = torch.stack([ptuning_args[0]])
+            output_ids = self.model.generate(input_ids,
+                                             sampling_config=None,
+                                             prompt_table=ptuning_args[0],
+                                             max_new_tokens=max_new_tokens,
+                                             end_id=end_id,
+                                             pad_id=self.tokenizer.pad_token_id,
+                                             top_k=self.args.top_k,
+                                             num_beams=self.args.num_beams,
+                                             output_sequence_lengths=False,
+                                             return_dict=False)
+        else:
+            if self.model_type == 'nougat':
+                # Trim encoder input_ids to match visual features shape
+                ids_shape = (self.args.batch_size, visual_features.shape[1])
+                input_ids = torch.zeros(ids_shape, dtype=torch.int32)
+
+            output_ids = self.model.generate(
+                input_ids,
+                decoder_input_ids,
+                max_new_tokens,
+                num_beams=self.args.num_beams,
+                bos_token_id=self.tokenizer.bos_token_id,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+                debug_mode=False,
+                prompt_embedding_table=ptuning_args[0],
+                prompt_tasks=ptuning_args[1],
+                prompt_vocab_size=ptuning_args[2])
+
+            # Reset input_lengths to match decoder_input_ids
+            input_lengths = torch.ones(input_lengths.shape,
+                                       dtype=input_lengths.dtype)
+        profiler.stop("LLM")
+
+        if tensorrt_llm.mpi_rank() == 0:
+            # Extract a list of tensors of shape beam_width x output_ids.
+            output_beams_list = [
+                self.tokenizer.batch_decode(
+                    output_ids[batch_idx, :, input_lengths[batch_idx]:],
+                    skip_special_tokens=True)
+                for batch_idx in range(self.args.batch_size)
+            ]
+
+            stripped_text = [[
+                output_beams_list[batch_idx][beam_idx].strip()
+                for beam_idx in range(self.args.num_beams)
+            ] for batch_idx in range(self.args.batch_size)]
+            profiler.stop("Generate")
+            return stripped_text
+        else:
+            profiler.stop("Generate")
+            return None
+
+    def get_visual_features(self, image):
+        visual_features = {'input': image.half()}
+        visual_output_info = self.visual_encoder_session.infer_shapes(
+            [TensorInfo('input', trt.DataType.HALF, image.shape)])
+        visual_outputs = {
+            t.name: torch.empty(tuple(t.shape),
+                                dtype=trt_dtype_to_torch(t.dtype),
+                                device=image.device)
+            for t in visual_output_info
+        }
+
+        ok = self.visual_encoder_session.run(visual_features, visual_outputs,
+                                             self.stream.cuda_stream)
+        assert ok, "Runtime execution failed for vision encoder session"
+        self.stream.synchronize()
+
+        image_embeds = visual_outputs['output']
+        image_atts = torch.ones(image_embeds.size()[:-1],
+                                dtype=torch.long).to(image.device)
+
+        return image_embeds, image_atts
+
+    def setup_fake_prompts(self, visual_features, split_input_ids,
+                           input_lengths):
+        # visual_features (num_images, feature_len, token_embed)
+        # Assemble fake prompts which points to image embedding actually
+        input_ids = [split_input_ids[0]]
+        fake_prompt_counter = self.model_config.vocab_size
+        assert len(visual_features) <= len(split_input_ids), "Unexpected number of visual features. Please check #<image> in prompt and the #image files."
+        for idx, visual_feature in enumerate(visual_features):
+            fake_prompt_id = torch.arange(fake_prompt_counter, fake_prompt_counter + visual_feature.shape[0])
+            fake_prompt_counter += visual_feature.shape[0]
+            fake_prompt_id = fake_prompt_id.reshape(1, visual_feature.shape[0])
+            input_ids.append(fake_prompt_id)
+            # in case no post prompt
+            if len(split_input_ids) > idx + 1:
+                input_ids.append(split_input_ids[idx + 1])
+        
+        input_ids = torch.cat(input_ids, dim=1).contiguous().to(torch.int32)
+
+        if self.decoder_llm or self.runtime_mapping.is_first_pp_rank():
+            ptuning_args = self.ptuning_setup(visual_features, input_ids,
+                                              input_lengths)
+        else:
+            ptuning_args = [None, None, None]
+            
+        ptuning_args[0] = torch.cat((ptuning_args[0], ptuning_args[0]))
+
+        return input_ids, ptuning_args
+
+    def ptuning_setup(self, prompt_table, input_ids, input_lengths):
+        hidden_size = self.model_config.hidden_size * self.runtime_mapping.tp_size
+        if prompt_table is not None:
+            task_vocab_size = torch.tensor(
+                [prompt_table.shape[1]],
+                dtype=torch.int32,
+            ).cuda()
+            prompt_table = prompt_table.view(
+                (prompt_table.shape[0] * prompt_table.shape[1],
+                 prompt_table.shape[2]))
+
+            assert prompt_table.shape[
+                1] == hidden_size, "Prompt table dimensions do not match hidden size"
+
+            prompt_table = prompt_table.cuda().to(
+                dtype=tensorrt_llm._utils.str_dtype_to_torch(
+                    self.model_config.dtype))
+        else:
+            prompt_table = torch.empty([1, hidden_size]).cuda()
+            task_vocab_size = torch.zeros([1]).cuda()
+
+        if self.model_config.remove_input_padding:
+            tasks = torch.zeros([torch.sum(input_lengths)],
+                                dtype=torch.int32).cuda()
+            if self.decoder_llm: tasks = tasks.unsqueeze(0)
+        else:
+            tasks = torch.zeros(input_ids.shape, dtype=torch.int32).cuda()
+
+        return [prompt_table, tasks, task_vocab_size]
+
+    def setup_inputs(self, input_text, images):
+        if 'vila' in self.model_type:
+            # LLaVA and VILA
+            if self.model_type == "llava":
+                pre_prompt = "USER:\n"
+                if input_text is None:
+                    input_text = "Question: which city is this? Answer:"
+            elif self.model_type == "vila":
+                pre_prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: "
+                if input_text is None:
+                    input_text = "Please describe the traffic condition."
+            post_prompt = input_text + " ASSISTANT:"
+
+            if self.model_type == "vila":
+                sys.path.append(f"{VILA_PATH}")
+                from llava.model import LlavaLlamaModel, LlavaLlamaConfig
+                from transformers import AutoModel
+                model = AutoModel.from_pretrained(self.args.hf_model_dir,
+                    device_map='auto',
+                    trust_remote_code=True,
+                )
+                vision_tower = model.get_vision_tower()
+                image_processor = vision_tower.image_processor
+                from llava.mm_utils import process_images
+                image = process_images(images, image_processor, model.config).to(model.device, dtype=torch.float16)
+        else:
+            raise NotImplementedError("Unsupported model.")
+
+        # Repeat inputs to match batch size
+        pre_prompt = [pre_prompt] * self.args.batch_size
+        post_prompt = [post_prompt] * self.args.batch_size
+        image = image.to(self.device)
+
+        # Generate decoder_input_ids for enc-dec models
+        # Custom prompts can be added as:
+        # decoder_input_ids = model.tokenizer(decoder_prompt).input_ids
+        if self.decoder_llm:
+            decoder_input_ids = None
+        else:
+            config = AutoConfig.from_pretrained(args.hf_model_dir)
+            decoder_start_id = config.decoder_start_token_id  # T5
+            if decoder_start_id is None:
+                decoder_start_id = config.decoder.bos_token_id  # Nougat
+
+            decoder_input_ids = torch.IntTensor([[decoder_start_id]])
+            decoder_input_ids = decoder_input_ids.repeat((args.batch_size, 1))
+
+        return input_text, pre_prompt, post_prompt, image, decoder_input_ids
+
+    def run(self, input_text, input_image, max_new_tokens):
+        input_text, pre_prompt, post_prompt, processed_image, decoder_input_ids = model.setup_inputs(
+            input_text, input_image)
+
+        model.generate(pre_prompt,
+                       post_prompt,
+                       processed_image,
+                       decoder_input_ids,
+                       max_new_tokens,
+                       warmup=True)
+        num_iters = self.profiling_iterations if self.args.run_profiling else 1
+        for _ in range(num_iters):
+            output_text = model.generate(pre_prompt,
+                                         post_prompt,
+                                         processed_image,
+                                         decoder_input_ids,
+                                         max_new_tokens,
+                                         warmup=False)
+        if self.runtime_rank == 0:
+            self.print_result(input_text, output_text)
+        return output_text
+
+    def print_result(self, input_text, output_text):
+        logger.info("---------------------------------------------------------")
+        if self.model_type != 'nougat':
+            logger.info(f"\n[Q] {input_text}")
+        logger.info(f"\n[A] {output_text[0]}")
+
+        if args.num_beams == 1:
+            output_ids = self.tokenizer(output_text[0][0],
+                                        add_special_tokens=False)['input_ids']
+            logger.info(f"Generated {len(output_ids)} tokens")
+
+        if self.args.check_accuracy:
+            for i in range(self.args.batch_size - 1):
+                if not (output_text[i] == output_text[i + 1]):
+                    logger.info(f"Output {i} and {i + 1} do not match")
+                    assert False
+            if self.model_type != 'nougat':
+                if self.model_type == "vila":
+                    assert output_text[0][0].lower(
+                    ) == 'the traffic condition in the image is quite busy, with multiple cars and bicycles sharing the road. there are also pedestrians walking on'
+                else:
+                    assert output_text[0][0].lower() == 'singapore'
+
+        if self.args.run_profiling:
+            msec_per_batch = lambda name: 1000 * profiler.elapsed_time_in_sec(
+                name) / self.profiling_iterations
+            logger.info('Latencies per batch (msec)')
+            logger.info('TRT vision encoder: %.1f' % (msec_per_batch('Vision')))
+            logger.info('TRTLLM LLM generate: %.1f' % (msec_per_batch('LLM')))
+            logger.info('Multimodal generate: %.1f' %
+                        (msec_per_batch('Generate')))
+
+        logger.info("---------------------------------------------------------")
+
+
+def load_images(image_files):
+    def load_image(image_file):
+        if image_file.startswith("http") or image_file.startswith("https"):
+            print("downloading image from url", args.image_file)
+            response = requests.get(image_file)
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+        else:
+            image = Image.open(image_file).convert("RGB")
+        return image
+    out = []
+    for image_file in image_files:
+        image = load_image(image_file)
+        out.append(image)
+    return out
+
+if __name__ == '__main__':
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    args = parse_arguments()
+    tensorrt_llm.logger.set_level(args.log_level)
+
+    model = MultimodalModelRunner(args)
+
+    images = load_images(args.image_file.split(args.sep))
+    text_output = model.run(args.input_text, images, args.max_new_tokens)
diff --git a/environment_setup.sh b/environment_setup.sh
index 9c4e413c..d9a0d891 100755
--- a/environment_setup.sh
+++ b/environment_setup.sh
@@ -1,5 +1,13 @@
-conda create -n vila python=3.10 -y
-conda activate vila
+CONDA_ENV=${1:-""}
+
+if [ -n "$CONDA_ENV" ]; then
+    conda create -n $CONDA_ENV python=3.10 -y
+    conda activate $CONDA_ENV
+    # this is optional if you prefer to system built-in nvcc.
+    conda install -c nvidia cuda-toolkit -y
+else
+    echo "skip conda enviroments"
+fi
 
 pip install --upgrade pip  # enable PEP 660 support
 wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.4.2/flash_attn-2.4.2+cu118torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
@@ -8,5 +16,5 @@ pip install -e .
 pip install -e ".[train]"
 
 pip install git+https://github.com/huggingface/transformers@v4.36.2
-cp -r ./llava/train/transformers_replace/* ~/anaconda3/envs/vila/lib/python3.10/site-packages/transformers/models/
-
+site_pkg_path=$(python -c 'import site; print(site.getsitepackages()[0])')
+cp -rv ./llava/train/transformers_replace/* $site_pkg_path/transformers/
diff --git a/inference_test/inference_test.py b/inference_test/inference_test.py
index 0f21a45a..2ef7f3ce 100644
--- a/inference_test/inference_test.py
+++ b/inference_test/inference_test.py
@@ -1,20 +1,38 @@
-'''
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+"""
 Inference test to run all examples from the paper and compare w/ expected output.
 Both the inference results and expected output will be printed out.
 
 Currently do not support multi-turn chat. Each time an image and question are input and answer is output.
-'''
-
+"""
 
 import argparse
-import os
 import json
+import os
+
 import torch
 from PIL import Image
+
+from llava.constants import IMAGE_TOKEN_INDEX
 from llava.conversation import SeparatorStyle, conv_templates
+from llava.mm_utils import (KeywordsStoppingCriteria, process_images,
+                            tokenizer_image_token)
 from llava.model import *
-from llava.mm_utils import process_images, KeywordsStoppingCriteria, tokenizer_image_token
-from llava.constants import IMAGE_TOKEN_INDEX
 
 DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
 
@@ -26,14 +44,16 @@ def eval_model(args, model, tokenizer, image_processor):
     # read json file
     with open(args.test_json_path) as f:
         all_test_cases = json.load(f)
-    
+
     result_list = []
-    print(len(all_test_cases['test_cases']))
-    
-    for test_case in all_test_cases['test_cases']:
+    print(len(all_test_cases["test_cases"]))
+
+    for test_case in all_test_cases["test_cases"]:
         # read images first
         image_file_list = test_case["image_paths"]
-        image_list = [Image.open(os.path.join(args.test_image_path, image_file)).convert('RGB') for image_file in image_file_list]
+        image_list = [
+            Image.open(os.path.join(args.test_image_path, image_file)).convert("RGB") for image_file in image_file_list
+        ]
         image_tensor = process_images(image_list, image_processor, model.config)
 
         # image_tokens = DEFAULT_IMAGE_PATCH_TOKEN * image_token_len
@@ -41,7 +61,7 @@ def eval_model(args, model, tokenizer, image_processor):
         for i in range(len(test_case["QAs"])):
             query = test_case["QAs"][i]["question"]
             query_text = query
-            
+
             if 1:
                 # query = query.replace("<image>", image_tokens)
                 if len(image_list) < 3:
@@ -75,12 +95,12 @@ def eval_model(args, model, tokenizer, image_processor):
                 conv.append_message(conv.roles[1], None)
                 prompt = conv.get_prompt()
 
-            print("%"*10+" "*5+"VILA Response"+" "*5+"%"*10)
+            print("%" * 10 + " " * 5 + "VILA Response" + " " * 5 + "%" * 10)
 
             # inputs = tokenizer([prompt])
             inputs = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX)
             input_ids = torch.as_tensor(inputs).cuda().unsqueeze(0)
-            
+
             stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
             keywords = [stop_str]
             stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
@@ -89,25 +109,22 @@ def eval_model(args, model, tokenizer, image_processor):
             with torch.inference_mode():
                 output_ids = model.generate(
                     input_ids,
-                    images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True),
+                    images=image_tensor.to(dtype=torch.float16, device="cuda", non_blocking=True),
                     do_sample=True if args.temperature > 0 else False,
                     temperature=args.temperature,
                     top_p=0.7,
                     # top_p=args.top_p,
                     # num_beams=args.num_beams,
                     max_new_tokens=512,
-                    #use_cache=True,
+                    # use_cache=True,
                     stopping_criteria=[stopping_criteria],
                 )
-            input_token_len = input_ids.shape[1]
-            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-            if n_diff_input_output > 0:
-                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+
+            outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
             outputs = outputs.strip()
 
-            print(f'Question: {query_text}')
-            print(f'VILA output: {outputs}')
+            print(f"Question: {query_text}")
+            print(f"VILA output: {outputs}")
             print(f'Expected output: {test_case["QAs"][i]["expected_answer"]}')
 
             result_list.append(
@@ -127,12 +144,10 @@ def eval_model(args, model, tokenizer, image_processor):
 
     args = parser.parse_args()
 
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_name, None, "llava_llama")
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_name, "llava_llama", None)
     result_list = eval_model(args, model, tokenizer, image_processor)
     save_name = f"inference-test_{args.model_name.split('/')[-1]}"
     if "nosys" in args.conv_mode:
         save_name += "_nosys"
     save_name += ".json"
     result_list_str = json.dumps(result_list, indent=2)
-
-
diff --git a/inference_test/test_data/car_blocker.png b/inference_test/test_data/car_blocker.png
new file mode 100755
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diff --git a/inference_test/test_data/count_giraff_4.png b/inference_test/test_data/count_giraff_4.png
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HcmV?d00001

diff --git a/llava/__init__.py b/llava/__init__.py
index 4d1f016d..8b137891 100644
--- a/llava/__init__.py
+++ b/llava/__init__.py
@@ -1 +1 @@
-from .model import LlavaLlamaForCausalLM
+
diff --git a/llava/conversation.py b/llava/conversation.py
index 8d9d3dd4..c3c70864 100644
--- a/llava/conversation.py
+++ b/llava/conversation.py
@@ -29,6 +29,7 @@ class SeparatorStyle(Enum):
     PLAIN = auto()
     LLAMA_2 = auto()
     MISTRAL = auto()
+    LLAMA_3 = auto()
 
 
 @dataclasses.dataclass
@@ -77,6 +78,15 @@ def get_prompt(self):
                     ret += role + ": " + message + seps[i % 2]
                 else:
                     ret += role + ":"
+        elif self.sep_style == SeparatorStyle.LLAMA_3:
+            ret = self.system + self.sep
+            for role, message in messages:
+                if message:
+                    if type(message) is tuple:
+                        message = message[0]
+                    ret += role + message + self.sep
+                else:
+                    ret += role
         elif self.sep_style == SeparatorStyle.MPT:
             ret = self.system + self.sep
             for role, message in messages:
@@ -409,9 +419,38 @@ def dict(self):
     version="v1_mmtag",
 )
 
+hermes_2 = Conversation(
+    system='<|im_start|>system\nAnswer the questions.',
+    roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+    sep_style=SeparatorStyle.MPT,
+    sep='<|im_end|>',
+    messages=(
+    ),
+    offset=0,
+    version="hermes-2"
+)
+
+
+# Template added by Yukang. Note (kentang-mit@): sep is <|eot_id|> for official template.
+llama_3_chat = Conversation(
+    system="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. "
+           "You are able to understand the visual content that the user provides, "
+           "and assist the user with a variety of tasks using natural language.",
+    roles=("<|start_header_id|>user<|end_header_id|>\n\n",
+           "<|start_header_id|>system<|end_header_id|>\n\n"),
+    version="llama_v3",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_3,
+    sep="<|end_of_text|>",
+)
+
+
 default_conversation = conv_vicuna_v1
 conv_templates = {
     "default": conv_vicuna_v0,
+    "hermes-2": hermes_2,
+    "llama_3": llama_3_chat,
     "v0": conv_vicuna_v0,
     "v1": conv_vicuna_v1,
     "vicuna_v1": conv_vicuna_v1,
diff --git a/llava/data/__init__.py b/llava/data/__init__.py
index 63852071..f84c6a93 100644
--- a/llava/data/__init__.py
+++ b/llava/data/__init__.py
@@ -1,2 +1,3 @@
 from .dataset import *
-from .datasets_mixture import *
\ No newline at end of file
+from .datasets_mixture import *
+from .simple_vila_webdataset import VILAWebDataset
diff --git a/llava/data/dataset.py b/llava/data/dataset.py
index a729ab3f..3564adfa 100644
--- a/llava/data/dataset.py
+++ b/llava/data/dataset.py
@@ -13,38 +13,51 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 
-import os, os.path as osp
 import base64
 import copy
-import llava.data.datasets_mixture as datasets_mixture
-from dataclasses import dataclass, field
 import io
-import numpy as np
-import random
 import json
 import logging
+import os
+import os.path as osp
 import pathlib
 import pickle
-import time
-from typing import Dict, Optional, Sequence, List
+import random
 import re
+import time
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Sequence
 
+import numpy as np
+import PIL
 import torch
-#torch.backends.cudnn.enabled = False
-
 import transformers
-
-from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from datasets import concatenate_datasets, load_dataset
+from PIL import Image, ImageFile
+from pytorchvideo.data.encoded_video import EncodedVideo
 from torch.utils.data import ConcatDataset, Dataset
-from llava.train.llava_trainer import LLaVATrainer
-from llava.train.args import TrainingArguments, DataArguments
+from torchvision.transforms import Resize
+from transformers import PreTrainedTokenizer
 
+import llava.data.datasets_mixture as datasets_mixture
 from llava import conversation as conversation_lib
+from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                             DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
+                             IMAGE_TOKEN_INDEX)
+from llava.data.datasets_mixture import DATASETS
+from llava.eval.mmmu_utils.data_utils import (CAT_SHORT2LONG, construct_prompt,
+                                              load_yaml, process_single_sample)
+from llava.mm_utils import is_gemma_tokenizer, tokenizer_image_token, opencv_extract_frames, process_image
 from llava.model import *
-from llava.mm_utils import tokenizer_image_token
+from llava.train.args import DataArguments, TrainingArguments
+from llava.train.llava_trainer import LLaVATrainer
+
+# torch.backends.cudnn.enabled = False
 
-from PIL import Image
 
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+PIL.Image.MAX_IMAGE_PIXELS = 1000000000
 # local_rank = None
 
 # def rank0_print(*args):
@@ -52,9 +65,7 @@
 #         print(*args)
 
 
-
-def _tokenize_fn(strings: Sequence[str],
-                 tokenizer: transformers.PreTrainedTokenizer) -> Dict:
+def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
     """Tokenize a list of strings."""
     tokenized_list = [
         tokenizer(
@@ -63,14 +74,12 @@ def _tokenize_fn(strings: Sequence[str],
             padding="longest",
             max_length=tokenizer.model_max_length,
             truncation=True,
-        ) for text in strings
-    ]
-    input_ids = labels = [
-        tokenized.input_ids[0] for tokenized in tokenized_list
+        )
+        for text in strings
     ]
+    input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
     input_ids_lens = labels_lens = [
-        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item()
-        for tokenized in tokenized_list
+        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
     ]
     return dict(
         input_ids=input_ids,
@@ -87,7 +96,7 @@ def _mask_targets(target, tokenized_lens, speakers):
     target[:cur_idx] = IGNORE_INDEX
     for tokenized_len, speaker in zip(tokenized_lens, speakers):
         if speaker == "human":
-            target[cur_idx+2:cur_idx + tokenized_len] = IGNORE_INDEX
+            target[cur_idx + 2 : cur_idx + tokenized_len] = IGNORE_INDEX
         cur_idx += tokenized_len
 
 
@@ -103,35 +112,38 @@ def _add_speaker_and_signal(header, source, get_conversation=True):
         elif from_str.lower() == "gpt":
             from_str = conversation_lib.default_conversation.roles[1]
         else:
-            from_str = 'unknown'
-        sentence["value"] = (BEGIN_SIGNAL + from_str + ": " +
-                             sentence["value"] + END_SIGNAL)
+            from_str = "unknown"
+        sentence["value"] = BEGIN_SIGNAL + from_str + ": " + sentence["value"] + END_SIGNAL
         if get_conversation:
             conversation += sentence["value"]
     conversation += BEGIN_SIGNAL
     return conversation
 
 
-def preprocess_multimodal(
-    sources: Sequence[str],
-    data_args: DataArguments
-) -> Dict:
+def preprocess_multimodal(sources: Sequence[str], data_args: DataArguments) -> Dict:
     is_multimodal = data_args.is_multimodal
     if not is_multimodal:
         return sources
 
     for source in sources:
-        for sentence in source:
-            if DEFAULT_IMAGE_TOKEN in sentence['value']:
-                sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '').strip()
-                sentence['value'] = DEFAULT_IMAGE_TOKEN + '\n' + sentence['value']
-                sentence['value'] = sentence['value'].strip()
+        concat_values = "".join([sentence["value"] for sentence in source])
+        for sid, sentence in enumerate(source):
+            # In multimodal conversations, we automatically prepend '<image>' at the start of the first sentence if it doesn't already contain one.
+            if sid == 0 and DEFAULT_IMAGE_TOKEN not in concat_values:
+                sentence["value"] = f"{DEFAULT_IMAGE_TOKEN}\n" + sentence["value"]
+            if DEFAULT_IMAGE_TOKEN in sentence["value"]:
+                sentence_chunks = [chunk.strip() for chunk in sentence["value"].split(DEFAULT_IMAGE_TOKEN)]
+                sentence_chunks = [
+                    chunk + " " if not (chunk.endswith("\n")) else chunk for chunk in sentence_chunks[:-1]
+                ] + [sentence_chunks[-1]]
+                sentence["value"] = f"{DEFAULT_IMAGE_TOKEN}\n".join(sentence_chunks).strip()
+
+                replace_token = DEFAULT_IMAGE_TOKEN
                 if "mmtag" in conversation_lib.default_conversation.version:
-                    sentence['value'] = sentence['value'].replace(DEFAULT_IMAGE_TOKEN, '<Image>' + DEFAULT_IMAGE_TOKEN + '</Image>')
-            replace_token = DEFAULT_IMAGE_TOKEN
-            if data_args.mm_use_im_start_end:
-                replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
-            sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
+                    replace_token = "<Image>" + replace_token + "</Image>"
+                if data_args.mm_use_im_start_end:
+                    replace_token = DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
+                sentence["value"] = sentence["value"].replace(DEFAULT_IMAGE_TOKEN, replace_token)
 
     return sources
 
@@ -163,7 +175,10 @@ def preprocess_llama_2(
     # print(f"{conversations[0]}")
 
     if has_image:
-        input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
+        input_ids = torch.stack(
+            [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations],
+            dim=0,
+        )
     else:
         input_ids = tokenizer(
             conversations,
@@ -175,7 +190,10 @@ def preprocess_llama_2(
 
     targets = input_ids.clone()
 
-    assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 or conv.sep_style == conversation_lib.SeparatorStyle.MISTRAL
+    assert (
+        conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2
+        or conv.sep_style == conversation_lib.SeparatorStyle.MISTRAL
+    )
 
     # Mask targets
     if is_mistral:
@@ -212,10 +230,97 @@ def preprocess_llama_2(
         if cur_len < tokenizer.model_max_length:
             if cur_len != total_len:
                 target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
+                print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." f" (ignored)")
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+    )
+
+
+def preprocess_llama_3(
+    sources,
+    tokenizer: transformers.PreTrainedTokenizer,
+    has_image: bool = False,
+    no_system_prompt: bool = False,
+) -> Dict:
+    # Note: implemented by yukang2017@, verified by kentang-mit@
+    conv = conversation_lib.default_conversation.copy()
+    roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+    if no_system_prompt:
+        conv.system = ""
+
+    # Apply prompt templates
+    conversations = []
+    for i, source in enumerate(sources):
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], f"{i}"
+            conv.append_message(role, sentence["value"])
+        conversations.append(conv.get_prompt())
+    # Tokenize conversations
+
+    if has_image:
+        input_ids = torch.stack(
+            [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
+    else:
+        input_ids = tokenizer(
+            conversations,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        ).input_ids
+
+    targets = input_ids.clone()
+    assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_3
+
+    # Mask targets
+    sep = conv.sep + conv.roles[1]
+    for conversation, target in zip(conversations, targets):
+        total_len = int(target.ne(tokenizer.pad_token_id).sum())
+
+        rounds = conversation.split(conv.sep)
+        re_rounds = [conv.sep.join(rounds[:3])]  # system + user + gpt
+        for conv_idx in range(3, len(rounds), 2):
+            re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx + 2]))  # user + gpt
+        cur_len = 0
+        target[:cur_len] = IGNORE_INDEX
+        for i, rou in enumerate(re_rounds):
+            if rou == "":
+                break
+
+            parts = rou.split(sep)
+            if len(parts) != 2:
+                print(f"WARNING: parts!=: {parts}")
+                break
+            parts[0] += sep
+
+            if has_image:
+                round_len = len(tokenizer_image_token(rou, tokenizer))
+                instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 1
+            else:
+                round_len = len(tokenizer(rou).input_ids)
+                instruction_len = len(tokenizer(parts[0]).input_ids) - 1
+
+            # include <|eot_id|> for all rounds
+            round_len += 1
+            instruction_len += 1
+
+            target[cur_len: cur_len + instruction_len] = IGNORE_INDEX
+            cur_len += round_len
+
+        target[cur_len:] = IGNORE_INDEX
+
+        if cur_len < tokenizer.model_max_length:
+            if cur_len != total_len:
+                target[:] = IGNORE_INDEX
+                print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}. {sources}" f" (ignored)")
 
     return dict(
         input_ids=input_ids,
@@ -251,7 +356,10 @@ def preprocess_v1(
     # Tokenize conversations
 
     if has_image:
-        input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
+        input_ids = torch.stack(
+            [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations],
+            dim=0,
+        )
     else:
         input_ids = tokenizer(
             conversations,
@@ -285,13 +393,13 @@ def preprocess_v1(
             if has_image:
                 round_len = len(tokenizer_image_token(rou, tokenizer))
                 instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
-                if i > 0:
+                if i > 0 and not is_gemma_tokenizer(tokenizer):
                     round_len = round_len - 1
                     instruction_len = instruction_len - 1
             else:
                 round_len = len(tokenizer(rou).input_ids)
                 instruction_len = len(tokenizer(parts[0]).input_ids) - 2
-                if i > 0:
+                if i > 0 and not is_gemma_tokenizer(tokenizer):
                     round_len = round_len - 1
                     instruction_len = instruction_len - 1
 
@@ -303,10 +411,7 @@ def preprocess_v1(
         if cur_len < tokenizer.model_max_length:
             if cur_len != total_len:
                 target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
+                print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}. {sources}" f" (ignored)")
 
     return dict(
         input_ids=input_ids,
@@ -317,8 +422,12 @@ def preprocess_v1(
 def preprocess_mpt(
     sources,
     tokenizer: transformers.PreTrainedTokenizer,
+    has_image: bool = False,
+    no_system_prompt: bool = False,
 ) -> Dict:
     conv = conversation_lib.default_conversation.copy()
+    if no_system_prompt:
+        conv.system = ""
     roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
 
     # Apply prompt templates
@@ -336,19 +445,31 @@ def preprocess_mpt(
         conversations.append(conv.get_prompt())
 
     # Tokenize conversations
-    input_ids = torch.stack([tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations], dim=0)
+    if has_image:
+        input_ids = torch.stack(
+            [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations], dim=0
+        )
+    else:
+        input_ids = tokenizer(
+            conversations,
+            return_tensors="pt",
+            padding="longest",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+        ).input_ids
+
     targets = input_ids.clone()
     assert conv.sep_style == conversation_lib.SeparatorStyle.MPT
 
     # Mask targets
     sep = conv.sep + conv.roles[1]
-    for conversation, target in zip(conversations, targets):
+    for conversation, target, its in zip(conversations, targets, input_ids):
         total_len = int(target.ne(tokenizer.pad_token_id).sum())
 
         rounds = conversation.split(conv.sep)
-        re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt
+        re_rounds = [conv.sep.join(rounds[:3])]  # system + user + gpt
         for conv_idx in range(3, len(rounds), 2):
-            re_rounds.append(conv.sep.join(rounds[conv_idx:conv_idx+2]))    # user + gpt
+            re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2]))  # user + gpt
         cur_len = 0
         target[:cur_len] = IGNORE_INDEX
         for i, rou in enumerate(re_rounds):
@@ -359,20 +480,22 @@ def preprocess_mpt(
             if len(parts) != 2:
                 break
             parts[0] += sep
-            round_len = len(tokenizer_image_token(rou, tokenizer)) + len(tokenizer_image_token(conv.sep, tokenizer))
-            instruction_len = len(tokenizer_image_token(parts[0], tokenizer))
+
+            if has_image:
+                round_len = len(tokenizer_image_token(rou, tokenizer)) + len(tokenizer_image_token(conv.sep, tokenizer))
+                instruction_len = len(tokenizer_image_token(parts[0], tokenizer))
+            else:
+                round_len = len(tokenizer(rou).input_ids) + len(tokenizer(conv.sep).input_ids)
+                instruction_len = len(tokenizer(parts[0]).input_ids)
+
             target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
 
             cur_len += round_len
         target[cur_len:] = IGNORE_INDEX
-
         if cur_len < tokenizer.model_max_length:
             if cur_len != total_len:
                 target[:] = IGNORE_INDEX
-                print(
-                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
-                    f" (ignored)"
-                )
+                print(f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}. {len(re_rounds)} {sources}" f" (ignored)")
 
     return dict(
         input_ids=input_ids,
@@ -388,15 +511,15 @@ def preprocess_plain(
     conversations = []
     for source in sources:
         assert len(source) == 2
-        assert DEFAULT_IMAGE_TOKEN in source[0]['value']
-        source[0]['value'] = DEFAULT_IMAGE_TOKEN
-        conversation = source[0]['value'] + source[1]['value'] + conversation_lib.default_conversation.sep
+        assert DEFAULT_IMAGE_TOKEN in source[0]["value"]
+        source[0]["value"] = DEFAULT_IMAGE_TOKEN
+        conversation = source[0]["value"] + source[1]["value"] + conversation_lib.default_conversation.sep
         conversations.append(conversation)
     # tokenize conversations
-    input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
+    input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations]
     targets = copy.deepcopy(input_ids)
     for target, source in zip(targets, sources):
-        tokenized_len = len(tokenizer_image_token(source[0]['value'], tokenizer))
+        tokenized_len = len(tokenizer_image_token(source[0]["value"], tokenizer))
         target[:tokenized_len] = IGNORE_INDEX
 
     return dict(input_ids=input_ids, labels=targets)
@@ -406,7 +529,7 @@ def preprocess(
     sources: Sequence[str],
     tokenizer: transformers.PreTrainedTokenizer,
     has_image: bool = False,
-    no_system_prompt: bool = False, # only work for v1
+    no_system_prompt: bool = False,  # only work for v1
 ) -> Dict:
     """
     Given a list of sources, each is a conversation list. This transform:
@@ -415,28 +538,32 @@ def preprocess(
     3. Tokenize the concatenated conversation;
     4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX.
     """
+    if conversation_lib.default_conversation.version == "mpt" or conversation_lib.default_conversation.version == "hermes-2":
+        return preprocess_mpt(sources, tokenizer, has_image=has_image, no_system_prompt=no_system_prompt)
     if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.PLAIN:
         return preprocess_plain(sources, tokenizer)
     if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_2:
         return preprocess_llama_2(sources, tokenizer, has_image=has_image)
     if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.MISTRAL:
         return preprocess_llama_2(sources, tokenizer, has_image=has_image, is_mistral=True)
+    if conversation_lib.default_conversation.sep_style == conversation_lib.SeparatorStyle.LLAMA_3:
+        return preprocess_llama_3(sources, tokenizer, has_image=has_image, no_system_prompt=no_system_prompt)
     if conversation_lib.default_conversation.version.startswith("v1"):
         return preprocess_v1(sources, tokenizer, has_image=has_image, no_system_prompt=no_system_prompt)
-    if conversation_lib.default_conversation.version == "mpt":
-        return preprocess_mpt(sources, tokenizer)
+
     # add end signal and concatenate together
     conversations = []
     for source in sources:
         header = f"{conversation_lib.default_conversation.system}\n\n"
         conversation = _add_speaker_and_signal(header, source)
         conversations.append(conversation)
+
     # tokenize conversations
     def get_tokenize_len(prompts):
         return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts]
 
     if has_image:
-        input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors='pt') for prompt in conversations]
+        input_ids = [tokenizer_image_token(prompt, tokenizer, return_tensors="pt") for prompt in conversations]
     else:
         conversations_tokenized = _tokenize_fn(conversations, tokenizer)
         input_ids = conversations_tokenized["input_ids"]
@@ -453,7 +580,10 @@ def get_tokenize_len(prompts):
     return dict(input_ids=input_ids, labels=targets)
 
 
-class LazySupervisedDataset(Dataset):
+from llava.data.utils import VILAEncodedVideo
+
+
+class DummyDataset(Dataset):
     """Dataset for supervised fine-tuning.
     This class is originally implemented by the LLaVA team and modified by
     Ji Lin and Haotian Tang.
@@ -464,8 +594,115 @@ def __init__(self, data_path: str,
                  data_args: DataArguments,
                  image_folder: str,
                  training_args: TrainingArguments):
+        super(DummyDataset, self).__init__()
+        # list_data_dict = json.load(open(data_path, "r"))
+        self.num_dummy_samples = 32768
+        import random
+        import string
+
+        def generate_random_string(length):
+            letters = string.ascii_letters
+            result_str = ''.join(random.choice(letters) for _ in range(length))
+            return result_str
+        self.list_data_dict = []
+        for i in range(self.num_dummy_samples):
+            question = generate_random_string(32)
+            answer = question + generate_random_string(8)
+            data_dict = {
+                "id": i,
+                "image": "empty",
+                "conversations": [
+                    {
+                        "from": "human",
+                        "value": question,
+                    },
+                    {
+                        "from": "gpt", 
+                        "value": answer,
+                    },
+                ]
+            }
+            self.list_data_dict.append(data_dict)
+
+        # rank0_print("Formatting inputs...Skip in lazy mode")
+        print("Formatting inputs...Skip in lazy mode")
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+        self.image_folder = image_folder
+
+    def __len__(self):
+        return len(self.list_data_dict)
+
+    @property
+    def lengths(self):
+        length_list = []
+        for sample in self.list_data_dict:
+            img_tokens = 128 if "image" in sample else 0
+            length_list.append(sum(len(conv["value"].split()) for conv in sample["conversations"]) + img_tokens)
+        return length_list
+
+    @property
+    def modality_lengths(self):
+        length_list = []
+        for sample in self.list_data_dict:
+            cur_len = sum(len(conv["value"].split()) for conv in sample["conversations"])
+            cur_len = cur_len if "image" in sample else -cur_len
+            length_list.append(cur_len)
+        return length_list
+
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        sources = self.list_data_dict[i]
+        if isinstance(i, int):
+            sources = [sources]
+        assert len(sources) == 1, "Don't know why it is wrapped to a list"  # FIXME
+        if "image" in sources[0]:
+            image_file = self.list_data_dict[i]["image"]
+            image = process_image(image_file, self.data_args, self.image_folder)
+            sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
+        else:
+            sources = copy.deepcopy([e["conversations"] for e in sources])
+
+        data_dict = preprocess(
+            sources,
+            self.tokenizer,
+            has_image=(
+                "image" in self.list_data_dict[i]
+                or "video" in self.list_data_dict[i]
+                or "video_id" in self.list_data_dict[i]
+            ),
+        )
+        if isinstance(i, int):
+            data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
+
+        # image exist in the data
+        if "image" in self.list_data_dict[i]:
+            data_dict["image"] = image.unsqueeze(0)
+        else:
+            data_dict["image"] = None
+        return data_dict
+
+class LazySupervisedDataset(Dataset):
+    """Dataset for supervised fine-tuning.
+    This class is originally implemented by the LLaVA team and modified by
+    Ji Lin and Haotian Tang.
+    """
+
+    def __init__(
+        self,
+        data_path: str,
+        image_folder: str,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+    ):
         super(LazySupervisedDataset, self).__init__()
-        list_data_dict = json.load(open(data_path, "r"))
+        try:
+            with open(data_path, "r") as fp:
+                list_data_dict = json.load(fp)
+        except:
+            with open(data_path, "r") as fp:
+                list_data_dict = [json.loads(q) for q in fp]
 
         # rank0_print("Formatting inputs...Skip in lazy mode")
         print("Formatting inputs...Skip in lazy mode")
@@ -481,101 +718,147 @@ def __len__(self):
     def lengths(self):
         length_list = []
         for sample in self.list_data_dict:
-            img_tokens = 128 if 'image' in sample else 0
-            length_list.append(sum(len(conv['value'].split()) for conv in sample['conversations']) + img_tokens)
+            img_tokens = 128 if "image" in sample else 0
+            length_list.append(sum(len(conv["value"].split()) for conv in sample["conversations"]) + img_tokens)
         return length_list
 
     @property
     def modality_lengths(self):
         length_list = []
         for sample in self.list_data_dict:
-            cur_len = sum(len(conv['value'].split()) for conv in sample['conversations'])
-            cur_len = cur_len if 'image' in sample else -cur_len
+            cur_len = sum(len(conv["value"].split()) for conv in sample["conversations"])
+            cur_len = cur_len if "image" in sample else -cur_len
             length_list.append(cur_len)
         return length_list
+
+    
+
     
     @staticmethod
-    def _process_image(image_file, data_args, image_folder, resize=False):
-        processor = data_args.image_processor
-        if isinstance(image_file, str):
-            if image_folder is not None:
-                image = Image.open(os.path.join(image_folder, image_file)).convert('RGB')
-            else:
-                image = Image.open(image_file).convert('RGB')
+    def _load_video(video_path, num_video_frames, data_args, fps=None, frame_count=None):
+        from llava.mm_utils import opencv_extract_frames
+        from torchvision import transforms
+        video_loading_succeed = True
+        if "shortest_edge" in data_args.image_processor.size:
+            image_size = data_args.image_processor.size["shortest_edge"]
         else:
-            # image is stored in bytearray
-            image = image_file
-        if resize:
-            if hasattr(data_args.image_processor, "crop_size"):
-                # CLIP vision tower
-                crop_size = data_args.image_processor.crop_size
-            else:
-                # SIGLIP vision tower
-                assert hasattr(data_args.image_processor, "size")
-                crop_size = data_args.image_processor.size
-            image = image.resize((crop_size['height'], crop_size['width']))
-        if data_args.image_aspect_ratio == 'pad':
-            def expand2square(pil_img, background_color):
-                width, height = pil_img.size
-                if width == height:
-                    return pil_img
-                elif width > height:
-                    result = Image.new(pil_img.mode, (width, width), background_color)
-                    result.paste(pil_img, (0, (width - height) // 2))
-                    return result
-                else:
-                    result = Image.new(pil_img.mode, (height, height), background_color)
-                    result.paste(pil_img, ((height - width) // 2, 0))
-                    return result
-            image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
-            image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-        else:
-            image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-        return image
-    
+            image_size = data_args.image_processor.size["height"]
+        try:
+            pil_imgs = opencv_extract_frames(video_path, num_video_frames, fps, frame_count)
+        except Exception as e:
+            video_loading_succeed = False
+            print(f"bad data path {video_path}")
+            print(f"[DEBUG] Error processing {video_path}: {e}")
+            # video_outputs = torch.zeros(3, 8, image_size, image_size, dtype=torch.uint8)
+            pil_imgs = [torch.zeros(3, image_size, image_size, dtype=torch.float32)] * num_video_frames
+            pil_imgs = [Image.new("RGB", (448, 448), (0, 0, 0))] * num_video_frames
+
+        return pil_imgs, video_loading_succeed
+            
+
+
     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         sources = self.list_data_dict[i]
         if isinstance(i, int):
             sources = [sources]
         assert len(sources) == 1, "Don't know why it is wrapped to a list"  # FIXME
-        if 'image' in sources[0]:
-            image_file = self.list_data_dict[i]['image']
-            image = self._process_image(image_file, self.data_args, self.image_folder)
-            sources = preprocess_multimodal(
-                copy.deepcopy([e["conversations"] for e in sources]),
-                self.data_args)
+        if "image" in sources[0]:
+            image_file = self.list_data_dict[i]["image"]
+            if isinstance(image_file, list):
+                image = torch.stack(
+                    [process_image(img, self.data_args, self.image_folder) for img in image_file]
+                )
+            else:
+                image = process_image(image_file, self.data_args, self.image_folder)
+            sources = preprocess_multimodal(copy.deepcopy([e["conversations"] for e in sources]), self.data_args)
+        elif ("video" in sources[0]) or ("video_id" in sources[0]):
+            num_video_frames = self.data_args.num_video_frames
+            if "video" in sources[0]:
+                video_file = sources[0]["video"]
+            else:
+                video_file = sources[0]["video_id"] + ".mp4"
+            video_folder = self.image_folder
+            video_path = os.path.join(video_folder, video_file)
+            if 'fps' in sources[0]:
+                fps = sources[0]['fps']
+            else:
+                fps = None
+            if 'frame_count' in sources[0]:
+                frame_count = sources[0]['frame_count']
+            else:
+                frame_count = None
+
+            images, video_loading_succeed = self._load_video(video_path, num_video_frames, self.data_args, fps=fps, frame_count=frame_count)
+
+            image_tensor = torch.stack(
+                [process_image(image, self.data_args, None) for image in images]
+            )
+
+            if "video" in sources[0]:
+                question = sources[0]["conversations"][0]["value"].rstrip()
+                answer = sources[0]["conversations"][1]["value"].rstrip()
+            else:
+                question = sources[0]["q"]
+                answer = sources[0]["a"]
+
+            if not video_loading_succeed:
+                answer = "Empty video."
+
+            question = question.replace("<image>\n", "").replace("\n<image>", "").replace("<image>", "")
+            question = question.replace("<video>\n", "").replace("\n<video>", "").replace("<video>", "")
+            question = "<image>\n" * num_video_frames + question
+            conversation = [
+                {"from": "human", "value": question},
+                {"from": "gpt", "value": answer},
+            ]
+
+            sources = [conversation]
         else:
             sources = copy.deepcopy([e["conversations"] for e in sources])
+
+        # data_dict = preprocess(sources, self.tokenizer, has_image=("image" in self.list_data_dict[i]))
         data_dict = preprocess(
             sources,
             self.tokenizer,
-            has_image=('image' in self.list_data_dict[i]))
+            has_image=(
+                "image" in self.list_data_dict[i]
+                or "video" in self.list_data_dict[i]
+                or "video_id" in self.list_data_dict[i]
+            ),
+        )
         if isinstance(i, int):
-            data_dict = dict(input_ids=data_dict["input_ids"][0],
-                             labels=data_dict["labels"][0])
+            data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
 
         # image exist in the data
-        if 'image' in self.list_data_dict[i]:
-            data_dict['image'] = image.unsqueeze(0)
+        if "image" in self.list_data_dict[i]:
+            if len(image.shape) == 4:
+                data_dict["image"] = image
+            else:
+                data_dict["image"] = image.unsqueeze(0)
+        elif ("video" in self.list_data_dict[i]) or ("video_id" in self.list_data_dict[i]):
+            data_dict["image"] = image_tensor
+            if not video_loading_succeed:
+                data_dict['labels'][:] = IGNORE_INDEX
         else:
             # llava 1.5 way
             # image does not exist in the data, but the model is multimodal
             # crop_size = self.data_args.image_processor.crop_size
             # data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
             # vila way
-            data_dict['image'] = None
+            data_dict["image"] = None
         return data_dict
 
 
 class LazyMMC4Dataset(Dataset):
     """Dataset for supervised fine-tuning.
     This class is implemented by Ji Lin and Haotian Tang."""
+
     num_image_tokens = 576
 
     def __init__(
         self,
         data_path: str,
-        image_folfer: str,
+        image_folder: str,
         tokenizer: transformers.PreTrainedTokenizer,
         data_args: DataArguments,
         training_args: TrainingArguments,
@@ -590,24 +873,16 @@ def __init__(
         # actually shards and stats info
         n_shards = len(os.listdir(data_path)) // 2
         # n_shards = 100
-        count_info_list = sorted(
-            [f for f in os.listdir(data_path) if f.endswith(".count")]
-        )[:n_shards]
-        n_samples = [
-            int(open(os.path.join(data_path, f), "r").read().strip())
-            for f in count_info_list
-        ]
+        count_info_list = sorted([f for f in os.listdir(data_path) if f.endswith(".count")])[:n_shards]
+        n_samples = [int(open(os.path.join(data_path, f), "r").read().strip()) for f in count_info_list]
 
         print("total MMC4 samples", sum(n_samples))  # 10,881,869
 
-        rank = training_args.process_index # int(os.environ["RANK"])
-        world_size = training_args.world_size # int(os.environ["WORLD_SIZE"])
+        rank = training_args.process_index  # int(os.environ["RANK"])
+        world_size = training_args.world_size  # int(os.environ["WORLD_SIZE"])
         shared_size = n_shards // world_size
 
-        gpu_samples = [
-            sum(n_samples[i * shared_size : (i + 1) * shared_size])
-            for i in range(world_size)
-        ]
+        gpu_samples = [sum(n_samples[i * shared_size : (i + 1) * shared_size]) for i in range(world_size)]
         self.n_samples = min(gpu_samples) * world_size  # total size
         self.idx_offset = rank * min(gpu_samples)
         shard_start, shard_end = rank * shared_size, (rank + 1) * shared_size
@@ -631,7 +906,7 @@ def __init__(
 
         self.tokenizer = tokenizer
         self.data_args = data_args
-        self.image_folfer = image_folfer
+        self.image_folder = image_folder
 
         self.image_following_text_only = image_following_text_only
         self.text_only = text_only
@@ -639,7 +914,7 @@ def __init__(
     def __len__(self):
         # return len(self.data_list)
         return self.n_samples
-    
+
     @property
     def modality_lengths(self):
         # Estimate the number of tokens after tokenization, used for length-grouped sampling
@@ -647,7 +922,7 @@ def modality_lengths(self):
         for info in self.data_list:
             num_images = min(6, len(info["image_info"]))
             sentences = [info["text_list"][x["matched_text_index"]] for x in info["image_info"][:num_images]]
-            # The unit of cur_len is "words". We assume 1 word = 2 tokens. 
+            # The unit of cur_len is "words". We assume 1 word = 2 tokens.
             cur_len = num_images * self.num_image_tokens // 2 + sum([len(x) for x in sentences])
             length_list.append(cur_len)
         return length_list
@@ -707,10 +982,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
 
         if len(images) > 0:
             images = torch.stack(
-                [
-                    LazySupervisedDataset._process_image(image, self.data_args, self.image_folfer)
-                    for image in images
-                ]
+                [process_image(image, self.data_args, self.image_folder) for image in images]
             )
 
             # the same size for all images, so we concat
@@ -737,11 +1009,10 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         if input_ids[-1] == IMAGE_TOKEN_INDEX:  # need to remove one last image
             last_non_im_patch_indices = torch.where(input_ids != IMAGE_TOKEN_INDEX)[0][-1] + 1
             input_ids = input_ids[:last_non_im_patch_indices]
-        
 
         n_im_patch = (input_ids == IMAGE_TOKEN_INDEX).sum().item()
 
-        images = images[: n_im_patch]
+        images = images[:n_im_patch]
         assert len(images) == n_im_patch, print(text, input_ids)
 
         targets = input_ids.clone()
@@ -749,23 +1020,16 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         if self.image_following_text_only:  # keep only text after leading image token
             # remove loss for any token before the first <image> token
             label_idx = 0
-            while (
-                label_idx < targets.shape[-1] and targets[label_idx] != IMAGE_TOKEN_INDEX
-            ):
+            while label_idx < targets.shape[-1] and targets[label_idx] != IMAGE_TOKEN_INDEX:
                 targets[label_idx] = IGNORE_INDEX
                 label_idx += 1
 
-            pad_token = self.tokenizer.convert_tokens_to_ids(
-                [self.tokenizer.pad_token]
-            )[0]
+            pad_token = self.tokenizer.convert_tokens_to_ids([self.tokenizer.pad_token])[0]
 
             pad_token_idxs = torch.where(targets == pad_token)[0]
             for pad_token_idx in pad_token_idxs:
                 token_idx = pad_token_idx + 1
-                while (
-                    token_idx < targets.shape[-1]
-                    and targets[token_idx] != IMAGE_TOKEN_INDEX
-                ):
+                while token_idx < targets.shape[-1] and targets[token_idx] != IMAGE_TOKEN_INDEX:
                     targets[token_idx] = IGNORE_INDEX
                     token_idx += 1
             # do not train on padding tokens
@@ -781,12 +1045,13 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
 class LazyCoyoDataset(Dataset):
     """Dataset for supervised fine-tuning.
     This class is implemented by Ji Lin and Haotian Tang."""
+
     num_image_tokens = 576
 
     def __init__(
         self,
         data_path: str,
-        image_folfer: str,
+        image_folder: str,
         tokenizer: transformers.PreTrainedTokenizer,
         data_args: DataArguments,
         training_args: TrainingArguments,
@@ -801,27 +1066,20 @@ def __init__(
         # actually shards and stats info
         n_shards = len(os.listdir(data_path)) // 2
         # n_shards = 100
-        count_info_list = sorted(
-            [f for f in os.listdir(data_path) if f.endswith(".count")]
-        )[:n_shards]
-        n_samples = [
-            int(open(os.path.join(data_path, f), "r").read().strip())
-            for f in count_info_list
-        ]
+        count_info_list = sorted([f for f in os.listdir(data_path) if f.endswith(".count")])[:n_shards]
+        n_samples = [int(open(os.path.join(data_path, f), "r").read().strip()) for f in count_info_list]
 
         print("total COYO samples", sum(n_samples))
 
-        rank = training_args.process_index # int(os.environ["RANK"])
-        world_size = training_args.world_size # int(os.environ["WORLD_SIZE"])
+        rank = training_args.process_index  # int(os.environ["RANK"])
+        world_size = training_args.world_size  # int(os.environ["WORLD_SIZE"])
         shared_size = n_shards // world_size
 
         gpu_samples = [
-            sum(n_samples[i * shared_size : (i + 1) * shared_size]) // n_samples_per_idx
-            for i in range(world_size)
+            sum(n_samples[i * shared_size : (i + 1) * shared_size]) // n_samples_per_idx for i in range(world_size)
         ]
         self.n_samples = min(gpu_samples) * world_size  # total size
         self.idx_offset = rank * min(gpu_samples)
-        
 
         shard_start, shard_end = rank * shared_size, (rank + 1) * shared_size
         print(f" * loading data from shard {shard_start}-{shard_end}")
@@ -845,8 +1103,7 @@ def __init__(
         # now pack the samples into groups
         n_groups = len(full_data_list) // n_samples_per_idx
         full_data_list = [
-            full_data_list[i : i + n_samples_per_idx]
-            for i in range(0, len(full_data_list), n_samples_per_idx)
+            full_data_list[i : i + n_samples_per_idx] for i in range(0, len(full_data_list), n_samples_per_idx)
         ]
         if len(full_data_list[-1]) < n_samples_per_idx:
             full_data_list = full_data_list[:-1]
@@ -857,7 +1114,7 @@ def __init__(
 
         self.tokenizer = tokenizer
         self.data_args = data_args
-        self.image_folfer = image_folfer
+        self.image_folder = image_folder
 
     def __len__(self):
         # return len(self.data_list)
@@ -868,8 +1125,8 @@ def modality_lengths(self):
         # Estimate the number of tokens after tokenization, used for length-grouped sampling
         length_list = []
         for samples in self.data_list:
-            cur_len = sum([len(conv['text' if 'text' in conv else 'caption'].split()) for conv in samples])
-            # The unit of cur_len is "words". We assume 1 word = 2 tokens. 
+            cur_len = sum([len(conv["text" if "text" in conv else "caption"].split()) for conv in samples])
+            # The unit of cur_len is "words". We assume 1 word = 2 tokens.
             cur_len = cur_len + len(samples) * self.num_image_tokens // 2
             length_list.append(cur_len)
         return length_list
@@ -882,13 +1139,13 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         image_list = []
 
         for sample in info_list:
-            caption_key = "text" if "text" in sample else "caption"# kentang-mit@: remove existing <image> tokens in the sentences
+            caption_key = (
+                "text" if "text" in sample else "caption"
+            )  # kentang-mit@: remove existing <image> tokens in the sentences
             # kentang-mit@: remove existing <image> token.
             # if this is an html tag, we still preserve its semantic meaning
             sample[caption_key] = sample[caption_key].replace("<image>", "<IMAGE>")
-            text_list.append(
-                DEFAULT_IMAGE_TOKEN + sample[caption_key] + self.tokenizer.eos_token
-            )
+            text_list.append(DEFAULT_IMAGE_TOKEN + sample[caption_key] + self.tokenizer.eos_token)
             if "image" in sample:
                 image_base64 = sample["image"]
                 rawbytes = base64.b64decode(image_base64)
@@ -898,10 +1155,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             image_list.append(image)
 
         image_list = torch.stack(
-            [
-                LazySupervisedDataset._process_image(image, self.data_args, self.image_folfer)
-                for image in image_list
-            ]
+            [process_image(image, self.data_args, self.image_folder) for image in image_list]
         )
 
         # the same size for all images, so we concat
@@ -934,7 +1188,6 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             input_ids = input_ids[0]
 
         else:
-            
             input_ids = [
                 tokenizer_image_token(
                     prompt,
@@ -944,11 +1197,10 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
                 for prompt in text_list
             ]
             # print([x.shape[0] for x in input_ids], [len(x.split()) for x in text_list], [len(re.findall(r"<image[^>]*>", x)) for x in text_list])
-            
+
             # input_ids = torch.nn.utils.rnn.pad_sequence(
             #     input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
             # )
-        
 
         targets = copy.deepcopy(input_ids)
         # mask image tokens is unnecessary for llava-1.5
@@ -961,7 +1213,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
 
 class LazyWDSDataset(Dataset):
     """Dataset for supervised fine-tuning.
-    This class is implemented by Ligeng Zhu."""
+    This class is implemented by Ji Lin and Ligeng Zhu."""
 
     def __init__(
         self,
@@ -980,23 +1232,17 @@ def __init__(
                 n_samples.append(info["successes"])
 
         print(f"[DEBUG] {data_path} total samples", sum(n_samples))  # 10,881,869
-
-        rank = training_args.process_index # int(os.environ["RANK"])
-        world_size = training_args.world_size # int(os.environ["WORLD_SIZE"])
+        rank = training_args.process_index  # int(os.environ["RANK"])
+        world_size = training_args.world_size  # int(os.environ["WORLD_SIZE"])
         shared_size = n_shards // world_size
 
-        gpu_samples = [
-            sum(n_samples[i * shared_size : (i + 1) * shared_size])
-            for i in range(world_size)
-        ]
+        gpu_samples = [sum(n_samples[i * shared_size : (i + 1) * shared_size]) for i in range(world_size)]
         self.n_samples = min(gpu_samples) * world_size  # total size
         self.idx_offset = rank * min(gpu_samples)
         shard_start, shard_end = rank * shared_size, (rank + 1) * shared_size
         print(f" * loading data from shard {shard_start}-{shard_end}")
 
-        tar_list = [
-            f"{shard_idx:05d}.tar" for shard_idx in range(shard_start, shard_end)
-        ]
+        tar_list = [f"{shard_idx:05d}.tar" for shard_idx in range(shard_start, shard_end)]
 
         self.data_list = []
         t1 = time.time()
@@ -1041,9 +1287,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         # one example of sources
         # [{'id': 'GCC_train_001738742', 'image': 'GCC_train_001738742.jpg', 'conversations': [{'from': 'human', 'value': 'Provide a brief description of the given image.\n<image>'}, {'from': 'gpt', 'value': 'a sketch of an ostrich'}]}]
         if "image" in sources[0]:
-            image = LazySupervisedDataset._process_image(
-                sources[0]["image"], self.data_args, self.image_folder
-            )
+            image = process_image(sources[0]["image"], self.data_args, self.image_folder)
             image = torch.unsqueeze(image, dim=0)
             # now random pick some context samples for training
             if hasattr(self.data_args, "num_shots"):
@@ -1055,16 +1299,13 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         data_dict = preprocess([sources[0]["conversations"]], self.tokenizer, has_image=True)
 
         if isinstance(i, int):
-            data_dict = dict(
-                input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0]
-            )
+            data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
 
         # image exist in the data
         if image is not None:
             data_dict["image"] = image
         else:
             raise NotImplementedError
-        
 
         return data_dict
 
@@ -1088,7 +1329,7 @@ def __init__(
 
         logging.warning("Loading data...")
         pkl_list = os.listdir(data_path)
-        
+
         self.sharded = False
         # The original unsharded implementation loads the entire vflan dataset
         # on each GPU. So 80x8=640G host memory per device.
@@ -1110,24 +1351,16 @@ def __init__(
             n_samples = []
             # actually shards and stats info
             n_shards = len(os.listdir(data_path)) // 2
-            count_info_list = sorted(
-                [f for f in os.listdir(data_path) if f.endswith(".count")]
-            )[:n_shards]
-            n_samples = [
-                int(open(os.path.join(data_path, f), "r").read().strip())
-                for f in count_info_list
-            ]
+            count_info_list = sorted([f for f in os.listdir(data_path) if f.endswith(".count")])[:n_shards]
+            n_samples = [int(open(os.path.join(data_path, f), "r").read().strip()) for f in count_info_list]
             self.n_samples = sum(n_samples)
             print("total VFlan samples", sum(n_samples))  # 10,881,869
 
-            rank = training_args.process_index # int(os.environ["RANK"])
-            world_size = training_args.world_size # int(os.environ["WORLD_SIZE"])
+            rank = training_args.process_index  # int(os.environ["RANK"])
+            world_size = training_args.world_size  # int(os.environ["WORLD_SIZE"])
             shared_size = n_shards // world_size
 
-            gpu_samples = [
-                sum(n_samples[i * shared_size : (i + 1) * shared_size])
-                for i in range(world_size)
-            ]
+            gpu_samples = [sum(n_samples[i * shared_size : (i + 1) * shared_size]) for i in range(world_size)]
             self.n_samples = min(gpu_samples) * world_size  # total size
             self.idx_offset = rank * min(gpu_samples)
             shard_start, shard_end = rank * shared_size, (rank + 1) * shared_size
@@ -1180,12 +1413,9 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             else:  # jpeg bytes
                 rawbytes = base64.b64decode(image_str)
                 decode_images.append(Image.open(io.BytesIO(rawbytes)).convert("RGB"))
-        if n_images == 8:
-            resize = True
-        else:
-            resize = False
+
         images = [
-            LazySupervisedDataset._process_image(img, self.data_args, resize=resize, image_folder=self.image_folder)
+            process_image(img, self.data_args, image_folder=self.image_folder)
             for img in decode_images
         ]
 
@@ -1194,9 +1424,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         #     raise NotImplementedError  # do not support multi-shot for FLAN
 
         # let's make sure there is no <image> in the question...
-        if (
-            "Image Descriptions" in question
-        ):  # NOTE: specicial handlement for generation_visual-dialog_train.pkl
+        if "Image Descriptions" in question:  # NOTE: specicial handlement for generation_visual-dialog_train.pkl
             question_split = question.split("\nQuestion: ")[1:]
             qa_pairs = []
             for qa in question_split:
@@ -1211,11 +1439,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
                 conversation.append({"from": "human", "value": q})
                 conversation.append({"from": "gpt", "value": a})
         else:
-            question = (
-                question.replace("<image>\n", "")
-                .replace("\n<image>", "")
-                .replace("<image>", "")
-            )
+            question = question.replace("<image>\n", "").replace("\n<image>", "").replace("<image>", "")
             question = "<image>\n" * n_images + question
             conversation = [
                 {"from": "human", "value": question},
@@ -1225,7 +1449,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         # the same size for all images, so we concat
         if len(images) == 0:
             assert not "<image>" in question
-        
+
         # sources = replace_image_patch_tokens([conversation], self.multimodal_cfg)
         sources = [conversation]
 
@@ -1248,9 +1472,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         )
 
         if isinstance(i, int):
-            data_dict = dict(
-                input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0]
-            )
+            data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
 
         if len(images) > 0:
             data_dict["image"] = torch.stack(images)
@@ -1260,7 +1482,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             # data_dict['image'] = torch.zeros(3, crop_size['height'], crop_size['width'])
             # data_dict['image'] = data_dict['image'].unsqueeze(0)
             # vila way of handling text-only data
-            data_dict['image'] = None
+            data_dict["image"] = None
 
         return data_dict
 
@@ -1272,20 +1494,21 @@ class LazyCCSWebDataset(Dataset):
     def __init__(
         self,
         data_path: str,
+        image_folder: str,
         tokenizer: transformers.PreTrainedTokenizer,
         data_args: DataArguments,
         training_args: TrainingArguments,
     ):
         super().__init__()
         t1 = time.time()
-        
-        from llava.data.vila_webdataset import VILAWebDataset
-        
+
+        from llava.data.simple_vila_webdataset import VILAWebDataset
+
         print("[DEBUG] ", osp.abspath(data_path))
         self.dataset = VILAWebDataset(
-            data_path = osp.abspath(data_path),
+            data_path=osp.abspath(data_path)
         )
-        
+
         t2 = time.time()
         print("Loading done. Total time: {:.2f} seconds".format(t2 - t1))
 
@@ -1313,7 +1536,16 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             print(info.keys())
             print(info)
             raise KeyError
-        
+
+        caption = caption.replace("<image>", "<IMAGE>")
+        if isinstance(image_path, io.BytesIO):
+            image_path = Image.open(image_path).convert("RGB")
+
+        if not isinstance(image_path, PIL.Image.Image):
+            print(image_path)
+            print(info.keys())
+            print(type(image_path))
+            raise NotImplementedError
 
         rand_prompt = "<image>\n"
         sources = [
@@ -1329,9 +1561,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         # one example of sources
         # [{'id': 'GCC_train_001738742', 'image': 'GCC_train_001738742.jpg', 'conversations': [{'from': 'human', 'value': 'Provide a brief description of the given image.\n<image>'}, {'from': 'gpt', 'value': 'a sketch of an ostrich'}]}]
         if "image" in sources[0]:
-            image = LazySupervisedDataset._process_image(
-                sources[0]["image"], self.data_args
-            )
+            image = process_image(sources[0]["image"], self.data_args, image_folder=None)
             image = torch.unsqueeze(image, dim=0)
             # now random pick some context samples for training
             if hasattr(self.data_args, "num_shots"):
@@ -1343,29 +1573,82 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         data_dict = preprocess([sources[0]["conversations"]], self.tokenizer, has_image=True)
 
         if isinstance(i, int):
-            data_dict = dict(
-                input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0]
-            )
+            data_dict = dict(input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0])
 
         # image exist in the data
         if image is not None:
             data_dict["image"] = image
         else:
             raise NotImplementedError
-        
 
         return data_dict
 
 
+from functools import lru_cache
+
+
+@lru_cache(maxsize=16)
+def lru_json_load(fpath):
+    return json.load(open(fpath, "r"))
+
+
+class LazyEvaluateDataset(LazySupervisedDataset):
+    def __init__(
+        self,
+        data_path: str,
+        data_args: dict,
+        tokenizer: PreTrainedTokenizer,
+        config_path: str = "llava/eval/mmmu_utils/configs/llava1.5.yaml",
+        split="validation",
+        **kwargs,
+    ):
+        # run for each subject
+        sub_dataset_list = []
+        for subject in CAT_SHORT2LONG.values():
+            sub_dataset = load_dataset(data_path, subject, split=split)
+            sub_dataset_list.append(sub_dataset)
+
+        all_datasets = concatenate_datasets(sub_dataset_list)
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+        self.image_folder = None
+        self.config = self.get_config(config_path)
+        self.list_data_dict = self.get_processed_prompt(all_datasets)
+
+    def get_config(self, config_path: str) -> str:
+        config = load_yaml(config_path)
+        for key, value in config.items():
+            if key != "eval_params" and type(value) == list:
+                assert len(value) == 1, "key {} has more than one value".format(key)
+                config[key] = value[0]
+        return config
+
+    def get_processed_prompt(self, dataset: list) -> list:
+        processed_dataset = []
+        for d in dataset:
+            sample = process_single_sample(d)
+            processed_dict = construct_prompt(sample, self.config)
+
+            if '<image>' in processed_dict["gt_content"]:
+                processed_dict["gt_content"] = processed_dict["gt_content"].replace('<image>', 'image')
+            sample["conversations"] = [
+                {"from": "human", "value": processed_dict["final_input_prompt"]},
+                {"from": "gpt", "value": processed_dict["gt_content"]},
+            ]
+            processed_dataset.append(sample)
+        return processed_dataset
+
 
 class LazyCoyoWebDataset(Dataset):
     """Dataset for supervised fine-tuning.
     This class is implemented by Ligeng Zhu."""
+
     num_image_tokens = 576
 
     def __init__(
         self,
         data_path: str,
+        image_folder: str,
         tokenizer: transformers.PreTrainedTokenizer,
         data_args: DataArguments,
         training_args: TrainingArguments,
@@ -1374,18 +1657,19 @@ def __init__(
     ):
         super().__init__()
 
-        from llava.data.vila_webdataset import VILAWebDataset
-        
+        from llava.data.simple_vila_webdataset import VILAWebDataset
+
         print("[DEBUG] ", osp.abspath(data_path))
         self.dataset = VILAWebDataset(
-            data_path = osp.abspath(data_path),
+            data_path=osp.abspath(data_path),
+            meta_path=data_args.meta_path
         )
 
         # None: use original caption
         # Folder path: use original caption
-        self.caption_chocie = None
+        self.caption_choice = None
         self.data_path = data_path
-        
+
         print("total samples", len(self.dataset))
         rank = int(os.environ["RANK"])
         world_size = int(os.environ["WORLD_SIZE"])
@@ -1403,8 +1687,8 @@ def modality_lengths(self):
         # Estimate the number of tokens after tokenization, used for length-grouped sampling
         length_list = []
         for samples in self.data_list:
-            cur_len = sum([len(conv['text' if 'text' in conv else 'caption'].split()) for conv in samples])
-            # The unit of cur_len is "words". We assume 1 word = 2 tokens. 
+            cur_len = sum([len(conv["text" if "text" in conv else "caption"].split()) for conv in samples])
+            # The unit of cur_len is "words". We assume 1 word = 2 tokens.
             cur_len = cur_len + len(samples) * self.num_image_tokens // 2
             length_list.append(cur_len)
         return length_list
@@ -1412,13 +1696,16 @@ def modality_lengths(self):
     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         CONCAT_SAMPLES = False
         # info_list = self.dataset[i - self.idx_offset]
-        
-        begin_idx, end_idx = i * self.n_samples_per_idx, (i + 1) * self.n_samples_per_idx
+
+        begin_idx, end_idx = (
+            i * self.n_samples_per_idx,
+            (i + 1) * self.n_samples_per_idx,
+        )
         end_idx = min(end_idx, len(self.dataset))
 
         text_list = []
         image_list = []
-        
+
         for idx in range(begin_idx, end_idx):
             info = self.dataset[idx]
             if ".jpg" in info:
@@ -1435,50 +1722,38 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
                 print(info.keys())
                 print(info)
                 raise KeyError
-            
+
+            if self.caption_choice is not None:
+                # load new captions
+                shard = info["__shard__"]
+                url = info[".json"]["url"]
+                tar_name = osp.relpath(osp.realpath(shard), osp.realpath(self.data_path))
+                # tar_name = osp.dirname(shard)
+                shard_json_path = osp.join(self.caption_choice, tar_name + ".json")
+                shard_json = lru_json_load(shard_json_path)
+                # print("DEBUG:", shard, self.data_path, tar_name)
+                try:
+                    caption = shard_json[url]["output"]
+                except KeyError:
+                    print(f"{url} not in caption. fallback to original caption temporarially")
+
             caption = caption.replace("<image>", "<IMAGE>")
-            text_list.append(
-                DEFAULT_IMAGE_TOKEN + caption + self.tokenizer.eos_token
-            )
-            image_list.append(image_path)            
-            
-        # for sample in info_list:
-        #     caption_key = "text" if "text" in sample else "caption"# kentang-mit@: remove existing <image> tokens in the sentences
-        #     # kentang-mit@: remove existing <image> token.
-        #     # if this is an html tag, we still preserve its semantic meaning
-        #     sample[caption_key] = sample[caption_key].replace("<image>", "<IMAGE>")
-        #     text_list.append(
-        #         DEFAULT_IMAGE_TOKEN + sample[caption_key] + self.tokenizer.eos_token
-        #     )
-        #     if "image" in sample:
-        #         image_base64 = sample["image"]
-        #         rawbytes = base64.b64decode(image_base64)
-        #     else:
-        #         rawbytes = sample["rawbytes"]
-        #     image = Image.open(io.BytesIO(rawbytes)).convert("RGB")
-        #     image_list.append(image)
+            text_list.append(DEFAULT_IMAGE_TOKEN + caption + self.tokenizer.eos_token)
 
-        image_list = torch.stack(
-            [
-                LazySupervisedDataset._process_image(image, self.data_args)
-                for image in image_list
-            ]
-        )
+            if isinstance(image_path, io.BytesIO):
+                image_path = Image.open(image_path).convert("RGB")
 
-        # the same size for all images, so we concat
-        # cur_token_len = (
-        #     image_list[0].shape[-2] // self.multimodal_cfg["patch_size"]
-        # ) * (image_list[0].shape[-1] // self.multimodal_cfg["patch_size"])
-        # cur_token_len += self.multimodal_cfg["n_extra_patch"]
+            if not isinstance(image_path, PIL.Image.Image):
+                print(image_path)
+                print(info.keys())
+                print(type(image_path))
+                raise NotImplementedError
 
-        # replace_token = DEFAULT_IMAGE_TOKEN
-        # if self.multimodal_cfg["use_im_start_end"]:
-        #     replace_token = (
-        #         DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
-        #     )
-        # text_list = [
-        #     text.replace(DEFAULT_IMAGE_TOKEN, replace_token) for text in text_list
-        # ]
+            image_list.append(image_path)
+
+        image_list = torch.stack(
+            [process_image(image, self.data_args, image_folder=None) for image in image_list]
+        )
 
         if CONCAT_SAMPLES:
             # into <image>cap<eos><image>cap<eos>...
@@ -1493,9 +1768,7 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             ).input_ids  # 4, seq_len
 
             input_ids = input_ids[0]
-
         else:
-            
             input_ids = [
                 tokenizer_image_token(
                     prompt,
@@ -1504,12 +1777,6 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
                 )
                 for prompt in text_list
             ]
-            # print([x.shape[0] for x in input_ids], [len(x.split()) for x in text_list], [len(re.findall(r"<image[^>]*>", x)) for x in text_list])
-            
-            # input_ids = torch.nn.utils.rnn.pad_sequence(
-            #     input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
-            # )
-        
 
         targets = copy.deepcopy(input_ids)
         # mask image tokens is unnecessary for llava-1.5
@@ -1518,7 +1785,93 @@ def __getitem__(self, i) -> Dict[str, torch.Tensor]:
             targets[i][targets[i] == self.tokenizer.pad_token_id] = IGNORE_INDEX
 
         return dict(input_ids=input_ids, labels=targets, image=image_list)
-    
+
+
+class LazyVideoWebDataset(Dataset):
+    """Dataset for supervised fine-tuning."""
+
+    def __init__(
+        self,
+        data_path: str,
+        image_folder: str,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+        # cache_path: str,
+        # n_samples_per_idx=4,
+    ):
+        super().__init__()
+
+        from llava.data.simple_video_dataset import SimpleVideoDataset
+
+        self.dataset = SimpleVideoDataset(
+            data_path=osp.abspath(data_path),
+            cache_dir=f"{osp.abspath(data_path)}-webds-meta",
+            # cache_dir=cache_path,
+        )
+
+        # None: use original caption
+        # Folder path: use original caption
+        self.caption_chocie = None
+        self.data_path = data_path
+
+        print("total samples", len(self.dataset))
+        # InternVid: TODO
+        rank = int(os.environ["RANK"]) if "RANK" in os.environ else 2
+        world_size = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 32
+
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+
+    def __len__(self):
+        return len(self.dataset)
+
+    @property
+    def modality_lengths(self):
+        # Estimate the number of tokens after tokenization, used for length-grouped sampling
+        length_list = []
+        for samples in self.data_list:
+            cur_len = sum([len(conv["text" if "text" in conv else "caption"].split()) for conv in samples])
+            # The unit of cur_len is "words". We assume 1 word = 2 tokens.
+            cur_len = cur_len + len(samples) * self.num_image_tokens // 2
+            length_list.append(cur_len)
+        return length_list
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        ADD_TEXT_PROMPT = False
+        num_video_frames = self.data_args.num_video_frames
+
+        info = self.dataset[i]
+        
+        # print(info)
+        if ".mp4" in info:
+            caption, video_path = info[".txt"], info[".mp4"]
+        else:
+            video_path = None
+            caption = "Empty video."
+
+        images, video_loading_succeed = LazySupervisedDataset._load_video(video_path, num_video_frames, self.data_args)
+        
+        if not video_loading_succeed:
+            caption = "Empty video."
+
+        prompt = "<image>\n" * num_video_frames + caption
+
+
+        image_tensor = torch.stack(
+            [process_image(image, self.data_args, None) for image in images]
+        )
+
+        input_ids = tokenizer_image_token(
+            prompt,
+            self.tokenizer,
+            return_tensors="pt",
+        )
+        targets = copy.deepcopy(input_ids)
+        data_dict = dict(input_ids=input_ids, labels=targets, image=image_tensor)
+
+        return data_dict
+
 
 @dataclass
 class DataCollatorForSupervisedDataset(object):
@@ -1542,8 +1895,8 @@ def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
                 labels.append(instance["labels"])
             else:
                 labels += instance["labels"]
-            # Note (kentang-mit@: we do not directly push tensors to 
-            # images, but list of tensors. 
+            # Note (kentang-mit@: we do not directly push tensors to
+            # images, but list of tensors.
             if instance["image"] is not None:
                 cur_image = instance["image"]
                 assert len(cur_image.shape) == 4
@@ -1559,17 +1912,19 @@ def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
         # kentang-mit@: we need to make sure these two lists have
         # the same length. We will use input_ids to filter out images corresponding
         # to truncated <image> tokens later.
-        assert len(images) == len(input_ids)
+        for _images, _input_ids in zip(images, input_ids):
+            assert (
+                len(_images) == (_input_ids == IMAGE_TOKEN_INDEX).sum().item()
+            ), f"Number mismatch between images and placeholder image tokens in 'len(_images) == (_input_ids == IMAGE_TOKEN_INDEX).sum().item()'.\
+                Expect to have {len(_images)} images but only found {(_input_ids == IMAGE_TOKEN_INDEX).sum().item()} images in tokens. \
+                Error input_ids: {_input_ids} {self.tokenizer.decode([x if x != -200 else 200 for x in _input_ids])}"
 
         input_ids = torch.nn.utils.rnn.pad_sequence(
-            input_ids,
-            batch_first=True,
-            padding_value=self.tokenizer.pad_token_id)
-        labels = torch.nn.utils.rnn.pad_sequence(labels,
-                                                 batch_first=True,
-                                                 padding_value=IGNORE_INDEX)
-        input_ids = input_ids[:, :self.tokenizer.model_max_length]
-        labels = labels[:, :self.tokenizer.model_max_length]
+            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
+        )
+        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
+        input_ids = input_ids[:, : self.tokenizer.model_max_length]
+        labels = labels[:, : self.tokenizer.model_max_length]
         batch = dict(
             input_ids=input_ids,
             labels=labels,
@@ -1600,16 +1955,44 @@ def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
         return batch
 
 
-def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
-                                data_args: DataArguments,
-                                training_args: TrainingArguments) -> Dict:
+def make_supervised_data_module(
+    tokenizer: PreTrainedTokenizer,
+    data_args: DataArguments,
+    training_args: TrainingArguments,
+) -> Dict:
     """Make dataset and collator for supervised fine-tuning.
-    This function is originally implemented by the LLaVA team and 
-    modified by Jason Lu and Haotian Tang."""
+    This function is originally implemented by the LLaVA team and
+    modified by Jason Lu, Haotian Tang and Ligeng Zhu."""
+    datasets_mixture.register_datasets_mixtures()
+    train_dataset = build_datasets(data_args, training_args=training_args, tokenizer=tokenizer, split="train")
+    eval_dataset = build_datasets(data_args, training_args=training_args, tokenizer=tokenizer, split="eval")
+    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer, data_args=data_args)
+    return dict(
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        data_collator=data_collator,
+    )
+
+
+def build_datasets(
+    data_args: DataArguments,
+    training_args: TrainingArguments,
+    tokenizer: PreTrainedTokenizer,
+    split: str = "train",
+) -> None:
     all_datasets = []
     extra_info = []
-    datasets_mixture.register_datasets_mixtures()
-    mixture = datasets_mixture.DATASETS_MIXTURES[data_args.data_mixture]
+    # mixture = datasets_mixture.DATASETS_MIXTURES[data_args.data_mixture]
+    try:
+        ## keep the name 'data_mixture' for development FIXME
+        # mixture_names = getattr(data_args, f"{split}_data_mixture").strip().split("+")
+        attr_name = "data_mixture" if split == "train" else "eval_data_mixture"
+        mixture_names = getattr(data_args, attr_name).strip().split("+")
+    except:
+        logging.warning(f"Pay attention, split {split} is not built...")
+        return None
+    mixture = (DATASETS[_] for _ in mixture_names)
+    print(f"[Dataset-INFO]: Loading from {mixture_names}")
     image_folder = None
     for dataset in mixture:
         dataset_type = dataset.dataset_type
@@ -1624,28 +2007,62 @@ def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
             dataset_cls = LazyMMC4Dataset
         elif dataset_type == "coyo":
             dataset_cls = LazyCoyoDataset
+        elif dataset_type == "sam-wds":
+            print("dataset.py: Loading SAM class")
+            from llava.data.dataset_impl.sam import LazySAMWebDataset
+
+            dataset_cls = LazySAMWebDataset
         elif dataset_type == "coyo-wds":
-            print("dataset.py: Loading LazyCoyoWebDataset class")
             dataset_cls = LazyCoyoWebDataset
+        elif dataset_type == "coyo-wds-recap":
+            print("dataset.py: Loading coyo-wds-recap class")
+            from llava.data.dataset_impl.coyo_recap import \
+                LazyCoyoWebRecapDataset
+
+            dataset_cls = LazyCoyoWebRecapDataset
+        elif dataset_type == "textocr":
+            print("dataset.py: Loading textocr class")
+            from llava.data.dataset_impl.textocr import VILATextOCR
+
+            dataset_cls = VILATextOCR
+        elif dataset_type == "hiertext":
+            print("dataset.py: Loading hiertext class")
+            from llava.data.dataset_impl.hiertext import VILAHierText
+
+            dataset_cls = VILAHierText
+        elif dataset_type == "panda70m":
+            print("dataset.py: Loading VILAPanda70m class")
+            from llava.data.dataset_impl.panda70m import VILAPanda70m
+
+            dataset_cls = VILAPanda70m
         elif dataset_type == "ccs-wds":
-            print("dataset.py: Loading LazyCCSWebDataset class")
             dataset_cls = LazyCCSWebDataset
         elif dataset_type == "vflan":
             dataset_cls = LazyVFlanDataset
+        elif dataset_type == "video-wds":
+            dataset_cls = LazyVideoWebDataset
+        elif dataset_type == "evaluation":
+            dataset_cls = LazyEvaluateDataset
+        elif dataset_type == "dummy":
+            dataset_cls = DummyDataset
+            if hasattr(dataset, "image_path"):
+                image_folder = dataset.image_path
         else:
-            raise NotImplementedError
-        train_dataset = dataset_cls(tokenizer=tokenizer,
-                                data_path=dataset.data_path,
-                                image_folder=image_folder,
-                                data_args=data_args,
-                                training_args=training_args,)
-        all_datasets.append(train_dataset)
-        extra_info.append(len(train_dataset))
-    
-    all_datasets = ConcatDataset(all_datasets)
-    training_args.sample_lens = extra_info
+            raise NotImplementedError(f"{dataset_type} is not supported.")
+        data_args.meta_path = getattr(dataset, "meta_path", None)
+        dataset = dataset_cls(
+            tokenizer=tokenizer,
+            data_path=dataset.data_path,
+            image_folder=image_folder,
+            data_args=data_args,
+            training_args=training_args,
+        )
+        all_datasets.append(dataset)
+        extra_info.append(len(dataset))
 
-    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer, data_args=data_args)
-    return dict(train_dataset=all_datasets,
-                eval_dataset=None,
-                data_collator=data_collator)
+    all_datasets = ConcatDataset(all_datasets)
+    if split == "train":
+        training_args.sample_lens = extra_info
+    elif split == "eval":
+        training_args.eval_sample_lens = extra_info
+    return all_datasets
diff --git a/llava/data/dataset_impl/coyo_recap.py b/llava/data/dataset_impl/coyo_recap.py
new file mode 100644
index 00000000..9f16e63e
--- /dev/null
+++ b/llava/data/dataset_impl/coyo_recap.py
@@ -0,0 +1,76 @@
+import os, os.path as osp
+import base64
+import copy
+import llava.data.datasets_mixture as datasets_mixture
+
+import PIL
+from llava.data.datasets_mixture import DATASETS
+from dataclasses import dataclass, field
+import io
+import numpy as np
+import random
+import json
+import logging
+import pathlib
+import pickle
+import time
+from typing import Dict, Optional, Sequence, List
+import re
+
+import torch
+
+import transformers
+
+from llava.constants import (
+    IGNORE_INDEX,
+    IMAGE_TOKEN_INDEX,
+    DEFAULT_IMAGE_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN,
+)
+from torch.utils.data import ConcatDataset, Dataset
+from llava.train.llava_trainer import LLaVATrainer
+from llava.train.args import TrainingArguments, DataArguments
+
+from llava import conversation as conversation_lib
+from llava.model import *
+from llava.mm_utils import tokenizer_image_token, is_gemma_tokenizer
+
+from torchvision.transforms import Resize
+from pytorchvideo.data.encoded_video import EncodedVideo
+
+from PIL import Image
+from functools import lru_cache
+
+from llava.data.simple_vila_webdataset import VILAWebDataset
+from llava.data.dataset import LazySupervisedDataset
+
+@lru_cache(maxsize=16)
+def lru_json_load(fpath):
+    return json.load(open(fpath, "r"))
+
+from llava.data.dataset import LazyCoyoWebDataset
+class LazyCoyoWebRecapDataset(LazyCoyoWebDataset):
+    """Dataset for supervised fine-tuning.
+    This class is implemented by Ligeng Zhu."""
+
+    num_image_tokens = 576
+
+    def __init__(
+        self,
+        data_path: str,
+        image_folder: str,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+        n_samples_per_idx=4,
+    ):
+        super().__init__(
+            data_path=data_path, 
+            image_folder=image_folder,
+            tokenizer=tokenizer,
+            data_args=data_args,
+            training_args=training_args,
+            n_samples_per_idx=n_samples_per_idx
+        )
+        self.caption_choice = "/home/ligengz/workspace/coyo-25m-recap"
\ No newline at end of file
diff --git a/llava/data/dataset_impl/hiertext.py b/llava/data/dataset_impl/hiertext.py
new file mode 100644
index 00000000..3d3003be
--- /dev/null
+++ b/llava/data/dataset_impl/hiertext.py
@@ -0,0 +1,191 @@
+# refernced from https://github.com/CVC-DAG/OCR_datasets/blob/master/src/datasets/ocr/hiertext.py
+import base64
+import copy
+import io
+import json
+import logging
+import os
+import os.path
+import os.path as osp
+import pathlib
+import pickle
+import random
+import re
+import time
+from collections import defaultdict
+from dataclasses import dataclass, field
+from functools import lru_cache
+from typing import Dict, List, Optional, Sequence
+
+import numpy as np
+import PIL
+import torch
+import transformers
+from PIL import Image
+from pytorchvideo.data.encoded_video import EncodedVideo
+from torch.utils.data import ConcatDataset, Dataset
+from torchvision.transforms import Resize
+
+import llava.data.datasets_mixture as datasets_mixture
+from llava import conversation as conversation_lib
+from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                             DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
+                             IMAGE_TOKEN_INDEX)
+from llava.data.dataset_impl.textocr import GenericDataset, preprocess_OCR
+from llava.data.datasets_mixture import DATASETS
+from llava.train.args import DataArguments, TrainingArguments
+
+DEFAULT_HIERTEXT = "/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/hiertext"
+
+
+def bbx_from_vertices_list(vertices):
+    # Care about the index, potential source of errors
+    return (
+        min(vertices, key=lambda x: x[0])[0],
+        min(vertices, key=lambda x: x[1])[1],
+        max(vertices, key=lambda x: x[0])[0],
+        max(vertices, key=lambda x: x[1])[1],
+    )
+
+
+class HierTextDataset(GenericDataset):
+    name = "hiertext_dataset"
+
+    def __init__(
+        self,
+        base_folder=DEFAULT_HIERTEXT,
+        split="train",
+        handwritten=[True, False],
+        legibility=[True, False],
+        mode="words",
+    ) -> None:
+        self.split = f"{split}_legibility-{legibility}_handwritten-{handwritten}"
+
+        annotation_file = json.load(
+            open(
+                os.path.join(
+                    base_folder,
+                    "gt",
+                    "train.jsonl" if split == "train" else "validation.jsonl",
+                ),
+                "r",
+            )
+        )
+        images_path = os.path.join(base_folder, "train" if split == "train" else "validation")
+        self.base_images = images_path
+
+        self.samples = []
+        self.unique_fpath = set()
+        self.unique_samples = defaultdict(list)
+        for num, annotation in enumerate(annotation_file["annotations"]):
+            image_path = os.path.join(images_path, annotation["image_id"] + ".jpg")
+            for paragraph in annotation["paragraphs"]:
+                for line in paragraph["lines"]:
+                    x, y, x2, y2 = bbx_from_vertices_list(line["vertices"])
+
+                    if x2 * y2 < 225:
+                        # skip too small texts
+                        continue
+                    if x2 - x < y2 - y:
+                        continue  # TODO: Evaluation without vertical lines. Not fair.
+                    if line["legible"] in legibility and line["handwritten"] in handwritten and not line["vertical"]:
+                        if mode == "lines":
+                            data = {
+                                "bbx": bbx_from_vertices_list(line["vertices"]),
+                                "image_path": image_path,
+                                "transcription": line["text"],
+                                "vertical": line["vertical"],
+                            }
+                            self.samples.append(data)
+                            self.unique_samples[image_path].append(data)
+                            self.unique_fpath.add(image_path)
+                        else:
+                            for word in line["words"]:
+                                if not word["vertical"]:
+                                    data = {
+                                        "bbx": bbx_from_vertices_list(word["vertices"]),
+                                        "image_path": image_path,
+                                        "transcription": word["text"],
+                                        "vertical": word["vertical"],
+                                    }
+                                    self.samples.append(data)
+                                    self.unique_samples[image_path].append(data)
+                                    self.unique_fpath.add(image_path)
+        self.unique_fpath = list(self.unique_fpath)
+
+    def __len__(self):
+        return len(self.unique_fpath)
+
+    def __getitem__(self, idx):
+        # metadata = self.samples[idx]
+        # img_path = os.path.join(self.base_images, metadata["image_path"])
+        # image = Image.open(img_path).convert("RGB")
+
+        img_path = self.unique_fpath[idx]
+        metadatas = self.unique_samples[img_path]
+
+        annotations = []
+        for metadata in metadatas:
+            annotations.append(metadata["transcription"])
+        image = Image.open(img_path).convert("RGB")
+
+        return {
+            "image_path": img_path,
+            "origin_image": image,
+            "annotation": annotations,
+            "dataset": self.name,
+            "split": self.split,
+        }
+
+
+class VILAHierText(Dataset):
+    """
+    Dataset class for VILA OCR data.
+
+    Args:
+        data_path (str): The path to the data.
+        image_folder (str): The folder containing the images.
+        tokenizer (transformers.PreTrainedTokenizer): The tokenizer for text processing.
+        data_args (DataArguments): The data arguments.
+        training_args (TrainingArguments): The training arguments.
+        split (str, optional): The split of the dataset (default: "train").
+        min_area (float, optional): The minimum area of the text (default: 0.001).
+    """
+
+    def __init__(
+        self,
+        data_path,
+        image_folder,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+        split="train",
+        min_area=0.001,
+    ) -> None:
+        super().__init__()
+
+        data_path = osp.expanduser(data_path)
+        self.dataset = HierTextDataset(data_path, split)
+
+        self.data_path = data_path
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        meta = self.dataset[index]
+
+        img = meta["origin_image"]
+        fpath = meta["image_path"]
+        texts = " ".join(meta["annotation"])
+
+        return preprocess_OCR(image=img, texts=texts, data_args=self.data_args, tokenizer=self.tokenizer)
+
+
+if __name__ == "__main__":
+    dst = HierTextDataset()
+    print(len(dst))
+    for i in range(3):
+        print(dst[i])
diff --git a/llava/data/dataset_impl/sam.py b/llava/data/dataset_impl/sam.py
new file mode 100644
index 00000000..b00a6090
--- /dev/null
+++ b/llava/data/dataset_impl/sam.py
@@ -0,0 +1,185 @@
+import base64
+import copy
+import io
+import json
+import logging
+import os
+import os.path as osp
+import pathlib
+import pickle
+import random
+import re
+import time
+from dataclasses import dataclass, field
+from functools import lru_cache
+from typing import Dict, List, Optional, Sequence
+
+import numpy as np
+import PIL
+import torch
+import transformers
+from PIL import Image
+from pytorchvideo.data.encoded_video import EncodedVideo
+from torch.utils.data import ConcatDataset, Dataset
+from torchvision.transforms import Resize
+
+import llava.data.datasets_mixture as datasets_mixture
+from llava import conversation as conversation_lib
+from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                             DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
+                             IMAGE_TOKEN_INDEX)
+from llava.data.dataset import LazySupervisedDataset
+from llava.data.datasets_mixture import DATASETS
+from llava.data.simple_vila_webdataset import VILAWebDataset
+from llava.mm_utils import is_gemma_tokenizer, tokenizer_image_token
+from llava.model import *
+from llava.train.args import DataArguments, TrainingArguments
+from llava.train.llava_trainer import LLaVATrainer
+
+
+@lru_cache(maxsize=16)
+def lru_json_load(fpath):
+    return json.load(open(fpath, "r"))
+
+
+class LazySAMWebDataset(Dataset):
+    """Dataset for SAM high resolution data.
+    This class is implemented by Ligeng Zhu."""
+
+    num_image_tokens = 576
+
+    def __init__(
+        self,
+        data_path: str,
+        image_folder: str,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+        n_samples_per_idx=1,
+    ):
+        super().__init__()
+
+        print("[DEBUG] ", osp.abspath(data_path))
+        self.dataset = VILAWebDataset(
+            data_path=osp.abspath(data_path),
+        )
+
+        # None: use original caption
+        # Folder path: use original caption
+        # Efficient-Large-Model/sam-recap-VILA-13b
+        self.caption_choice = "/home/ligengz/workspace/sam-recap-VILA-13b"
+        self.data_path = data_path
+
+        print("total samples", len(self.dataset))
+        rank = int(os.environ["RANK"])
+        world_size = int(os.environ["WORLD_SIZE"])
+
+        self.n_samples_per_idx = n_samples_per_idx
+        # self.n_samples = len(self.dataset) // n_samples_per_idx
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+
+    def __len__(self):
+        return len(self.dataset) // self.n_samples_per_idx
+
+    @property
+    def modality_lengths(self):
+        # Estimate the number of tokens after tokenization, used for length-grouped sampling
+        length_list = []
+        for samples in self.data_list:
+            cur_len = sum([len(conv["text" if "text" in conv else "caption"].split()) for conv in samples])
+            # The unit of cur_len is "words". We assume 1 word = 2 tokens.
+            cur_len = cur_len + len(samples) * self.num_image_tokens // 2
+            length_list.append(cur_len)
+        return length_list
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        CONCAT_SAMPLES = False
+        # info_list = self.dataset[i - self.idx_offset]
+
+        begin_idx, end_idx = (
+            i * self.n_samples_per_idx,
+            (i + 1) * self.n_samples_per_idx,
+        )
+        end_idx = min(end_idx, len(self.dataset))
+
+        text_list = []
+        image_list = []
+
+        for idx in range(begin_idx, end_idx):
+            info = self.dataset[idx]
+            if ".jpg" in info:
+                image = info[".jpg"]
+            elif ".png" in info:
+                image = info[".png"]
+            elif ".webp" in info:
+                image = info[".webp"]
+            elif ".bmp" in info:
+                image = info[".bmp"]
+            elif ".tiff" in info:
+                image = info[".tiff"]
+            else:
+                print(info.keys())
+                print(info)
+                raise KeyError
+
+            assert self.caption_choice is not None
+            # load new captions
+            shard = info["__shard__"]
+            shard_key = info["__key__"].replace("./", "")
+            url = osp.join(shard, shard_key)
+
+            tar_name = osp.relpath(osp.realpath(shard), osp.realpath(self.data_path))
+            # tar_name = osp.dirname(shard)
+            shard_json_path = osp.join(self.caption_choice, tar_name + ".json")
+            shard_json = lru_json_load(shard_json_path)
+            # print("DEBUG:", shard, self.data_path, tar_name)
+            try:
+                caption = shard_json[url]["output"]
+            except KeyError:
+                print(f"{url} not in caption. fallback to original caption temporarially")
+
+            caption = caption.replace("<image>", "<IMAGE>")
+            text_list.append(DEFAULT_IMAGE_TOKEN + caption + self.tokenizer.eos_token)
+
+            if isinstance(image, io.BytesIO):
+                image = Image.open(image).convert("RGB")
+
+            if not isinstance(image, PIL.Image.Image):
+                print(image)
+                print(info.keys())
+                print(type(image))
+                raise NotImplementedError
+
+            image_list.append(image)
+
+        image_list = torch.stack(
+            [LazySupervisedDataset._process_image(image, self.data_args, image_folder=None) for image in image_list]
+        )
+
+        input_ids = [
+            tokenizer_image_token(
+                prompt,
+                self.tokenizer,
+                return_tensors="pt",
+            )
+            for prompt in text_list
+        ]
+
+        targets = copy.deepcopy(input_ids)
+        # mask image tokens is unnecessary for llava-1.5
+        # targets[targets == IMAGE_TOKEN_INDEX] = IGNORE_INDEX
+        for i in range(len(targets)):
+            targets[i][targets[i] == self.tokenizer.pad_token_id] = IGNORE_INDEX
+
+        return dict(input_ids=input_ids, labels=targets, image=image_list)
+
+
+if __name__ == "__main__":
+    data_path = "/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/sam-reformat"
+    dst = VILAWebDataset(
+        data_path=osp.abspath(data_path),
+    )
+    # print(dst[0])
+    for idx, data in enumerate(dst):
+        print(idx, data.keys())
diff --git a/llava/data/dataset_impl/textocr.py b/llava/data/dataset_impl/textocr.py
new file mode 100644
index 00000000..a328ff6e
--- /dev/null
+++ b/llava/data/dataset_impl/textocr.py
@@ -0,0 +1,280 @@
+import base64
+import copy
+import io
+import json
+import logging
+import os
+import os.path
+import os.path as osp
+import pathlib
+import pickle
+import random
+import re
+import time
+from dataclasses import dataclass, field
+from functools import lru_cache
+from typing import Dict, List, Optional, Sequence
+
+import numpy as np
+import PIL
+import torch
+import transformers
+from PIL import Image
+from pytorchvideo.data.encoded_video import EncodedVideo
+from torch.utils.data import ConcatDataset, Dataset
+from torchvision.transforms import Resize
+
+import llava.data.datasets_mixture as datasets_mixture
+from llava import conversation as conversation_lib
+from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                             DEFAULT_IMAGE_TOKEN, IGNORE_INDEX,
+                             IMAGE_TOKEN_INDEX)
+from llava.data.dataset import LazySupervisedDataset
+from llava.data.datasets_mixture import DATASETS
+from llava.data.simple_vila_webdataset import VILAWebDataset
+from llava.mm_utils import is_gemma_tokenizer, tokenizer_image_token
+from llava.model import *
+from llava.train.args import DataArguments, TrainingArguments
+from llava.train.llava_trainer import LLaVATrainer
+
+DEFAULT_TEXTOCR = "~/nvr_elm_llm/dataset/TextOCR"
+DEFAULT_TEXTOCR = osp.expanduser(DEFAULT_TEXTOCR)
+
+
+class GenericDataset:
+    """
+    A class representing a generic dataset.
+
+    This class provides methods for adding datasets and resizing images.
+
+    Attributes:
+        image_height (int): The desired height of the image.
+        patch_width (int): The width of the patch.
+
+    Methods:
+        add(dataset): Adds a dataset to the current dataset.
+        resize_image(image): Resizes the given image to the desired height and width.
+
+    """
+
+    def add(self, dataset):
+        return SummedDataset(self, dataset)
+
+    def resize_image(self, image):
+        original_width, original_height = image.size
+
+        original_height = max(original_height, 1)
+        original_width = max(original_width, 1)
+
+        scale = self.image_height / original_height
+
+        resized_width = int(round(scale * original_width, 0))
+        new_width = resized_width + (self.patch_width - (resized_width % self.patch_width))  # Adjusted this line
+
+        return image.resize((new_width, self.image_height))
+
+    def __add__(self, dataset):
+        return self.add(dataset)
+
+
+class SummedDataset(GenericDataset):
+    def __init__(self, dataset_left, dataset_right) -> None:
+        self.left = dataset_left
+        self.right = dataset_right
+
+    def __len__(self):
+        return len(self.left) + len(self.right)
+
+    def __getitem__(self, idx):
+        if idx > (len(self.left) - 1):
+            idx_corrected = idx % len(self.left)
+            return self.right[idx_corrected]
+
+        return self.left[idx]
+
+
+class TextOCRDataset(GenericDataset):
+    name = "text_ocr_dataset"
+
+    def __init__(
+        self,
+        base_folder=DEFAULT_TEXTOCR,
+        split="train",
+        transforms=lambda x: x,
+        min_area=0.001,
+    ) -> None:
+        super().__init__()
+
+        self.split = split
+        self.transforms = transforms
+        self.data = []
+        self.img2text = {}
+
+        annotations = json.load(open(os.path.join(base_folder, f"TextOCR_0.1_{split}.json"), "r"))
+        valid_images = [
+            {
+                "size": (
+                    annotations["imgs"][img]["width"],
+                    annotations["imgs"][img]["height"],
+                ),
+                "path": os.path.join(
+                    base_folder,
+                    annotations["imgs"][img]["file_name"].replace("train/", "train_images/"),
+                ),
+                "annots": [str(i) for i in annotations["imgToAnns"][img]],
+            }
+            for img in annotations["imgs"]
+        ]
+
+        for image in valid_images:
+            for ann in image["annots"]:
+                annotation = annotations["anns"][ann]
+                if annotation["utf8_string"] == ".":
+                    continue  # Unreadable characters
+
+                x, y, w, h = [int(x) for x in annotation["bbox"]]
+                img_area = image["size"][0] * image["size"][1]
+                if (w * h) / img_area < min_area:
+                    continue  # skip too small texts
+
+                fpath = image["path"]
+                self.data.append(
+                    {
+                        "image_path": fpath,
+                        "bbx": [int(x) for x in annotation["bbox"]],
+                        "transcription": annotation["utf8_string"],
+                    }
+                )
+
+                if fpath not in self.img2text:
+                    self.img2text[fpath] = []
+                self.img2text[fpath].append(
+                    {
+                        "bbx": [int(x) for x in annotation["bbox"]],
+                        "transcription": annotation["utf8_string"],
+                    }
+                )
+
+        self.image_ids = list(self.img2text.keys())
+
+    def __len__(self):
+        # return len(self.data)
+        return len(self.image_ids)
+
+    def __getitem__(self, idx):
+        img_id = self.image_ids[idx]
+        metadata = self.img2text[img_id]
+        origin_image = Image.open(img_id).convert("RGB")
+
+        annotation = [_["transcription"] for _ in metadata]
+        bboxes = [_["bbx"] for _ in metadata]
+
+        return {
+            "image_path": img_id,
+            "origin_image": origin_image,
+            "annotation": annotation,
+            "bboxes": bboxes,
+            "dataset": self.name,
+            "split": self.split,
+        }
+
+    def __getitem__single__(self, idx):
+        metadata = self.data[idx]
+        origin_image = Image.open(metadata["image_path"])
+        x, y, w, h = metadata["bbx"]
+        cropped_image = Image.open(metadata["image_path"]).crop((x, y, x + w, y + h)).convert("RGB")
+        return {
+            "image_path": metadata["image_path"],
+            "origin_image": origin_image,
+            "cropped_image": cropped_image,
+            "annotation": metadata["transcription"],
+            "dataset": self.name,
+            "split": self.split,
+            "tokens": [char for char in metadata["transcription"]],
+        }
+
+
+def preprocess_OCR(image, texts: list, data_args, tokenizer):
+    text = " ".join(texts)
+    caption = f"Please read the texts on image and type it below, each word separated by space.\n{text}"
+
+    caption = DEFAULT_IMAGE_TOKEN + caption + tokenizer.eos_token
+    vila_img = LazySupervisedDataset._process_image(image, data_args, image_folder=None)
+
+    input_ids = tokenizer_image_token(
+        caption,
+        tokenizer,
+        return_tensors="pt",
+    )
+
+    targets = copy.deepcopy(input_ids)
+    # mask image tokens is unnecessary for llava-1.5
+    # targets[targets == IMAGE_TOKEN_INDEX] = IGNORE_INDEX
+    for i in range(len(targets)):
+        targets[i][targets[i] == tokenizer.pad_token_id] = IGNORE_INDEX
+
+    return dict(
+        input_ids=input_ids,
+        labels=targets,
+        image=vila_img.unsqueeze(0),
+    )
+
+
+class VILATextOCR(Dataset):
+    """
+    Dataset class for VILA OCR data.
+
+    Args:
+        data_path (str): The path to the data.
+        image_folder (str): The folder containing the images.
+        tokenizer (transformers.PreTrainedTokenizer): The tokenizer for text processing.
+        data_args (DataArguments): The data arguments.
+        training_args (TrainingArguments): The training arguments.
+        split (str, optional): The split of the dataset (default: "train").
+        min_area (float, optional): The minimum area of the text (default: 0.001).
+    """
+
+    def __init__(
+        self,
+        data_path,
+        image_folder,
+        tokenizer: transformers.PreTrainedTokenizer,
+        data_args: DataArguments,
+        training_args: TrainingArguments,
+        split="train",
+        min_area=0.001,
+    ) -> None:
+        super().__init__()
+
+        data_path = osp.expanduser(data_path)
+        self.dataset = TextOCRDataset(data_path, split, min_area=min_area)
+
+        self.data_path = data_path
+        self.tokenizer = tokenizer
+        self.data_args = data_args
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        meta = self.dataset[index]
+
+        img = meta["origin_image"]
+        fpath = meta["image_path"]
+        text = " ".join(meta["annotation"])
+
+        caption = f"Please read the texts on image and type it below, each word separated by space.\n{text}"
+
+        data = preprocess_OCR(image=img, texts=text, data_args=self.data_args, tokenizer=self.tokenizer)
+        return data
+
+
+if __name__ == "__main__":
+    from pprint import pprint
+
+    dataset = TextOCRDataset()
+    # dataset = VILATextOCR()
+    print(len(dataset))
+
+    for idx in range(2):
+        pprint(dataset[idx])
diff --git a/llava/data/dataset_tar.py b/llava/data/dataset_tar.py
new file mode 100644
index 00000000..e081aeb3
--- /dev/null
+++ b/llava/data/dataset_tar.py
@@ -0,0 +1,246 @@
+import glob
+import tarfile
+import json
+import os, os.path as osp
+from io import BytesIO
+from PIL import Image, ImageFile
+import hashlib
+from torch.utils.data import Dataset, get_worker_info, ConcatDataset
+from multiprocessing.pool import ThreadPool as Pool
+
+try:  # make torchvision optional
+    from torchvision.transforms.functional import to_tensor
+except:
+    to_tensor = None
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class UnexpectedEOFTarFile(tarfile.TarFile):
+    def _load(self):
+        """Read through the entire archive file and look for readable
+        members.
+        """
+        try:
+            while True:
+                tarinfo = self.next()
+                if tarinfo is None:
+                    break
+        except tarfile.ReadError as e:
+            assert e.args[0] == "unexpected end of data"
+        self._loaded = True
+
+
+class TarDataset(Dataset):
+    """Dataset that supports Tar archives (uncompressed)."""
+
+    def __init__(
+        self,
+        archive,
+        transform=None,
+        is_valid_file=lambda m: m.isfile() and m.name.lower().endswith((".png", ".jpg", ".jpeg")),
+        ignore_unexpected_eof=False,
+        cache_dir="~/.cache/tardataset",
+    ):
+        self.transform = transform
+        self.archive = archive
+        self.default_label = archive
+
+        # open tar file. in a multiprocessing setting (e.g. DataLoader workers), we
+        # have to open one file handle per worker (stored as the tar_obj dict), since
+        # when the multiprocessing method is 'fork', the workers share this TarDataset.
+        # we want one file handle per worker because TarFile is not thread-safe.
+        worker = get_worker_info()
+        worker = worker.id if worker else None
+        self.tar_obj = {}  # lazy init
+
+        # TODO: add a function hash
+        # create a hash to cache results
+        mtime = osp.getmtime(archive)
+        # fn_hash = hash(is_valid_file)
+        m = hashlib.sha256()
+        m.update(str(mtime).encode("utf-8"))
+        # m.update(str(fn_hash).encode("utf-8"))
+        uuid = m.hexdigest()[:7]
+        # print(mtime, uuid)
+
+        tar_info = osp.realpath(archive).replace("/", "-") + f"-{uuid}.json"
+        fpath = osp.join(osp.expanduser(cache_dir), tar_info)
+        if not osp.exists(fpath):
+            self.tar_obj = {
+                worker: tarfile.open(archive) if ignore_unexpected_eof is False else UnexpectedEOFTarFile.open(archive)
+            }
+            print(f"{osp.basename(archive)} preparing tar.getnames() ...")
+            self.all_members = self.tar_obj[worker].getmembers()
+            # also store references to the iterated samples (a subset of the above)
+            self.samples = [m.name for m in self.all_members if is_valid_file(m)]
+            os.makedirs(osp.dirname(fpath), exist_ok=True)
+            json.dump(self.samples, open(fpath, "w"), indent=2)
+        else:
+            print(f"loading cached tarinfo from {fpath}")
+            self.samples = json.load(open(fpath, "r"))
+
+    def __getitem__(self, index):
+        image = self.get_image(self.samples[index], pil=True)
+        image = image.convert("RGB")  # if it's grayscale, convert to RGB
+        if self.transform:  # apply any custom transforms
+            image = self.transform(image)
+        return image, self.default_label
+
+    def __len__(self):
+        return len(self.samples)
+
+    def get_image(self, name, pil=False):
+        image = Image.open(BytesIO(self.get_file(name).read()))
+        if pil:
+            return image
+        return to_tensor(image)
+
+    def get_text_file(self, name, encoding="utf-8"):
+        """Read a text file from the Tar archive, returned as a string.
+
+        Args:
+          name (str): File name to retrieve.
+          encoding (str): Encoding of file, default is utf-8.
+
+        Returns:
+          str: Content of text file.
+        """
+        return self.get_file(name).read().decode(encoding)
+
+    def get_file(self, name):
+        """Read an arbitrary file from the Tar archive.
+
+        Args:
+          name (str): File name to retrieve.
+
+        Returns:
+          io.BufferedReader: Object used to read the file's content.
+        """
+        # ensure a unique file handle per worker, in multiprocessing settings
+        worker = get_worker_info()
+        worker = worker.id if worker else None
+
+        if worker not in self.tar_obj:
+            self.tar_obj[worker] = tarfile.open(self.archive)
+
+        return self.tar_obj[worker].extractfile(name)
+
+    def __del__(self):
+        """Close the TarFile file handles on exit."""
+        for o in self.tar_obj.values():
+            o.close()
+
+    def __getstate__(self):
+        """Serialize without the TarFile references, for multiprocessing compatibility."""
+        state = dict(self.__dict__)
+        state["tar_obj"] = {}
+        return state
+
+
+class TarImageFolder(Dataset):
+    """Dataset that supports Tar archives (uncompressed), with a folder per class.
+
+    Similarly to torchvision.datasets.ImageFolder, assumes that the images inside
+    the Tar archive are arranged in this way by default:
+      root/
+        dog.tar
+        cat.tar
+        ...
+        bird.tar
+
+      where
+
+      dog.tar/
+        xxx.png
+        xxy.png
+        [...]/xxz.png
+
+      cat.tar/
+        123.png
+        nsdf3.png
+        [...]/asd932_.png
+    """
+
+    def __init__(
+        self,
+        root,
+        transform=None,
+        max_loads=None,
+        is_valid_file=lambda m: m.isfile() and m.name.lower().endswith((".png", ".jpg", ".jpeg")),
+        pool_size=16,
+    ):
+        # load the archive meta information, and filter the samples
+        super().__init__()
+
+        self.transform = transform
+        # assign a label to each image, based on its top-level folder name
+        self.class_to_idx = {}
+        self.targets = []
+
+        tarfs = sorted(glob.glob(osp.join(root, "*.tar")))
+
+        if max_loads is not None:
+            tarfs = tarfs[: min(max_loads, len(tarfs))]
+        print(tarfs)
+        # tar_dst_list = [TarDataset(tar_path) for tar_path in tarfs]
+
+        self.class2idx = {}
+
+        for tar_fpath in tarfs:
+            self.class2idx[tar_fpath] = len(self.class2idx.keys())
+
+        print(self.class2idx)
+
+        # parallel loading
+        def worker(tar_path):
+            return TarDataset(tar_path)
+
+        pool = Pool(pool_size)
+        jobs = []
+        for tar_path in tarfs:
+            jobs.append(pool.apply_async(worker, (tar_path,)))
+        pool.close()
+        pool.join()
+        tar_dst_list = [_.get() for _ in jobs]
+        self.dataset = ConcatDataset(tar_dst_list)
+        print("TarImageFolder dataset init finish")
+
+        if len(self.class2idx) == 0:
+            raise IOError(
+                "No classes (top-level folders) were found with the given criteria. The given\n"
+                "extensions, is_valid_file is too strict, or the archive is empty."
+            )
+
+        # the inverse mapping is often useful
+        self.idx2class = {v: k for k, v in self.class2idx.items()}
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        image, _label = self.dataset[index]
+        if self.transform:  # apply any custom transforms
+            image = self.transform(image)
+
+        label = self.class2idx[_label]
+        return (image, label)
+
+
+if __name__ == "__main__":
+    # img = TarDataset(
+    #     "/lustre/fs4/portfolios/llmservice/projects/llmservice_nlp_fm/datasets/segmentation/sam/stage0/sa_000999.tar"
+    # )
+    # print("init finish, try to fetch images")
+    # print(img[0])
+
+    dst = TarImageFolder(
+        "/lustre/fs4/portfolios/llmservice/projects/llmservice_nlp_fm/datasets/segmentation/sam/stage0",
+        max_loads=16,
+    )
+    print("init finish, try to fetch images")
+
+    for idx, (image, label) in enumerate(dst):
+        print(image, label)
+        if idx > 100:
+            break
diff --git a/llava/data/datasets_mixture.py b/llava/data/datasets_mixture.py
index b6a1c1c0..6ef9e57f 100644
--- a/llava/data/datasets_mixture.py
+++ b/llava/data/datasets_mixture.py
@@ -1,69 +1,87 @@
-# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# SPDX-License-Identifier: Apache-2.0
-
 from dataclasses import dataclass, field
 
+
 @dataclass
 class Dataset:
     dataset_name: str
     dataset_type: str = field(default="torch")
-    data_path: str = field(
-        default=None, metadata={"help": "Path to the training data."}
-    )
-    image_path: str = field(
-        default=None, metadata={"help": "Path to the training image data."}
+    data_path: str = field(default=None, metadata={"help": "Path to the training data."})
+    meta_path: str = field(default=None, metadata={"help": "Path to the meta data for webdataset."})
+    image_path: str = field(default=None, metadata={"help": "Path to the training image data."})
+    description: str = field(
+        default=None,
+        metadata={
+            "help": "Detailed desciption of where the data is from, how it is labelled, intended use case and the size of the dataset."
+        },
     )
+    test_script: str = (None,)
+    maintainer: str = (None,)
+
 
-DATASETS_MIXTURES = {}
 DATASETS = {}
 
+import warnings
+
+
 def add_dataset(dataset):
+    if dataset.dataset_name in DATASETS:
+        # make sure the data_name is unique
+        warnings.warn(f"{dataset.dataset_name} already existed in DATASETS. Make sure the name is unique.")
+    assert "+" not in dataset.dataset_name, "Dataset name cannot include symbol '+'."
     DATASETS.update({dataset.dataset_name: dataset})
 
 
+
 def register_datasets_mixtures():
+
+    # Align
+    llava_1_5_mm_align = Dataset(
+        dataset_name='llava_1_5_align',
+        dataset_type='torch',
+        data_path='./playground/data/LLaVA-Pretrain/LLaVA-CC3M-Pretrain-595K.json',
+        image_path='./playground/data/LLaVA-Pretrain/images'
+    )
+    add_dataset(llava_1_5_mm_align)
+
+    # Pretrain
     coyo_25m = Dataset(
         dataset_name='coyo',
         dataset_type='coyo',
         data_path='./playground/data/coyo-700m/pkl02-split')
     add_dataset(coyo_25m)
-    coyo_25m_wds = Dataset(
-        dataset_name='coyo-webdataset',
-        dataset_type='coyo-wds',
-        data_path='./playground/data/coyo-25m-vila')
-    add_dataset(coyo_25m_wds)
+
     mmc4core = Dataset(
         dataset_name='mmc4core',
         dataset_type='mmc4',
         data_path='./playground/data/mmc4-core/pkl-core')
     add_dataset(mmc4core)
-    llava_1_5_mm_align = Dataset(
-        dataset_name='llava_1_5_align',
-        dataset_type='torch',
-        data_path='./playground/data/LLaVA-Pretrain/LLaVA-CC3M-Pretrain-595K.json',
-        image_path='./playground/data/LLaVA-Pretrain/images'
+
+    sharegpt4v_pretrain = Dataset(
+        dataset_name="sharegpt4v_pretrain",
+        dataset_type="torch",
+        data_path="./playground/data/ShareGPT4V/jason-filter-share-captioner_coco_lcs_sam_1246k_1107.json",
+        image_path="./playground/data/ShareGPT4V/data",
     )
-    add_dataset(llava_1_5_mm_align)
-    llava_1_5_sft = Dataset(
-        dataset_name='llava_1_5_sft',
-        dataset_type='torch',
-        data_path='./playground/data/llava_v1_5_mix665k.json',
-        image_path='./playground/data'
+    add_dataset(sharegpt4v_pretrain)
+
+    # SFT
+    sharegpt4v_gpt4_100k = Dataset(
+        dataset_name="sharegpt4v_gpt4_100k",
+        dataset_type="torch",
+        data_path="./playground/datasharegpt_video/ShareGPT4V/sharegpt4v_instruct_gpt4-vision_cap100k.json",
+        image_path="./playground/datasharegpt_video/ShareGPT4V/data",
+    )
+    add_dataset(sharegpt4v_gpt4_100k)
+
+    llava_instruct = Dataset(
+        dataset_name="llava_instruct",
+        dataset_type="torch",
+        data_path="./playground/data/llava_instruct_150k_zh.jsonl",
+        image_path="./playground/data/coco",
+        description="",
     )
-    add_dataset(llava_1_5_sft)
+    add_dataset(llava_instruct)
+
     sharegpt4v_sft = Dataset(
         dataset_name='sharegpt4v_sft',
         dataset_type='torch',
@@ -71,20 +89,141 @@ def register_datasets_mixtures():
         image_path='./playground/data'
     )
     add_dataset(sharegpt4v_sft)
+
+    dvqa_train_200k = Dataset(
+        dataset_name="dvqa_train_200k",
+        dataset_type="torch",
+        data_path="./playground/data/dvqa_train_200k.jsonl",
+        image_path="./playground/data/dvqa",
+        description="",
+    )
+    add_dataset(dvqa_train_200k)
+
+    chartqa_train_18k = Dataset(
+        dataset_name="chartqa_train_18k",
+        dataset_type="torch",
+        data_path="./playground/data/chartqa_train_18k.jsonl",
+        image_path="./playground/data/chartqa",
+        description="",
+    )
+    add_dataset(chartqa_train_18k)
+
+    ai2d_train_12k = Dataset(
+        dataset_name="ai2d_train_12k",
+        dataset_type="torch",
+        data_path="./playground/data/ai2d_train_12k.jsonl",
+        image_path="./playground/data/ai2d",
+        description="",
+    )
+    add_dataset(ai2d_train_12k)
+
+    docvqa_train_10k = Dataset(
+        dataset_name="docvqa_train_10k",
+        dataset_type="torch",
+        data_path="./playground/data/docvqa_train_10k.jsonl",
+        image_path="./playground/data/docvqa",
+        description="",
+    )
+    add_dataset(docvqa_train_10k)
+
+    geoqa = Dataset(
+        dataset_name="geoqa",
+        dataset_type="torch",
+        data_path="./playground/data/geoqa+.jsonl",
+        image_path="./playground/data/geoqa+",
+        description="",
+    )
+    add_dataset(geoqa)
+
+    synthdog_en = Dataset(
+        dataset_name="synthdog_en",
+        dataset_type="torch",
+        data_path="./playground/data/synthdog_en.jsonl",
+        image_path="./playground/data/synthdog-en",
+        description="",
+    )
+    add_dataset(synthdog_en)
+
     vflan = Dataset(
         dataset_name='vflan',
         dataset_type='vflan',
         data_path='./playground/data/vlm-flan-clean-text1m-nosqa-sharded'
     )
     add_dataset(vflan)
+
+
+    scienceqa = Dataset(
+        dataset_name="scienceqa",
+        dataset_type="torch",
+        data_path="./playground/data/scienceqa/scienceqa_train_12k.json",
+        image_path="./playground/data/scienceqa/images",
+    )
+    add_dataset(scienceqa)
+
     
+    sherlock = Dataset(
+        dataset_name="sherlock",
+        dataset_type="torch",
+        data_path="./playground/data/sherlock/processed/sherlock_317k.json",
+        image_path="./playground/data/sherlock/images",
+    )
+    add_dataset(sherlock)
+    math = Dataset(
+        dataset_name="math",
+        dataset_type="vflan",
+        data_path="./playground/data/math",
+    )
+    add_dataset(math)
+
+    wit_subset = Dataset(
+        dataset_name="wit_subset",
+        dataset_type="torch",
+        data_path="./playground/data/WIT/wit_1_8m/wit_processed_538k.json",
+        image_path="./playground/data/WIT/wit_1_8m/images"
+    )
+    add_dataset(wit_subset)
+
+    youcook2 = Dataset(
+        dataset_name="youcook2",
+        dataset_type="torch",
+        data_path="./playground/data/youcook2/youcook_filtered_v3.json",
+        image_path="./playground/data/youcook2/video_data_clipped",
+    )
+    add_dataset(youcook2)
     
-    DATASETS_MIXTURES.update({'coyo_25m_wds': [coyo_25m_wds]})
-    DATASETS_MIXTURES.update({'llava_1_5_mm_align': [llava_1_5_mm_align]})
-    DATASETS_MIXTURES.update({'llava_1_5_sft': [llava_1_5_sft]})
-    DATASETS_MIXTURES.update({'sharegpt4v_sft': [sharegpt4v_sft]})
-    DATASETS_MIXTURES.update({'coyo_25m': [coyo_25m]})
-    DATASETS_MIXTURES.update({'mmc4core': [mmc4core]})
-    DATASETS_MIXTURES.update({'coyo_25m_mmc4core': [coyo_25m, mmc4core]})
-    DATASETS_MIXTURES.update({'vflan': [vflan]})
-    DATASETS_MIXTURES.update({'vflan_sharegpt4v_sft': [vflan, sharegpt4v_sft]})
+    vatex = Dataset(
+        dataset_name="vatex",
+        dataset_type="torch",
+        data_path="./playground/data/vatex/vatex_filtered_v3.json",
+        image_path="./playground/data/vatex/videos_clipped",
+    )
+    add_dataset(vatex)
+
+    video_chatgpt = Dataset(
+        dataset_name="video_chatgpt",
+        dataset_type="torch",
+        data_path="./playground/data/Video_ChatGPT/VideoInstruct-100K/VideoInstruct100K.json",
+        image_path="./playground/data/Video_ChatGPT/activitynet_videos/",
+    )
+    add_dataset(video_chatgpt)
+
+    shot2story_shotonly = Dataset(
+        dataset_name="shot2story_shotonly",
+        dataset_type="torch",
+        data_path="./playground/data/shot2story/shot2story_shotonly.json",
+        image_path="./playground/data/shot2story/Shot2Story/data/videos_extracted",
+    )
+    add_dataset(shot2story_shotonly)
+
+    sharegpt_video = Dataset(
+        dataset_name="sharegpt_video",
+        dataset_type="torch",
+        data_path="./playground/data/sharegpt_video/video_caption_pretrain.json",
+        image_path="./playground/data/sharegpt_video/videos",
+    )
+    add_dataset(sharegpt_video)
+
+
+
+
+    
\ No newline at end of file
diff --git a/llava/data/simple_video_dataset.py b/llava/data/simple_video_dataset.py
new file mode 100644
index 00000000..fc37d8b9
--- /dev/null
+++ b/llava/data/simple_video_dataset.py
@@ -0,0 +1,269 @@
+import shutil
+import os, os.path as osp, io
+import argparse
+import pprint
+import pickle
+from bisect import bisect
+import base64
+from PIL import Image
+import json
+from filelock import Timeout, FileLock
+from functools import lru_cache, reduce
+import tarfile
+from multiprocessing.pool import ThreadPool as Pool
+import multiprocessing
+import torch
+import torch.distributed
+from torch.utils.data import Dataset, get_worker_info, ConcatDataset
+
+import getpass
+from llava.wids import ShardListDataset
+
+
+# @lru_cache(maxsize=32)
+def load_tarfile(tar_path):
+    return tarfile.open(tar_path)
+
+
+# INTERNVID = "/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/internvid/video"
+INTERNVID = "/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v3/ego4d/ego4d_clips_tar/ego4d_1m"
+CACHEDIR = "/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v3/ego4d/ego4d_clips_tar/ego4d_1m-webds-meta"
+
+def process_tarfile(tar_abspath, tar_meta_path, cache_dir):
+    tar_realpath = osp.realpath(tar_abspath)
+    tar_real_meta_path = osp.join(
+        osp.expanduser(cache_dir),
+        "dev",
+        tar_realpath.replace("/", "--") + ".wdsmeta.json",
+    )
+
+    print(f"Fetch meta information {tar_abspath} ...")
+
+    if not osp.exists(tar_meta_path) and not osp.exists(tar_real_meta_path):
+        print(f"    Generating meta: {tar_meta_path}")
+        try:
+            tar = load_tarfile(tar_abspath)
+            uuids = list(set([".".join(_.split(".")[:-1]) for _ in tar.getnames()]))
+        except tarfile.ReadError as e:
+            print(f"Skipping {tar_abspath}")
+            print(e)
+            return
+        nsamples = len(uuids)
+        url = osp.abspath(tar_abspath)
+        tar_meta = {
+            "url": url,
+            "nsamples": nsamples,
+            "filesize": osp.getsize(tar_abspath),
+        }
+        os.makedirs(osp.dirname(tar_meta_path), exist_ok=True)
+        json.dump(tar_meta, open(tar_meta_path, "w+"), indent=2)
+
+    if osp.exists(tar_meta_path):
+        print(f"    Generating abs meta: {tar_meta_path}")
+        tar_meta = json.load(open(tar_meta_path, "r"))
+    elif osp.exists(tar_real_meta_path):
+        print(f"    Generating abs meta: {tar_real_meta_path}")
+        tar_meta = json.load(open(tar_real_meta_path, "r"))
+    else:
+        raise NotImplementedError
+
+    tar_meta["url"] = osp.abspath(tar_abspath)
+    os.makedirs(osp.dirname(tar_meta_path), exist_ok=True)
+    json.dump(tar_meta, open(tar_meta_path, "w+"), indent=2)
+    if tar_meta_path != tar_real_meta_path and not osp.exists(tar_real_meta_path):
+        # tar_meta["url"] = osp.realpath(tar_abspath)
+        print(f"    [abs2real] Copying {tar_meta_path} => {tar_real_meta_path}")
+        os.makedirs(osp.dirname(tar_real_meta_path), exist_ok=True)
+        json.dump(tar_meta, open(tar_real_meta_path, "w+"), indent=2)
+
+    return tar_meta
+
+class SimpleVideoDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        data_path="/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/internvid/video_data_tar/InternVid-1300K-flt",
+        # cache_dir="/home/ligengz/.cache/simplecoyo",
+        # cache_dir="/lustre/fsw/portfolios/llmservice/projects/llmservice_nlp_fm/datasets/captioning/vila-webds-meta",
+        cache_dir="/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/internvid/video_data_tar/InternVid-1300K-flt-webds-meta",
+        meta_path=None,
+        # image_load_mode="pil",  # pil / rawbytes / fpath,
+        max_shards_to_load=None,
+        overwrite=False,
+    ):
+        self.data_path = data_path
+        self.meta_path = meta_path
+        if meta_path is None:
+            self.meta_path = osp.join(
+                cache_dir,
+                data_path.replace("/", "--") + f".max_shards:{max_shards_to_load}" + ".wdsmeta.json",
+            )
+            self.max_shards_to_load = max_shards_to_load
+
+        if not osp.exists(self.meta_path) or overwrite:
+            assert (
+                not torch.distributed.is_initialized()
+            ), "Dataset meta file does not exist and generating may take a long time. \
+                Please exit distributed mode and run `python llava/train/simple_coyo_dataset.py <webdataset path>`. \
+                or set proper `meta_path=` when initializing."
+            print(f"Meta path not found: {self.meta_path}")
+            print(f"Walking through dirs {data_path}")
+            # tar_list = sorted([f for f in os.listdir(data_path) if f.endswith(".tar")])
+            tar_list = []
+            for root, dirs, files in os.walk(data_path):
+                for file in files:
+                    fpath = osp.join(root, file)
+                    fpath = osp.relpath(fpath, data_path)
+                    if not fpath.endswith(".tar"):
+                        continue
+                    # fpath = osp.abspath(osp.join(root, file))
+                    tar_list.append(fpath)
+            tar_list = sorted(tar_list)
+
+            if "internvid" in data_path:
+                meta_name = "internvid-dev"
+            elif "ego4d" in data_path:
+                meta_name = "ego4d-dev"
+            else:
+                print(f"Unknown dataset: {data_path}. Please include the dataset name in the data path")
+                meta_name = "unknown"
+                raise NotImplementedError
+
+            meta = {
+                "name": meta_name,
+                "__kind__": "SimpleVideoDataset",
+                "wids_version": 1,
+                "shardlist": [],
+            }
+
+
+            max_processes = 16  # Set the maximum number of processes
+            pool = multiprocessing.Pool(processes=max_processes)
+            results = []
+            for idx, tar_relpath in enumerate(tar_list):
+                tar_abspath = osp.join(data_path, tar_relpath)
+                tar_meta_path = osp.join(
+                    osp.expanduser(cache_dir),
+                    "dev",
+                    tar_abspath.replace("/", "--") + ".wdsmeta.json",
+                )
+                result = pool.apply_async(process_tarfile, (tar_abspath, tar_meta_path, cache_dir))
+                results.append(result)
+
+            pool.close()
+            pool.join()
+
+            meta["shardlist"] = [result.get() for result in results if result.get() is not None]
+            # sorted by tar names
+            meta["shardlist"] = sorted(meta["shardlist"], key=lambda x: x["url"])
+            os.makedirs(osp.dirname(self.meta_path), exist_ok=True)
+            json.dump(meta, open(self.meta_path, "w+"), indent=2)
+
+        print(f"[SimplyVideo] Loading meta infomation {self.meta_path}", flush=True)
+
+        # uuid = abs(hash(self.meta_path)) % (10 ** 8)
+        import hashlib
+
+        uuid = hashlib.sha256(self.meta_path.encode()).hexdigest()[:8]
+        # get user name
+        user_name = os.getenv("USER")
+        print(f"User name: {user_name}")
+        self.dataset = ShardListDataset(
+            self.meta_path,
+            cache_dir=osp.expanduser(
+                f"/lustre/fsw/portfolios/nvr/users/{user_name}/cache/_wids_cache/{getpass.getuser()}-{uuid}"
+            ),
+        )
+
+    def __getitem__(self, idx):
+        return self.dataset[idx]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    @staticmethod
+    def simple_collate(batch):
+        batched_data = {}
+        for data in batch:
+            for k, v in data.items():
+                if k not in batched_data:
+                    batched_data[k] = []
+                batched_data[k].append(v)
+        return dict(batched_data)
+
+    @staticmethod
+    def custom_collate(batch):
+        def transform2list(a: dict):
+            # trasnform all leaf nodes to list
+            for k, v in a.items():
+                if isinstance(v, dict):
+                    a[k] = transform2list(v)
+                else:
+                    a[k] = [
+                        v,
+                    ]
+            return a
+
+        def merge(a: dict, b: dict, path=[], strict=False):
+            c = {}
+            keys = set(a.keys()).union(b.keys())
+            # print(keys)
+            for key in keys:
+                if key in a and key in b:
+                    if isinstance(a[key], dict) and isinstance(b[key], dict):
+                        # apply recursively
+                        c[key] = merge(a[key], b[key], path + [str(key)], strict=strict)
+                    else:
+                        c[key] = a[key] + b[key]
+                else:
+                    if strict:
+                        raise Exception("Conflict at " + ".".join(path + [str(key)]))
+                    c[key] = a[key] if key in a else b[key]
+            return c
+
+        tasks = (transform2list(_) for _ in batch)
+        return reduce(merge, tasks)
+
+
+if __name__ == "__main__":
+    import torch
+    import torch.distributed as dist
+    from torch.utils.data.distributed import DistributedSampler
+
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("data_path", nargs="?", type=str, default=INTERNVID)
+    parser.add_argument("cache_path", nargs="?", type=str, default=CACHEDIR)
+    # replaced by rank and world size
+    parser.add_argument("-m", "--max-shards", type=int, default=None)
+    parser.add_argument("-o", "--overwrite", action="store_true")
+    args = parser.parse_args()
+
+    print("overwrite:", args.overwrite)
+    train_dataset = SimpleVideoDataset(
+        data_path=args.data_path,
+        max_shards_to_load=args.max_shards,
+        cache_dir=args.cache_path,
+        overwrite=args.overwrite,
+    )
+
+    sampler = None
+    # from PIL import Image
+    from torch.utils.data import default_collate
+    from collections import defaultdict
+
+
+    dloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=False,
+        sampler=sampler,
+        batch_size=2,
+        collate_fn=SimpleVideoDataset.custom_collate,
+        num_workers=8,
+    )
+    # sampler.set_epoch(0)
+    print(len(train_dataset), len(dloader))
+    for idx, data in enumerate(dloader):
+        print(f"{idx}-of-{len(dloader)}", data)
+        if idx >= 5:
+            break
diff --git a/llava/data/simple_vila_webdataset.py b/llava/data/simple_vila_webdataset.py
new file mode 100644
index 00000000..13118ad7
--- /dev/null
+++ b/llava/data/simple_vila_webdataset.py
@@ -0,0 +1,322 @@
+import argparse
+import base64
+import getpass
+import io
+import json
+import os
+import os.path as osp
+import pickle
+import pprint
+import shutil
+import tarfile
+from bisect import bisect
+from functools import lru_cache, reduce
+from multiprocessing.pool import ThreadPool as Pool
+
+import torch
+import torch.distributed
+from filelock import FileLock, Timeout
+from PIL import Image
+from torch.utils.data import ConcatDataset, Dataset, get_worker_info
+
+from llava.wids import ShardListDataset
+
+
+# @lru_cache(maxsize=32)
+def load_tarfile(tar_path):
+    return tarfile.open(tar_path)
+
+
+COYO_25M_VILA = "~/datasets/captioning/coyo-25m-vila"
+
+
+def save_json(obj, fpath):
+    print(f"saving to {fpath}")
+    os.makedirs(osp.dirname(fpath), exist_ok=True)
+    json.dump(obj, open(fpath, "w+"), indent=2)
+
+
+def generate_and_load_tar_meta(data_path, tar_path, cache_dir, overwrite=False):
+    tar_abspath = osp.abspath(osp.join(data_path, tar_path))
+    tar_abs_metapath = osp.join(
+        osp.expanduser(cache_dir),
+        "dev",
+        tar_abspath.replace("/", "--") + ".wdsmeta.json",
+    )
+    tar_real_metapath = osp.join(
+        osp.expanduser(cache_dir),
+        "dev",
+        osp.realpath(tar_abspath).replace("/", "--") + ".wdsmeta.json",
+    )
+
+    if not osp.exists(tar_abs_metapath) and not osp.exists(tar_real_metapath) or overwrite:
+        # generate meta information for both abs and real file paths
+        print(f"    Generating meta: {tar_abs_metapath}")
+        try:
+            tar = load_tarfile(tar_abspath)
+            uuids = list(set([osp.splitext(_)[0] for _ in tar.getnames()]))
+        except tarfile.ReadError as e:
+            print(f"Skipping {tar_abspath}")
+            print(e)
+            return None
+        nsamples = len(uuids)
+        # print(uuids)
+        # print(nsamples)
+        # input()
+
+        tar_meta = {
+            "url": osp.abspath(tar_abspath),
+            "nsamples": nsamples,
+            "filesize": osp.getsize(tar_abspath),
+        }
+        save_json(tar_meta, tar_abs_metapath)
+
+        tar_meta = {
+            "url": osp.realpath(tar_abspath),
+            "nsamples": nsamples,
+            "filesize": osp.getsize(tar_abspath),
+        }
+        save_json(tar_meta, tar_real_metapath)
+
+    if osp.exists(tar_abs_metapath):
+        print(f"    Loading abs meta: {tar_abs_metapath}")
+        tar_meta = json.load(open(tar_abs_metapath, "r"))
+    elif osp.exists(tar_real_metapath):
+        print(f"    Loading realpath meta: {tar_real_metapath}")
+        tar_meta = json.load(open(tar_real_metapath, "r"))
+    else:
+        return None
+    return tar_meta
+
+
+def generate_wids_meta(tar_list, data_path, cache_dir, idx=0, total=0):
+    # TODO(ligeng): add return value
+    meta_path_of_tar_abs = osp.join(
+        osp.expanduser(cache_dir),
+        data_path.replace("/", "--") + ".wdsmeta.json",
+    )
+
+    meta_path_of_tar_rel = osp.join(osp.expanduser(data_path), "wids-meta.json")
+    ####################################################################################
+    meta = {
+        "name": "coyo-dev",
+        "__kind__": "VILA-WebDataset",
+        "wids_version": 1,
+        "shardlist": [],
+    }
+
+    for idx, tar_path in enumerate(tar_list):
+        print(f"{idx}-of-{len(tar_list)}")
+        tar_meta = generate_and_load_tar_meta(data_path, tar_path, cache_dir)
+        # tar_meta["url"] = tar_path
+        tar_meta["url"] = osp.abspath(osp.join(data_path, tar_path))
+        meta["shardlist"].append(tar_meta)
+
+    # sorted by tar names
+    meta["shardlist"] = sorted(meta["shardlist"], key=lambda x: x["url"])
+    if total == 0:
+        # only save for all information
+        save_json(meta, meta_path_of_tar_abs)
+
+    ####################################################################################
+    meta = {
+        "name": "coyo-dev",
+        "__kind__": "VILA-WebDataset",
+        "wids_version": 1,
+        "shardlist": [],
+    }
+    for idx, tar_path in enumerate(tar_list):
+        print(f"{idx}-of-{len(tar_list)}")
+        tar_meta = generate_and_load_tar_meta(data_path, tar_path, cache_dir)
+        if tar_meta is None:
+            continue
+        tar_meta["url"] = tar_path
+        meta["shardlist"].append(tar_meta)
+
+    # sorted by tar names
+    meta["shardlist"] = sorted(meta["shardlist"], key=lambda x: x["url"])
+    if total == 0:
+        # only save for all information
+        save_json(meta, meta_path_of_tar_rel)
+
+
+def prepare_wids_meta(data_path, cache_dir="~/datasets/vila-webds-meta-2", idx=0, total=0):
+    cache_dir = osp.expanduser(cache_dir)
+    # TODO(ligeng): speedup the generation
+    #   1. parallelize the meta file generation
+    #   2. add options for meta file
+    tar_list = []
+    for root, dirs, files in os.walk(data_path):
+        for file in files:
+            fpath = osp.join(root, file)
+            fpath = osp.relpath(fpath, data_path)
+            # print(fpath)
+            if not fpath.endswith(".tar"):
+                continue
+            # fpath = osp.abspath(osp.join(root, file))
+            tar_list.append(fpath)
+    tar_list = sorted(tar_list)
+
+    if total > 0:
+        chunk = len(tar_list) // total
+        begin_idx = chunk * idx
+        end_idx = chunk * (idx + 1)
+        if idx == total - 1:
+            end_idx = len(tar_list)
+        tar_list = tar_list[begin_idx:end_idx]
+        print(f"{chunk}, {begin_idx} -> {end_idx}")
+
+    assert len(tar_list) > 0, f"no tar was found in the repository {data_path} !"
+    print(f"generating meta for total {len(tar_list)} files.")
+    generate_wids_meta(tar_list, data_path, cache_dir, idx=idx, total=total)
+
+
+class VILAWebDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        data_path=COYO_25M_VILA,
+        meta_path=None,
+        cache_dir="/home/ligengz/datasets/vila-webds-meta",
+        max_shards_to_load=None,
+    ):
+        self.data_path = osp.expanduser(data_path)
+        self.meta_path = osp.expanduser(meta_path) if meta_path is not None else None
+        # self.max_shards_to_load = max_shards_to_load
+
+        _local_meta_path = osp.join(self.data_path, "wids-meta.json")
+        if meta_path is None and osp.exists(_local_meta_path):
+            print(f"loading from {_local_meta_path}")
+            self.meta_path = meta_path = _local_meta_path
+
+        if meta_path is None:
+            self.meta_path = osp.join(
+                osp.expanduser(cache_dir),
+                self.data_path.replace("/", "--") + f".max_shards:{max_shards_to_load}" + ".wdsmeta.json",
+            )
+
+        assert osp.exists(
+            self.meta_path
+        ), f"meta path not found in [{self.meta_path}] or [{_local_meta_path}]"
+        print(f"[SimplyCoyo] Loading meta infomation {self.meta_path}", flush=True)
+
+        # uuid = abs(hash(self.meta_path)) % (10 ** 8)
+        import hashlib
+
+        uuid = hashlib.sha256(self.meta_path.encode()).hexdigest()[:8]
+        self.dataset = ShardListDataset(
+            self.meta_path,
+            cache_dir=osp.expanduser(f"~/.cache/_wids_cache/{getpass.getuser()}-{uuid}"),
+        )
+
+    def __getitem__(self, idx):
+        return self.dataset[idx]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    @staticmethod
+    def simple_collate(batch):
+        batched_data = {}
+        for data in batch:
+            for k, v in data.items():
+                if k not in batched_data:
+                    batched_data[k] = []
+                batched_data[k].append(v)
+        return dict(batched_data)
+
+    @staticmethod
+    def custom_collate(batch):
+        def transform2list(a: dict):
+            # trasnform all leaf nodes to list
+            for k, v in a.items():
+                if isinstance(v, dict):
+                    a[k] = transform2list(v)
+                else:
+                    a[k] = [
+                        v,
+                    ]
+            return a
+
+        def merge(a: dict, b: dict, path=[], strict=False):
+            c = {}
+            keys = set(a.keys()).union(b.keys())
+            # print(keys)
+            for key in keys:
+                if key in a and key in b:
+                    if isinstance(a[key], dict) and isinstance(b[key], dict):
+                        # apply recursively
+                        c[key] = merge(a[key], b[key], path + [str(key)], strict=strict)
+                    else:
+                        c[key] = a[key] + b[key]
+                else:
+                    if strict:
+                        raise Exception("Conflict at " + ".".join(path + [str(key)]))
+                    c[key] = a[key] if key in a else b[key]
+            return c
+
+        tasks = (transform2list(_) for _ in batch)
+        return reduce(merge, tasks)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    import torch
+    import torch.distributed as dist
+    from torch.utils.data.distributed import DistributedSampler
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("data_path", nargs="?", type=str)  # , default=COYO_25M_VILA)
+    parser.add_argument("-o", "--overwrite", action="store_true")
+    parser.add_argument("--shards", type=int, default=0)
+    parser.add_argument("--total", type=int, default=0)
+    parser.add_argument("--test-all", action="store_true")
+    args = parser.parse_args()
+
+    print("Data path: ", args.data_path)
+    prepare_wids_meta(args.data_path, idx=args.shards, total=args.total)
+
+    if args.total > 0:
+        print("building meta information only")
+        exit(0)
+        
+    train_dataset = VILAWebDataset(
+        data_path=args.data_path,
+    )
+    # print("overwrite:", args.overwrite)
+    print("dataset size: ", len(train_dataset))
+    print(train_dataset[0])
+    
+    if args.test_all:
+        print("iterating all dataset for data integrity.")
+        train_dataset = VILAWebDataset(
+            data_path=args.data_path,
+            # cache_dir="~/.cache/simplecoyo",
+            # overwrite=args.overwrite,
+        )
+
+        sampler = None
+        from collections import defaultdict
+
+        from PIL import Image
+
+        dloader = torch.utils.data.DataLoader(
+            train_dataset,
+            shuffle=False,
+            sampler=sampler,
+            batch_size=8,
+            collate_fn=VILAWebDataset.custom_collate,
+            num_workers=8,
+        )
+        # dloader = train_dataset
+        # sampler.set_epoch(0)
+        print(len(train_dataset), len(dloader))
+        count = 0
+        for idx, data in enumerate(dloader):
+            if ".json" in data and ".mp4" in data:
+                print(f"{idx}-of-{len(dloader)}", type(data), count)
+            else:
+                count += 1
+            
+            # if idx >= 5:
+            #     break
diff --git a/llava/data/utils.py b/llava/data/utils.py
new file mode 100644
index 00000000..46beef0b
--- /dev/null
+++ b/llava/data/utils.py
@@ -0,0 +1,22 @@
+import io
+import pathlib
+from io import BytesIO
+
+from iopath.common.file_io import g_pathmgr
+from pytorchvideo.data.encoded_video import EncodedVideo, select_video_class
+
+
+class VILAEncodedVideo(EncodedVideo):
+    @classmethod
+    def from_bytesio(cls, file_path: str, decode_audio: bool = True, decoder: str = "pyav"):
+        if isinstance(file_path, io.BytesIO):
+            video_file = file_path
+            file_path = "tmp.mp4"
+        elif isinstance(file_path, str):
+            # We read the file with PathManager so that we can read from remote uris.
+            with g_pathmgr.open(file_path, "rb") as fh:
+                video_file = io.BytesIO(fh.read())
+        else:
+            print(f"unsupported type {type(file_path)}")
+        video_cls = select_video_class(decoder)
+        return video_cls(video_file, pathlib.Path(file_path).name, decode_audio)
diff --git a/llava/eval/eval_gpt_review.py b/llava/eval/eval_gpt_review.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_gpt_review_bench.py b/llava/eval/eval_gpt_review_bench.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_gpt_review_visual.py b/llava/eval/eval_gpt_review_visual.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_mmmu.py b/llava/eval/eval_mmmu.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_mmvet.py b/llava/eval/eval_mmvet.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_pope.py b/llava/eval/eval_pope.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_science_qa.py b/llava/eval/eval_science_qa.py
old mode 100644
new mode 100755
index c61a8711..ec0b95a6
--- a/llava/eval/eval_science_qa.py
+++ b/llava/eval/eval_science_qa.py
@@ -66,6 +66,9 @@ def get_pred_idx(prediction, choices, options):
 
         if pred_text in args.options:
             answer = pred_text
+        elif len(pred_text) >= 2 and pred_text[0] in args.options and pred_text[1:2] == ".":
+            # kentang-mit@: update this because some llama3 output has this format.
+            answer = pred_text[0]
         elif len(pred_text) >= 3 and pred_text[0] in args.options and pred_text[1:3] == ". ":
             answer = pred_text[0]
         else:
diff --git a/llava/eval/eval_science_qa_gpt4.py b/llava/eval/eval_science_qa_gpt4.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_science_qa_gpt4_requery.py b/llava/eval/eval_science_qa_gpt4_requery.py
old mode 100644
new mode 100755
diff --git a/llava/eval/eval_textvqa.py b/llava/eval/eval_textvqa.py
old mode 100644
new mode 100755
diff --git a/llava/eval/evaluate_vqa.py b/llava/eval/evaluate_vqa.py
new file mode 100755
index 00000000..ef1ec64a
--- /dev/null
+++ b/llava/eval/evaluate_vqa.py
@@ -0,0 +1,147 @@
+import argparse
+import json
+import os
+from typing import Optional
+import math
+
+from tqdm import tqdm
+
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.conversation import conv_templates
+from llava.model.builder import load_pretrained_model
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, get_model_name_from_path
+
+from PIL import Image
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+ds_collections = {
+    'docvqa_test': {
+        'test': './playground/data/eval/docvqa/test.jsonl',
+        'metric': None,
+        'max_new_tokens': 100,
+    },
+    'chartqa_test_human': {
+        'test': './playground/data/eval/chartqa/test_human.jsonl',
+        'metric': 'relaxed_accuracy',
+        'max_new_tokens': 100,
+    },
+    'chartqa_test_augmented': {
+        'test': './playground/data/eval/chartqa/test_augmented.jsonl',
+        'metric': 'relaxed_accuracy',
+        'max_new_tokens': 100,
+    },
+    'ocrvqa_val': {
+        'test': './playground/data/eval/ocrvqa/ocrvqa_val.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 100,
+    },
+    'ocrvqa_test': {
+        'test': './playground/data/eval/ocrvqa/ocrvqa_test.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 100,
+    },
+    'ai2diagram_test': {
+        'test': './playground/data/eval/ai2d/test.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 10,
+    }
+}
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--image-folder", type=str, default="")
+    parser.add_argument("--answers-file", type=str, default=None)
+    parser.add_argument('--dataset', type=str, default='')
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    args = parser.parse_args()
+
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base,)
+
+    questions = [json.loads(q) for q in open(os.path.expanduser(ds_collections[args.dataset]['test']), "r")]
+    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
+    outputs = []
+    for line in tqdm(questions):
+        qs = line["question"]
+        image_file = line['image']
+        question_id = line['question_id']
+        annotation = line['answer']
+        if model.config.mm_use_im_start_end:
+            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
+        else:
+            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+
+        conv = conv_templates[args.conv_mode].copy()
+        conv.append_message(conv.roles[0], qs)
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
+
+        image = Image.open(os.path.join(args.image_folder, image_file))
+        image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
+
+        pred = model.generate(
+            input_ids=input_ids.cuda(),
+            images=image_tensor.unsqueeze(0).half().cuda(),
+            do_sample=False,
+            num_beams=1,
+            max_new_tokens=ds_collections[args.dataset]['max_new_tokens'],
+            num_return_sequences=1,
+            use_cache=True
+        )
+
+        answer = tokenizer.batch_decode(pred, skip_special_tokens=True)[0]
+        answer = answer.strip()
+
+        if args.dataset in ['ocrvqa_val', 'ocrvqa_test']:
+            outputs.append({
+                'questionId': question_id,
+                'answer': answer,
+                'annotation': annotation,
+            })
+        elif args.dataset in ['ai2diagram_test']:
+            outputs.append({
+                'image': question_id,
+                'answer': answer,
+                'annotation': annotation,
+            })
+        elif args.dataset in ['chartqa_test_human', 'chartqa_test_augmented']:
+            outputs.append({
+                'answer': answer,
+                'annotation': annotation,
+            })
+        elif args.dataset in ['docvqa_test']:
+            outputs.append({
+                'questionId': question_id,
+                'answer': answer,
+            })
+        else:
+            raise NotImplementedError
+
+    answers_file = os.path.expanduser(args.answers_file)
+    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
+
+    with open(answers_file, 'w') as ans_file:
+        for output in outputs:
+            ans_file.write(json.dumps(output) + "\n")
+
diff --git a/llava/eval/evaluate_vqa_score.py b/llava/eval/evaluate_vqa_score.py
new file mode 100755
index 00000000..6b7777e9
--- /dev/null
+++ b/llava/eval/evaluate_vqa_score.py
@@ -0,0 +1,143 @@
+import argparse
+import json
+import time
+from typing import Optional
+
+ds_collections = {
+    'docvqa_test': {
+        'test': './playground/data/eval/docvqa/test.jsonl',
+        'metric': None,
+        'max_new_tokens': 100,
+    },
+    'chartqa_test_human': {
+        'test': './playground/data/eval/chartqa/test_human.jsonl',
+        'metric': 'relaxed_accuracy',
+        'max_new_tokens': 100,
+    },
+    'chartqa_test_augmented': {
+        'test': './playground/data/eval/chartqa/test_augmented.jsonl',
+        'metric': 'relaxed_accuracy',
+        'max_new_tokens': 100,
+    },
+    'ocrvqa_val': {
+        'test': './playground/data/eval/ocrvqa/ocrvqa_val.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 100,
+    },
+    'ocrvqa_test': {
+        'test': './playground/data/eval/ocrvqa/ocrvqa_test.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 100,
+    },
+    'ai2diagram_test': {
+        'test': './playground/data/eval/ai2d/test.jsonl',
+        'metric': 'accuracy',
+        'max_new_tokens': 10,
+    }
+}
+
+# https://github.com/google-research/pix2struct/blob/main/pix2struct/metrics.py#L81
+def relaxed_correctness(target: str,
+                        prediction: str,
+                        max_relative_change: float = 0.05) -> bool:
+    """Calculates relaxed correctness.
+
+    The correctness tolerates certain error ratio defined by max_relative_change.
+    See https://arxiv.org/pdf/2203.10244.pdf, end of section 5.1:
+    “Following Methani et al. (2020), we use a relaxed accuracy measure for the
+    numeric answers to allow a minor inaccuracy that may result from the automatic
+    data extraction process. We consider an answer to be correct if it is within
+    5% of the gold answer. For non-numeric answers, we still need an exact match
+    to consider an answer to be correct.”
+
+    Args:
+      target: Target string.
+      prediction: Predicted string.
+      max_relative_change: Maximum relative change.
+
+    Returns:
+      Whether the prediction was correct given the specified tolerance.
+    """
+
+    def _to_float(text: str) -> Optional[float]:
+        try:
+            if text.endswith('%'):
+                # Convert percentages to floats.
+                return float(text.rstrip('%')) / 100.0
+            else:
+                return float(text)
+        except ValueError:
+            return None
+
+    prediction_float = _to_float(prediction)
+    target_float = _to_float(target)
+    if prediction_float is not None and target_float:
+        relative_change = abs(prediction_float -
+                              target_float) / abs(target_float)
+        return relative_change <= max_relative_change
+    else:
+        return prediction.lower() == target.lower()
+
+
+def evaluate_relaxed_accuracy(entries):
+    scores = []
+    for elem in entries:
+        if isinstance(elem['annotation'], str):
+            elem['annotation'] = [elem['annotation']]
+        score = max([
+            relaxed_correctness(elem['answer'].strip(), ann)
+            for ann in elem['annotation']
+        ])
+        scores.append(score)
+    return sum(scores) / len(scores)
+
+
+def evaluate_exact_match_accuracy(entries):
+    scores = []
+    for elem in entries:
+        if isinstance(elem['annotation'], str):
+            elem['annotation'] = [elem['annotation']]
+        score = max([
+            (1.0 if
+             (elem['answer'].strip().lower() == ann.strip().lower()) else 0.0)
+            for ann in elem['annotation']
+        ])
+        scores.append(score)
+    return sum(scores) / len(scores)
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--answers-file", type=str, default=None)
+    parser.add_argument('--dataset', type=str, default='')
+    args = parser.parse_args()
+
+    print(f"Evaluating {args.dataset} ...")
+    outputs = [json.loads(q) for q in open(args.answers_file, "r")]
+
+    if ds_collections[args.dataset]['metric'] == 'relaxed_accuracy':
+        print({
+            'relaxed_accuracy': evaluate_relaxed_accuracy(outputs)
+        })
+    elif ds_collections[args.dataset]['metric'] == 'accuracy':
+        if 'gqa' in args.dataset:
+            for entry in outputs:
+                response = entry['answer']
+                response = response.strip().split('.')[0].split(
+                    ',')[0].split('!')[0].lower()
+                if 'is ' in response:
+                    response = response.split('is ')[1]
+                if 'are ' in response:
+                    response = response.split('are ')[1]
+                if 'a ' in response:
+                    response = response.split('a ')[1]
+                if 'an ' in response:
+                    response = response.split('an ')[1]
+                if 'the ' in response:
+                    response = response.split('the ')[1]
+                if ' of' in response:
+                    response = response.split(' of')[0]
+                response = response.strip()
+                entry['answer'] = response
+        print({'accuracy': evaluate_exact_match_accuracy(outputs)})
\ No newline at end of file
diff --git a/llava/eval/generate_webpage_data_from_table.py b/llava/eval/generate_webpage_data_from_table.py
old mode 100644
new mode 100755
diff --git a/llava/eval/m4c_evaluator.py b/llava/eval/m4c_evaluator.py
old mode 100644
new mode 100755
diff --git a/llava/eval/mathvista_utils/calculate_score.py b/llava/eval/mathvista_utils/calculate_score.py
new file mode 100755
index 00000000..68772fdf
--- /dev/null
+++ b/llava/eval/mathvista_utils/calculate_score.py
@@ -0,0 +1,204 @@
+import re
+import pandas as pd
+
+# !pip install python-Levenshtein
+from Levenshtein import distance
+
+import sys
+
+sys.path.append("../")
+from .utilities import *
+
+
+def get_most_similar(prediction, choices):
+    """
+    Use the Levenshtein distance (or edit distance) to determine which of the choices is most similar to the given prediction
+    """
+    distances = [distance(prediction, choice) for choice in choices]
+    ind = distances.index(min(distances))
+    return choices[ind]
+    # return min(choices, key=lambda choice: distance(prediction, choice))
+
+
+def normalize_extracted_answer(
+    extraction, choices, question_type, answer_type, precision
+):
+    """
+    Normalize the extracted answer to match the answer type
+    """
+    if question_type == "multi_choice":
+        # make sure the extraction is a string
+        if isinstance(extraction, str):
+            extraction = extraction.strip()
+        else:
+            try:
+                extraction = str(extraction)
+            except:
+                extraction = ""
+
+        # extract "A" from "(A) text"
+        letter = re.findall(r"\(([a-zA-Z])\)", extraction)
+        if len(letter) > 0:
+            extraction = letter[0].upper()
+
+        options = [chr(ord("A") + i) for i in range(len(choices))]
+
+        if extraction in options:
+            # convert option letter to text, e.g. "A" -> "text"
+            ind = options.index(extraction)
+            extraction = choices[ind]
+        else:
+            # select the most similar option
+            extraction = get_most_similar(extraction, choices)
+        assert extraction in choices
+
+    elif answer_type == "integer":
+        try:
+            extraction = str(int(float(extraction)))
+        except:
+            extraction = None
+
+    elif answer_type == "float":
+        try:
+            extraction = str(round(float(extraction), precision))
+        except:
+            extraction = None
+
+    elif answer_type == "list":
+        try:
+            extraction = str(extraction)
+        except:
+            extraction = None
+
+    return extraction
+
+
+def safe_equal(prediction, answer):
+    """
+    Check if the prediction is equal to the answer, even if they are of different types
+    """
+    try:
+        if prediction == answer:
+            return True
+        return False
+    except Exception as e:
+        print(e)
+        return False
+
+
+def get_acc_with_contion(res_pd, key, value):
+    if key == "skills":
+        # if value in res_pd[key]:
+        total_pd = res_pd[res_pd[key].apply(lambda x: value in x)]
+    else:
+        total_pd = res_pd[res_pd[key] == value]
+
+    correct_pd = total_pd[total_pd["true_false"] == True]
+    acc = "{:.2f}".format(len(correct_pd) / len(total_pd) * 100)
+    return len(correct_pd), len(total_pd), acc
+
+
+def simple_calculate_score(results: dict, scores_file: str):
+    full_pids = list(results.keys())
+    print("Number of testing problems:", len(full_pids))
+
+    ## [1] Evaluate if the prediction is true or false
+    print("\nEvaluating the predictions...")
+    for pid in full_pids:
+        problem = results[pid]
+        # print(problem)
+
+        choices = problem["choices"]
+        question_type = problem["question_type"]
+        answer_type = problem["answer_type"]
+        precision = problem["precision"]
+        extraction = problem["extraction"]
+
+        answer = problem["answer"]
+
+        # normalize the extracted answer to match the answer type
+        prediction = normalize_extracted_answer(
+            extraction, choices, question_type, answer_type, precision
+        )
+
+        # verify the prediction is true or false
+        true_false = safe_equal(prediction, answer)
+
+        problem["prediction"] = prediction
+        problem["true_false"] = true_false
+
+    ## [2] Calculate the average accuracy
+    total = len(full_pids)
+    correct = 0
+    for pid in full_pids:
+        if results[pid]["true_false"]:
+            correct += 1
+    accuracy = str(round(correct / total * 100, 2))
+    print(f"\nCorrect: {correct}, Total: {total}, Accuracy: {accuracy}%")
+
+    scores = {"average": {"accuracy": accuracy, "correct": correct, "total": total}}
+
+    ## [3] Calculate the fine-grained accuracy scores
+
+    # merge the 'metadata' attribute into the data
+    for pid in results:
+        results[pid].update(results[pid].pop("metadata"))
+
+    # convert the data to a pandas DataFrame
+    df = pd.DataFrame(results).T
+
+    print(len(df))
+    print("Number of test problems:", len(df))
+    # assert len(df) == 1000 # Important!!!
+
+    # asign the target keys for evaluation
+    target_keys = [
+        "question_type",
+        "answer_type",
+        "language",
+        "source",
+        "category",
+        "task",
+        "context",
+        "grade",
+        "skills",
+    ]
+
+    for key in target_keys:
+        print(f"\nType: [{key}]")
+        # get the unique values of the key
+        if key == "skills":
+            # the value is a list
+            values = []
+            for i in range(len(df)):
+                values += df[key][i]
+            values = list(set(values))
+        else:
+            values = df[key].unique()
+        # print(values)
+
+        # calculate the accuracy for each value
+        scores[key] = {}
+        for value in values:
+            correct, total, acc = get_acc_with_contion(df, key, value)
+            if total > 0:
+                print(f"[{value}]: {acc}% ({correct}/{total})")
+                scores[key][value] = {
+                    "accuracy": acc,
+                    "correct": correct,
+                    "total": total,
+                }
+
+        # sort the scores by accuracy
+        scores[key] = dict(
+            sorted(
+                scores[key].items(),
+                key=lambda item: float(item[1]["accuracy"]),
+                reverse=True,
+            )
+        )
+
+    # save the scores
+    print(f"\nSaving {scores_file}...")
+    save_json(scores, scores_file)
+    print("\nDone!")
diff --git a/llava/eval/mathvista_utils/extract_answer.py b/llava/eval/mathvista_utils/extract_answer.py
new file mode 100755
index 00000000..4ccdc349
--- /dev/null
+++ b/llava/eval/mathvista_utils/extract_answer.py
@@ -0,0 +1,81 @@
+import os
+import re
+
+from .utilities import *
+
+# OpenAI
+import openai
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+# print(openai.api_key)
+
+# load demo prompt
+from .prompts.ext_ans import demo_prompt
+
+
+def verify_extraction(extraction):
+    extraction = extraction.strip()
+    if extraction == "" or extraction == None:
+        return False
+    return True
+
+
+def create_test_prompt(demo_prompt, query, response):
+    demo_prompt = demo_prompt.strip()
+    test_prompt = f"{query}\n\n{response}"
+    full_prompt = f"{demo_prompt}\n\n{test_prompt}\n\nExtracted answer: "
+    return full_prompt
+
+
+def extract_answer(response, problem, quick_extract=False):
+    question_type = problem["question_type"]
+    answer_type = problem["answer_type"]
+    choices = problem["choices"]
+    query = problem["query"]
+    pid = problem["pid"]
+
+    if response == "":
+        return ""
+
+    if question_type == "multi_choice" and response in choices:
+        return response
+
+    if answer_type == "integer":
+        try:
+            extraction = int(response)
+            return str(extraction)
+        except:
+            pass
+
+    if answer_type == "float":
+        try:
+            extraction = str(float(response))
+            return extraction
+        except:
+            pass
+
+    # quick extraction
+    if quick_extract:
+        print("Quickly extracting answer...")
+        # The answer is "text". -> "text"
+        try:
+            result = re.search(r'The answer is "(.*)"\.', response)
+            if result:
+                extraction = result.group(1)
+                return extraction
+        except:
+            pass
+
+    # general extraction
+    try:
+        from openai import OpenAI
+
+        client = OpenAI()
+        full_prompt = create_test_prompt(demo_prompt, query, response)
+        extraction = get_chat_response(full_prompt, client)
+        return extraction
+    except Exception as e:
+        print(e)
+        print(f"Error in extracting answer for {pid}")
+
+    return ""
diff --git a/llava/eval/mathvista_utils/prompts/ext_ans.py b/llava/eval/mathvista_utils/prompts/ext_ans.py
new file mode 100755
index 00000000..964aedb4
--- /dev/null
+++ b/llava/eval/mathvista_utils/prompts/ext_ans.py
@@ -0,0 +1,42 @@
+
+
+# pids = 852,  104,  824,  506,  540
+
+demo_prompt = """
+Please read the following example. Then extract the answer from the model response and type it at the end of the prompt.
+
+Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end.
+Question: Which number is missing?
+
+Model response: The number missing in the sequence is 14.
+
+Extracted answer: 14
+
+Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value, e.g., 1.2, 1.3, 1.4, at the end.
+Question: What is the fraction of females facing the camera?
+
+Model response: The fraction of females facing the camera is 0.6, which means that six out of ten females in the group are facing the camera.
+
+Extracted answer: 0.6
+
+Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end.
+Question: How much money does Luca need to buy a sour apple candy and a butterscotch candy? (Unit: $)
+
+Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.
+
+Extracted answer: 1.45
+
+Hint: Please answer the question requiring a Python list as an answer and provide the final list, e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end.
+Question: Between which two years does the line  graph saw its maximum peak?
+
+Model response: The line graph saw its maximum peak between 2007 and 2008.
+
+Extracted answer: [2007, 2008]
+
+Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.
+Question: What fraction of the shape is blue?\nChoices:\n(A) 3/11\n(B) 8/11\n(C) 6/11\n(D) 3/5
+
+Model response: The correct answer is (B) 8/11.
+
+Extracted answer: B
+"""
\ No newline at end of file
diff --git a/llava/eval/mathvista_utils/utilities.py b/llava/eval/mathvista_utils/utilities.py
new file mode 100755
index 00000000..240258f3
--- /dev/null
+++ b/llava/eval/mathvista_utils/utilities.py
@@ -0,0 +1,213 @@
+import os
+import cv2
+import json
+import time
+import pickle
+import openai
+import re
+from word2number import w2n
+
+
+def create_dir(output_dir):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+
+def read_csv(file):
+    data = []
+    with open(file, "r") as f:
+        for line in f:
+            data.append(line.strip())
+    return data
+
+
+def read_pandas_csv(csv_path):
+    # read a pandas csv sheet
+    import pandas as pd
+
+    df = pd.read_csv(csv_path)
+    return df
+
+
+def read_json(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def read_jsonl(file):
+    with open(file, "r") as f:
+        data = [json.loads(line) for line in f]
+    return data
+
+
+def read_pickle(path):
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def save_json(data, path):
+    with open(path, "w") as f:
+        json.dump(data, f, indent=4)
+
+
+def save_array_img(path, image):
+    cv2.imwrite(path, image)
+
+
+def contains_digit(text):
+    # check if text contains a digit
+    if any(char.isdigit() for char in text):
+        return True
+    return False
+
+
+def contains_number_word(text):
+    # check if text contains a number word
+    ignore_words = ["a", "an", "point"]
+    words = re.findall(
+        r"\b\w+\b", text
+    )  # This regex pattern matches any word in the text
+    for word in words:
+        if word in ignore_words:
+            continue
+        try:
+            w2n.word_to_num(word)
+            return True  # If the word can be converted to a number, return True
+        except ValueError:
+            continue  # If the word can't be converted to a number, continue with the next word
+
+    # check if text contains a digit
+    if any(char.isdigit() for char in text):
+        return True
+
+    return False  # If none of the words could be converted to a number, return False
+
+
+def contains_quantity_word(text, special_keep_words=[]):
+    # check if text contains a quantity word
+    quantity_words = ["most", "least", "fewest"
+                      "more", "less", "fewer", 
+                      "largest", "smallest", "greatest", 
+                      "larger", "smaller", "greater", 
+                      "highest", "lowest", "higher", "lower",
+                      "increase", "decrease",
+                      "minimum", "maximum", "max", "min",
+                      "mean", "average", "median",
+                      "total", "sum", "add", "subtract",
+                      "difference", "quotient", "gap",
+                      "half", "double", "twice", "triple",
+                      "square", "cube", "root",
+                      "approximate", "approximation",
+                      "triangle", "rectangle", "circle", "square", "cube", "sphere", "cylinder", "cone", "pyramid",
+                      "multiply", "divide",
+                      "percentage", "percent", "ratio", "proportion", "fraction", "rate", 
+                    ]
+    
+    quantity_words += special_keep_words  # dataset specific words
+
+    words = re.findall(
+        r"\b\w+\b", text
+    )  # This regex pattern matches any word in the text
+    if any(word in quantity_words for word in words):
+        return True
+
+    return False  # If none of the words could be converted to a number, return False
+
+
+def is_bool_word(text):
+    if text in ["Yes", "No", "True", "False", 
+                "yes", "no", "true", "false", 
+                "YES", "NO", "TRUE", "FALSE"]:
+        return True
+    return False
+
+
+def is_digit_string(text):
+    # remove ".0000"
+    text = text.strip()
+    text = re.sub(r"\.0+$", "", text)
+    try:
+        int(text)
+        return True
+    except ValueError:
+        return False
+
+
+def is_float_string(text):
+    # text is a float string if it contains a "." and can be converted to a float
+    if "." in text:
+        try:
+            float(text)
+            return True
+        except ValueError:
+            return False
+    return False
+
+
+def copy_image(image_path, output_image_path):
+    from shutil import copyfile
+
+    copyfile(image_path, output_image_path)
+
+
+def copy_dir(src_dir, dst_dir):
+    from shutil import copytree
+
+    # copy the source directory to the target directory
+    copytree(src_dir, dst_dir)
+
+
+import PIL.Image as Image
+
+
+def get_image_size(img_path):
+    img = Image.open(img_path)
+    width, height = img.size
+    return width, height
+
+
+def get_chat_response(
+    prompt, client, model="gpt-3.5-turbo", temperature=0, max_tokens=256, n=1, patience=10000000,sleep_time=0,
+):
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    # print("I am here")
+    while patience > 0:
+        patience -= 1
+        try:
+            response = client.chat.completions.create(
+                model=model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                n=n,
+            )
+            if n == 1:
+                prediction = response.choices[0].message.content.strip()
+                if prediction != "" and prediction != None:
+                    return prediction
+            else:
+                prediction = [
+                    choice.message.content.strip() for choice in response.choices
+                ]
+                if prediction[0] != "" and prediction[0] != None:
+                    return prediction
+
+        except Exception as e:
+            if "Rate limit" not in str(e):
+                print(e)
+
+            if "Please reduce the length of the messages" in str(e):
+                print("!!Reduce promot size")
+                # reduce input prompt and keep the tail
+                new_size = int(len(promot) * 0.9)
+                new_start = len(promot) - new_size
+                promot = promot[new_start:]
+                messages = [
+                    {"role": "user", "content": promot},
+                ]
+
+            if sleep_time > 0:
+                time.sleep(sleep_time)
+    return ""
diff --git a/llava/eval/mmmu_utils/configs/llava1.5.yaml b/llava/eval/mmmu_utils/configs/llava1.5.yaml
old mode 100644
new mode 100755
diff --git a/llava/eval/mmmu_utils/data_utils.py b/llava/eval/mmmu_utils/data_utils.py
old mode 100644
new mode 100755
index 161d33da..c66cfd81
--- a/llava/eval/mmmu_utils/data_utils.py
+++ b/llava/eval/mmmu_utils/data_utils.py
@@ -2,58 +2,84 @@
 # https://github.com/MMMU-Benchmark/MMMU
 """Utils for data load, save, and process (e.g., prompt construction)"""
 
-import os
 import json
-import yaml
+import os
 import re
 
+import yaml
 
 DOMAIN_CAT2SUB_CAT = {
-  'Art and Design': ['Art', 'Art_Theory', 'Design', 'Music'],
-  'Business': ['Accounting', 'Economics', 'Finance', 'Manage','Marketing'],
-  'Science': ['Biology', 'Chemistry', 'Geography', 'Math', 'Physics',],
-  'Health and Medicine': ['Basic_Medical_Science', 'Clinical_Medicine', 'Diagnostics_and_Laboratory_Medicine', 'Pharmacy', 'Public_Health'],
-  'Humanities and Social Science': ['History', 'Literature', 'Sociology', 'Psychology'],
-  'Tech and Engineering': ['Agriculture', 'Architecture_and_Engineering', 'Computer_Science', 'Electronics', 'Energy_and_Power', 'Materials', 'Mechanical_Engineering'],
+    "Art and Design": ["Art", "Art_Theory", "Design", "Music"],
+    "Business": ["Accounting", "Economics", "Finance", "Manage", "Marketing"],
+    "Science": [
+        "Biology",
+        "Chemistry",
+        "Geography",
+        "Math",
+        "Physics",
+    ],
+    "Health and Medicine": [
+        "Basic_Medical_Science",
+        "Clinical_Medicine",
+        "Diagnostics_and_Laboratory_Medicine",
+        "Pharmacy",
+        "Public_Health",
+    ],
+    "Humanities and Social Science": [
+        "History",
+        "Literature",
+        "Sociology",
+        "Psychology",
+    ],
+    "Tech and Engineering": [
+        "Agriculture",
+        "Architecture_and_Engineering",
+        "Computer_Science",
+        "Electronics",
+        "Energy_and_Power",
+        "Materials",
+        "Mechanical_Engineering",
+    ],
 }
 
 
 CAT_SHORT2LONG = {
-    'acc': 'Accounting',
-    'agri': 'Agriculture',
-    'arch': 'Architecture_and_Engineering',
-    'art': 'Art',
-    'art_theory': 'Art_Theory',
-    'bas_med': 'Basic_Medical_Science',
-    'bio': 'Biology',
-    'chem': 'Chemistry',
-    'cli_med': 'Clinical_Medicine',
-    'cs': 'Computer_Science',
-    'design': 'Design',
-    'diag_med': 'Diagnostics_and_Laboratory_Medicine',
-    'econ': 'Economics',
-    'elec': 'Electronics',
-    'ep': 'Energy_and_Power',
-    'fin': 'Finance',
-    'geo': 'Geography',
-    'his': 'History',
-    'liter': 'Literature',
-    'manage': 'Manage',
-    'mark': 'Marketing',
-    'mate': 'Materials',
-    'math': 'Math',
-    'mech': 'Mechanical_Engineering',
-    'music': 'Music',
-    'phar': 'Pharmacy',
-    'phys': 'Physics',
-    'psy': 'Psychology',
-    'pub_health': 'Public_Health',
-    'socio': 'Sociology'
+    "acc": "Accounting",
+    "agri": "Agriculture",
+    "arch": "Architecture_and_Engineering",
+    "art": "Art",
+    "art_theory": "Art_Theory",
+    "bas_med": "Basic_Medical_Science",
+    "bio": "Biology",
+    "chem": "Chemistry",
+    "cli_med": "Clinical_Medicine",
+    "cs": "Computer_Science",
+    "design": "Design",
+    "diag_med": "Diagnostics_and_Laboratory_Medicine",
+    "econ": "Economics",
+    "elec": "Electronics",
+    "ep": "Energy_and_Power",
+    "fin": "Finance",
+    "geo": "Geography",
+    "his": "History",
+    "liter": "Literature",
+    "manage": "Manage",
+    "mark": "Marketing",
+    "mate": "Materials",
+    "math": "Math",
+    "mech": "Mechanical_Engineering",
+    "music": "Music",
+    "phar": "Pharmacy",
+    "phys": "Physics",
+    "psy": "Psychology",
+    "pub_health": "Public_Health",
+    "socio": "Sociology",
 }
 
+
 # DATA SAVING
 def save_json(filename, ds):
-    with open(filename, 'w') as f:
+    with open(filename, "w") as f:
         json.dump(ds, f, indent=4)
 
 
@@ -62,8 +88,8 @@ def get_multi_choice_info(options):
     Given the list of options for multiple choice question
     Return the index2ans and all_choices
     """
-    
-    start_chr = 'A'
+
+    start_chr = "A"
     all_choices = []
     index2ans = {}
     for i, option in enumerate(options):
@@ -72,8 +98,9 @@ def get_multi_choice_info(options):
 
     return index2ans, all_choices
 
+
 def load_yaml(file_path):
-    with open(file_path, 'r') as stream:
+    with open(file_path, "r") as stream:
         try:
             yaml_dict = yaml.safe_load(stream)
         except yaml.YAMLError as exc:
@@ -86,27 +113,35 @@ def parse_img_path(text):
     matches = re.findall("<img='(.*?)'>", text)
     return matches
 
+
 def process_single_sample(data):
-    question = data['question']
-    o_imgs_paths = []
-    for option in data['options']:
-        current_o_imgs_paths = parse_img_path(option)
-        for img_path in current_o_imgs_paths:
-            o_imgs_paths.append(img_path)
-
-    if len(o_imgs_paths) > 1:  # multiple images in options, used for random selection
-        return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'],
-             'image': None, 'question_type': data['question_type']}
-    else:
-        return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'],
-             'image': data['image_1'], 'question_type': data['question_type']}
+    question = data["question"]
+    options = eval(data["options"])
+    image_keys = []
+    candidates = [question] + options
+    ## process image
+    for i, c in enumerate(candidates):
+        matched_patterns = re.findall(r"<image \d*>", c)
+        image_keys += [pattern.strip("<>").replace(" ", "_") for pattern in matched_patterns]
+        for pattern in matched_patterns:
+            candidates[i] = candidates[i].replace(pattern, "<image>")
+
+    return {
+        "id": data["id"],
+        "question": candidates[0],
+        "options": str(candidates[1:]),
+        "answer": data["answer"],
+        "image": [data[key] for key in image_keys],
+        "question_type": data["question_type"],
+    }
 
 
 # DATA SAVING
 def save_json(filename, ds):
-    with open(filename, 'w') as f:
+    with open(filename, "w") as f:
         json.dump(ds, f, indent=4)
 
+
 def save_jsonl(filename, data):
     """
     Save a dictionary of data to a JSON Lines file with the filename as key and caption as value.
@@ -115,32 +150,32 @@ def save_jsonl(filename, data):
         filename (str): The path to the file where the data should be saved.
         data (dict): The dictionary containing the data to save where key is the image path and value is the caption.
     """
-    with open(filename, 'w', encoding='utf-8') as f:
+    with open(filename, "w", encoding="utf-8") as f:
         for img_path, caption in data.items():
             # Extract the base filename without the extension
             base_filename = os.path.basename(img_path)
             # Create a JSON object with the filename as the key and caption as the value
             json_record = json.dumps({base_filename: caption}, ensure_ascii=False)
             # Write the JSON object to the file, one per line
-            f.write(json_record + '\n')
+            f.write(json_record + "\n")
+
 
 def save_args(args, path_dir):
     argsDict = args.__dict__
-    with open(path_dir + 'setting.txt', 'w') as f:
-        f.writelines('------------------ start ------------------' + '\n')
+    with open(path_dir + "setting.txt", "w") as f:
+        f.writelines("------------------ start ------------------" + "\n")
         for eachArg, value in argsDict.items():
-            f.writelines(eachArg + ' : ' + str(value) + '\n')
-        f.writelines('------------------- end -------------------')
-
+            f.writelines(eachArg + " : " + str(value) + "\n")
+        f.writelines("------------------- end -------------------")
 
 
 # DATA PROCESSING
 def construct_prompt(sample, config):
-    question = sample['question']
-    options = eval(sample['options'])
+    question = sample["question"]
+    options = eval(sample["options"])
     example = ""
-    if sample['question_type'] == 'multiple-choice':
-        start_chr = 'A'
+    if sample["question_type"] == "multiple-choice":
+        start_chr = "A"
         prediction_range = []
         index2ans = {}
         for option in options:
@@ -148,29 +183,29 @@ def construct_prompt(sample, config):
             example += f"({start_chr}) {option}\n"
             index2ans[start_chr] = option
             start_chr = chr(ord(start_chr) + 1)
-        empty_prompt_sample_structure = config['multi_choice_example_format']
+        empty_prompt_sample_structure = config["multi_choice_example_format"]
         empty_prompt = empty_prompt_sample_structure.format(question, example)
         res_dict = {}
-        res_dict['index2ans'] = index2ans
-        res_dict['correct_choice'] = sample['answer']
-        res_dict['all_choices'] = prediction_range
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'].strip() + '\n\n' + empty_prompt
+        res_dict["index2ans"] = index2ans
+        res_dict["correct_choice"] = sample["answer"]
+        res_dict["all_choices"] = prediction_range
+        res_dict["empty_prompt"] = empty_prompt
+        if config["task_instructions"]:
+            res_dict["final_input_prompt"] = config["task_instructions"].strip() + "\n\n" + empty_prompt
         else:
-            res_dict['final_input_prompt'] = empty_prompt
+            res_dict["final_input_prompt"] = empty_prompt
 
-        res_dict['gt_content'] = options[ord(sample['answer'].upper()) - ord('A')]
+        res_dict["gt_content"] = options[ord(sample["answer"].upper()) - ord("A")]
     else:
-        empty_prompt_sample_structure = config['short_ans_example_format']
+        empty_prompt_sample_structure = config["short_ans_example_format"]
         empty_prompt = empty_prompt_sample_structure.format(question)
         res_dict = {}
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'].strip() + '\n\n' + empty_prompt
+        res_dict["empty_prompt"] = empty_prompt
+        if config["task_instructions"]:
+            res_dict["final_input_prompt"] = config["task_instructions"].strip() + "\n\n" + empty_prompt
         else:
-            res_dict['final_input_prompt'] = empty_prompt
-        res_dict['gt_content'] = sample['answer']
+            res_dict["final_input_prompt"] = empty_prompt
+        res_dict["gt_content"] = sample["answer"]
 
     res_dict.update(sample)
     return res_dict
diff --git a/llava/eval/mmmu_utils/eval_utils.py b/llava/eval/mmmu_utils/eval_utils.py
old mode 100644
new mode 100755
diff --git a/llava/eval/mmmu_utils/merge_jsons.py b/llava/eval/mmmu_utils/merge_jsons.py
new file mode 100644
index 00000000..f199ff75
--- /dev/null
+++ b/llava/eval/mmmu_utils/merge_jsons.py
@@ -0,0 +1,22 @@
+# python llava/eval/mmmu_utils/merge_jsons.py --prediction-path ./playground/data/eval/MMMU/validation_results/$CKPT --num-chunks $CHUNKS
+
+import argparse
+import json
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--prediction-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    args = parser.parse_args()
+    prediction_paths = [
+        f"{args.prediction_path}-{chunk}.json" for chunk in range(args.num_chunks)
+    ]
+    combined_results = dict()
+    for path in prediction_paths:
+        with open(path, "r") as f:
+            partial_results = json.load(f)
+        combined_results.update(**partial_results)
+    print(len(combined_results))
+    result_str = json.dumps(combined_results, indent=2)
+    with open(f"{args.prediction_path}.json", "w") as f:
+        f.write(result_str)
diff --git a/llava/eval/mmmu_utils/model_utils.py b/llava/eval/mmmu_utils/model_utils.py
old mode 100644
new mode 100755
index 4c9828da..c611b5e9
--- a/llava/eval/mmmu_utils/model_utils.py
+++ b/llava/eval/mmmu_utils/model_utils.py
@@ -1,15 +1,19 @@
 # This file is originated from the official MMMU codebase:
 # https://github.com/MMMU-Benchmark/MMMU
-from random import random
+import random
+
 import torch
-from llava.mm_utils import is_gemma_tokenizer, KeywordsStoppingCriteria
+
+from llava.mm_utils import KeywordsStoppingCriteria, is_gemma_tokenizer
+
 
 def call_llava_engine_df(args, sample, model, tokenizer=None, processor=None):
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-    from llava.conversation import conv_templates, SeparatorStyle
+    from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                                 DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX)
+    from llava.conversation import SeparatorStyle, conv_templates
 
     def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
-        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
+        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("<image>")]
 
         def insert_separator(X, sep):
             return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
@@ -24,30 +28,35 @@ def insert_separator(X, sep):
             input_ids.extend(x[offset:])
 
         if return_tensors is not None:
-            if return_tensors == 'pt':
+            if return_tensors == "pt":
                 return torch.tensor(input_ids, dtype=torch.long)
-            raise ValueError(f'Unsupported tensor type: {return_tensors}')
+            raise ValueError(f"Unsupported tensor type: {return_tensors}")
         return input_ids
 
     def deal_with_prompt(input_text, mm_use_im_start_end):
         qs = input_text
+        if DEFAULT_IMAGE_TOKEN not in qs:
+            qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
         if mm_use_im_start_end:
-            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
-        else:
-            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
+            qs.replace(DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN)
         return qs
 
-    prompt = sample['final_input_prompt']
+    prompt = sample["final_input_prompt"]
     prompt = deal_with_prompt(prompt, model.config.mm_use_im_start_end)
-    conv = conv_templates['vicuna_v1'].copy()
+    conv = conv_templates[args.conv_mode].copy()
     conv.append_message(conv.roles[0], prompt)
     conv.append_message(conv.roles[1], None)
     prompt = conv.get_prompt()
-    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-    image = sample['image']
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()
+    image = sample["image"]
+
     stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
     keywords = [stop_str]
-    stopping_criteria = [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)] if args.conv_mode == "v0" or is_gemma_tokenizer(tokenizer) else None
+    stopping_criteria = (
+        [KeywordsStoppingCriteria(keywords, tokenizer, input_ids)]
+        if args.conv_mode == "v0" or is_gemma_tokenizer(tokenizer)
+        else None
+    )
     if image is not None:
         output_ids = model.generate(
             input_ids,
@@ -58,23 +67,23 @@ def deal_with_prompt(input_text, mm_use_im_start_end):
             num_beams=5,
             max_new_tokens=128,
             use_cache=True,
-            stopping_criteria=stopping_criteria,)
+            stopping_criteria=stopping_criteria,
+        )
 
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        response = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+        return outputs
     else:  # multiple images actually
-        if sample['question_type'] == 'multiple-choice':
-            all_choices = sample['all_choices']
-            response = random.choice(all_choices)
+        raise ValueError("INVALID GENERATION FOR MULTIPLE IMAGE INPUTS")
+        # default behavior (random sample answer) from MMMU's offcials implementation
+        if sample["question_type"] == "multiple-choice":
+            all_choices = sample["all_choices"]
+            outputs = random.choice(all_choices)
         else:
-            response = 'INVALID GENERATION FOR MULTIPLE IMAGE INPUTS'
+            outputs = "INVALID GENERATION FOR MULTIPLE IMAGE INPUTS"
 
-    return response
+    return outputs
 
 
 def llava_image_processor(raw_image, vis_processors=None):
-    image_tensor = vis_processors.preprocess(raw_image, return_tensors='pt')['pixel_values'][0]
-    return image_tensor
\ No newline at end of file
+    image_tensor = vis_processors.preprocess(raw_image, return_tensors="pt")["pixel_values"][0]
+    return image_tensor
diff --git a/llava/eval/model_qa.py b/llava/eval/model_qa.py
old mode 100644
new mode 100755
index dc037468..8503def4
--- a/llava/eval/model_qa.py
+++ b/llava/eval/model_qa.py
@@ -60,6 +60,7 @@ def eval_model(model_name, questions_file, answers_file):
             temperature=0.7,
             max_new_tokens=1024,
             stopping_criteria=[stopping_criteria])
+
         outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
         try:
             index = outputs.index(conv.sep, len(prompt))
diff --git a/llava/eval/model_vqa.py b/llava/eval/model_vqa.py
old mode 100644
new mode 100755
index 48769c40..a8e7733b
--- a/llava/eval/model_vqa.py
+++ b/llava/eval/model_vqa.py
@@ -33,7 +33,7 @@ def eval_model(args):
     disable_torch_init()
     model_path = os.path.expanduser(args.model_path)
     model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
 
     questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
     questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
@@ -76,11 +76,7 @@ def eval_model(args):
                 max_new_tokens=1024,
                 use_cache=True)
 
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
         outputs = outputs.strip()
         if outputs.endswith(stop_str):
             outputs = outputs[:-len(stop_str)]
diff --git a/llava/eval/model_vqa_loader.py b/llava/eval/model_vqa_loader.py
old mode 100644
new mode 100755
index c757b0dc..defa486b
--- a/llava/eval/model_vqa_loader.py
+++ b/llava/eval/model_vqa_loader.py
@@ -76,7 +76,7 @@ def eval_model(args):
     disable_torch_init()
     model_path = os.path.expanduser(args.model_path)
     model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
 
     questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
     questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
@@ -113,11 +113,7 @@ def eval_model(args):
                 use_cache=True,
                 stopping_criteria=stopping_criteria,)
 
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
         outputs = outputs.strip()
 
         ans_id = shortuuid.uuid()
diff --git a/llava/eval/model_vqa_mmbench.py b/llava/eval/model_vqa_mmbench.py
old mode 100644
new mode 100755
index 4637ed13..51a9ad2b
--- a/llava/eval/model_vqa_mmbench.py
+++ b/llava/eval/model_vqa_mmbench.py
@@ -58,7 +58,7 @@ def eval_model(args):
     disable_torch_init()
     model_path = os.path.expanduser(args.model_path)
     model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
 
     questions = pd.read_table(os.path.expanduser(args.question_file))
     questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
@@ -127,11 +127,7 @@ def eval_model(args):
                     use_cache=True,
                     stopping_criteria=stopping_criteria,)
 
-            input_token_len = input_ids.shape[1]
-            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-            if n_diff_input_output > 0:
-                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+            outputs = outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
             outputs = outputs.strip()
             if outputs.endswith(stop_str):
                 outputs = outputs[:-len(stop_str)]
diff --git a/llava/eval/model_vqa_mmmu.py b/llava/eval/model_vqa_mmmu.py
old mode 100644
new mode 100755
index a714e193..57ffdd14
--- a/llava/eval/model_vqa_mmmu.py
+++ b/llava/eval/model_vqa_mmmu.py
@@ -1,21 +1,26 @@
 # This file is originated from the official MMMU codebase:
 # https://github.com/MMMU-Benchmark/MMMU
-import torch
 import os
 import random
+from argparse import ArgumentParser
 
 import numpy as np
+import torch
+from datasets import concatenate_datasets, load_dataset
 from tqdm import tqdm
 
-from datasets import load_dataset, concatenate_datasets
-from llava.model.builder import load_pretrained_model
+from llava.eval.mmmu_utils.data_utils import (CAT_SHORT2LONG, construct_prompt,
+                                              load_yaml, process_single_sample,
+                                              save_json)
+from llava.eval.mmmu_utils.eval_utils import (parse_multi_choice_response,
+                                              parse_open_response)
+from llava.eval.mmmu_utils.model_utils import (call_llava_engine_df,
+                                               llava_image_processor)
 from llava.mm_utils import get_model_name_from_path
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import process_images
 
-from argparse import ArgumentParser
 
-from llava.eval.mmmu_utils.data_utils import load_yaml, construct_prompt, save_json, process_single_sample, CAT_SHORT2LONG
-from llava.eval.mmmu_utils.model_utils import call_llava_engine_df, llava_image_processor
-from llava.eval.mmmu_utils.eval_utils import parse_multi_choice_response, parse_open_response
 
 
 def run_model(args, samples, model, call_model_engine_fn=None, tokenizer=None, processor=None, conv_version=None):
@@ -24,13 +29,14 @@ def run_model(args, samples, model, call_model_engine_fn=None, tokenizer=None, p
         for sample in tqdm(samples):
             response = call_model_engine_fn(args, sample, model, tokenizer, processor)
 
-            if sample['question_type'] == 'multiple-choice':
-                pred_ans = parse_multi_choice_response(response, sample['all_choices'], sample['index2ans'])
+            if sample["question_type"] == "multiple-choice":
+                pred_ans = parse_multi_choice_response(response, sample["all_choices"], sample["index2ans"])
             else:  # open question
                 pred_ans = response
-            out_samples[sample['id']] = pred_ans
+            out_samples[sample["id"]] = pred_ans
     return out_samples
 
+
 def set_seed(seed_value):
     """
     Set the seed for PyTorch (both CPU and CUDA), Python, and NumPy for reproducible results.
@@ -46,22 +52,22 @@ def set_seed(seed_value):
     torch.backends.cudnn.deterministic = True
     torch.backends.cudnn.benchmark = False
 
+
 def main():
     parser = ArgumentParser()
-    parser.add_argument('--output_path', type=str, default='llava1.5_13b_val.json',
-                        help='name of saved json')
-    parser.add_argument('--config_path', type=str, default="llava/eval/mmmu_utils/configs/llava1.5.yaml")
-    parser.add_argument('--data_path', type=str, default="playground/data/eval/MMMU") # hf dataset path.
-    parser.add_argument('--model_path', type=str, default="liuhaotian/llava-v1.5-13b")
+    parser.add_argument("--output_path", type=str, default="llava1.5_13b_val.json", help="name of saved json")
+    parser.add_argument("--config_path", type=str, default="llava/eval/mmmu_utils/configs/llava1.5.yaml")
+    parser.add_argument("--data_path", type=str, default="playground/data/eval/MMMU")  # hf dataset path.
+    parser.add_argument("--model_path", type=str, default="liuhaotian/llava-v1.5-13b")
     parser.add_argument("--conv-mode", type=str, default="llava_v1")
-    parser.add_argument('--split', type=str, default='validation')
-    parser.add_argument('--seed', type=int, default=42)
+    parser.add_argument("--split", type=str, default="validation")
+    parser.add_argument("--seed", type=int, default=42)
 
     args = parser.parse_args()
     device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
     set_seed(args.seed)
 
-    print('llava_initializing...')
+    print("llava_initializing...")
     processor = None
     call_model_engine = call_llava_engine_df
     vis_process_func = llava_image_processor
@@ -69,8 +75,8 @@ def main():
     # load config and process to one value
     args.config = load_yaml(args.config_path)
     for key, value in args.config.items():
-        if key != 'eval_params' and type(value) == list:
-            assert len(value) == 1, 'key {} has more than one value'.format(key)
+        if key != "eval_params" and type(value) == list:
+            assert len(value) == 1, "key {} has more than one value".format(key)
             args.config[key] = value[0]
 
     # run for each subject
@@ -82,19 +88,17 @@ def main():
     # merge all dataset
     dataset = concatenate_datasets(sub_dataset_list)
 
-
     # load model
     model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, vis_processors, _ = load_pretrained_model(args.model_path, None,
-                                                                model_name)
+    tokenizer, model, vis_processors, _ = load_pretrained_model(args.model_path, model_name, None)
 
     samples = []
     for sample in dataset:
         sample = process_single_sample(sample)
 
         sample = construct_prompt(sample, args.config)
-        if sample['image']:
-            sample['image'] = vis_process_func(sample['image'], vis_processors).to(device)
+        if sample["image"]:
+            sample['image'] = process_images([image.convert("RGB") for image in sample['image']], vis_processors, model.config)
         samples.append(sample)
 
     # run ex
@@ -107,5 +111,5 @@ def main():
     # save_json(save_result_path, metric_dict)
 
 
-if __name__ == '__main__':
-    main()
\ No newline at end of file
+if __name__ == "__main__":
+    main()
diff --git a/llava/eval/model_vqa_mmmu_sharded.py b/llava/eval/model_vqa_mmmu_sharded.py
new file mode 100644
index 00000000..7f2dbeb3
--- /dev/null
+++ b/llava/eval/model_vqa_mmmu_sharded.py
@@ -0,0 +1,162 @@
+# This file is originated from the official MMMU codebase:
+# https://github.com/MMMU-Benchmark/MMMU
+import torch
+import os
+import random
+
+import math
+import numpy as np
+from tqdm import tqdm
+
+from datasets import load_dataset, concatenate_datasets
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path
+from llava.mm_utils import process_images
+
+from argparse import ArgumentParser
+
+from llava.eval.mmmu_utils.data_utils import (
+    load_yaml,
+    construct_prompt,
+    save_json,
+    process_single_sample,
+    CAT_SHORT2LONG,
+)
+from llava.eval.mmmu_utils.model_utils import (
+    call_llava_engine_df,
+    llava_image_processor,
+)
+from llava.eval.mmmu_utils.eval_utils import (
+    parse_multi_choice_response,
+    parse_open_response,
+)
+
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i : i + chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def run_model(
+    args,
+    samples,
+    model,
+    call_model_engine_fn=None,
+    tokenizer=None,
+    processor=None,
+    conv_version=None,
+):
+    out_samples = dict()
+    with torch.no_grad():
+        for sample in tqdm(samples):
+            response = call_model_engine_fn(args, sample, model, tokenizer, processor)
+
+            if sample["question_type"] == "multiple-choice":
+                pred_ans = parse_multi_choice_response(
+                    response, sample["all_choices"], sample["index2ans"]
+                )
+            else:  # open question
+                pred_ans = response
+            out_samples[sample["id"]] = pred_ans
+    return out_samples
+
+
+def set_seed(seed_value):
+    """
+    Set the seed for PyTorch (both CPU and CUDA), Python, and NumPy for reproducible results.
+
+    :param seed_value: An integer value to be used as the seed.
+    """
+    torch.manual_seed(seed_value)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed_value)
+        torch.cuda.manual_seed_all(seed_value)  # For multi-GPU setups
+    random.seed(seed_value)
+    np.random.seed(seed_value)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "--output_path",
+        type=str,
+        default="llava1.5_13b_val.json",
+        help="name of saved json",
+    )
+    parser.add_argument(
+        "--config_path", type=str, default="llava/eval/mmmu_utils/configs/llava1.5.yaml"
+    )
+    parser.add_argument(
+        "--data_path", type=str, default="playground/data/eval/MMMU"
+    )  # hf dataset path.
+    parser.add_argument("--model_path", type=str, default="liuhaotian/llava-v1.5-13b")
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--split", type=str, default="validation")
+    parser.add_argument("--seed", type=int, default=42)
+
+    args = parser.parse_args()
+    device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
+    set_seed(args.seed)
+
+    print("llava_initializing...")
+    processor = None
+    call_model_engine = call_llava_engine_df
+    vis_process_func = llava_image_processor
+
+    # load config and process to one value
+    args.config = load_yaml(args.config_path)
+    for key, value in args.config.items():
+        if key != "eval_params" and type(value) == list:
+            assert len(value) == 1, "key {} has more than one value".format(key)
+            args.config[key] = value[0]
+
+    # run for each subject
+    sub_dataset_list = []
+    print(len(list(CAT_SHORT2LONG.values())))
+    for subject in CAT_SHORT2LONG.values():
+        sub_dataset = load_dataset(args.data_path, subject, split=args.split)
+        sub_dataset_list.append(sub_dataset)
+
+    # merge all dataset
+    dataset = concatenate_datasets(sub_dataset_list)
+    dataset = list(dataset)
+    dataset = get_chunk(dataset, args.num_chunks, args.chunk_idx)
+
+    # load model
+    model_name = get_model_name_from_path(args.model_path)
+    tokenizer, model, vis_processors, _ = load_pretrained_model(
+        args.model_path, model_name, None
+    )
+
+    samples = []
+    for sample in dataset:
+        sample = process_single_sample(sample)
+
+        sample = construct_prompt(sample, args.config)
+        if sample["image"]:
+            sample['image'] = process_images([image.convert("RGB") for image in sample['image']], vis_processors, model.config)
+        samples.append(sample)
+
+    # run ex
+    # TODO (kentang-mit@): for other backbones such as mistral, we may still need this.
+    # conv_version = args.conv_mode
+    out_samples = run_model(
+        args, samples, model, call_model_engine, tokenizer, processor
+    )
+    os.makedirs("/".join(args.output_path.split("/")[:-1]), exist_ok=True)
+    output_path = args.output_path.replace(".json", f"-{args.chunk_idx}.json")
+    save_json(output_path, out_samples)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/llava/eval/model_vqa_nextqa.py b/llava/eval/model_vqa_nextqa.py
new file mode 100755
index 00000000..53fbad7e
--- /dev/null
+++ b/llava/eval/model_vqa_nextqa.py
@@ -0,0 +1,199 @@
+# This file is modified from https://github.com/haotian-liu/LLaVA/
+
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.data.dataset import LazySupervisedDataset
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+from llava.mm_utils import process_images
+
+from PIL import Image
+import math
+import numpy as np
+
+from torchvision.transforms import Resize
+from pytorchvideo.data.encoded_video import EncodedVideo
+
+import signal
+
+
+# This function will be called when the timeout is reached
+def handler(signum, frame):
+    raise TimeoutError()
+# Set the signal handler
+signal.signal(signal.SIGALRM, handler)
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def get_model_output(model, image_processor, tokenizer, video_path, qs, args):
+
+    num_video_frames = model.config.num_video_frames
+    images, video_loading_succeed = LazySupervisedDataset._load_video(video_path, num_video_frames, args)
+    image_tensor = process_images(images, image_processor, model.config)
+
+    qs = '<image>\n' * num_video_frames + qs
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    input_ids = tokenizer_image_token(
+        prompt,
+        tokenizer,
+        image_token_index=IMAGE_TOKEN_INDEX,
+        return_tensors="pt",
+    )
+    input_ids = torch.unsqueeze(input_ids, 0)
+    input_ids = torch.as_tensor(input_ids).cuda()
+
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor.half().cuda(),
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria]
+        )
+
+    input_token_len = input_ids.shape[1]
+    # n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    # if n_diff_input_output > 0:
+    #     print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+    outputs = outputs.strip()
+    if outputs.endswith(stop_str):
+        outputs = outputs[:-len(stop_str)]
+    outputs = outputs.strip()
+    return outputs
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
+    args.image_processor = image_processor
+
+    # Read the ground truth csv file
+    gt_file = os.path.expanduser(args.gt_file)
+    with open(gt_file, 'r') as f:
+        gt_qa_data = f.readlines()
+    # convert the csv data into a list of dictionaries
+    gt_questions = []
+    gt_answers = []
+    for line in gt_qa_data[1:]:
+        # ,video,frame_count,width,height,question,answer,qid,type
+        line = line.strip()
+        line = line.split(',')
+        gt_questions.append({'video_name': line[1], 'question': line[5], 'question_id': line[7]})
+        gt_answers.append({'answer': line[6]})
+    gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx)
+    gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx)
+
+    args.output_dir = os.path.expanduser(args.output_dir)
+    print(f"Output directory: {args.output_dir}")
+    args.video_dir = os.path.expanduser(args.video_dir)
+    print(f"Video directory: {args.video_dir}")
+    answers_file = os.path.join(args.output_dir, f"{args.output_name}.json")
+    os.makedirs(args.output_dir, exist_ok=True)
+    # Read cache answer file, each line is a json object
+    if os.path.exists(answers_file):
+        cache_ans_file = open(answers_file, "r")
+        cache_ans = cache_ans_file.readlines()
+        cache_ans_file.close()
+    else:
+        cache_ans = []
+
+    # Get cached video ids
+    cache_set = set([f"{json.loads(line)['video_name']}_{json.loads(line)['id']}" for line in cache_ans])
+        
+
+    # Create the output directory if it doesn't exist
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+    
+    # List to store the output results
+    output_list = [] 
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+
+
+    # Iterate over each sample in the ground truth file
+    index = 0
+    for sample in tqdm(gt_questions):
+        video_name = sample['video_name']
+        question = sample['question']
+        id = sample['question_id']
+        answer = gt_answers[index]['answer']
+        index += 1
+
+        sample_set = {
+            "video_name": video_name,
+            'id': id, 
+            'question': question, 
+            'answer': answer,
+        }
+
+        # Load the video file
+        for fmt in tqdm(video_formats):  # Added this line
+
+            temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+            if f"{video_name}_{id}" in cache_set:
+                print(f"Skipping {video_name}_{id} because it is in the cache")
+                continue
+            if os.path.exists(temp_path):
+                video_path = temp_path
+                # try:
+                # Run inference on the video and add the output to the list
+                output = get_model_output(model, image_processor, tokenizer, video_path, question, args)
+                sample_set['pred'] = output
+                output_list.append(sample_set)
+                # except Exception as e:
+                #     print(f"Error processing video file '{video_name}': {e}")
+                    # Write into the answer file.
+                with open(answers_file, 'a') as f:
+                    f.write(json.dumps(sample_set) + "\n")
+                break
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file', help='Path to the ground truth file containing question.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/model_vqa_qbench.py b/llava/eval/model_vqa_qbench.py
old mode 100644
new mode 100755
index b5c70f1b..b5643250
--- a/llava/eval/model_vqa_qbench.py
+++ b/llava/eval/model_vqa_qbench.py
@@ -32,7 +32,7 @@ def eval_model(args):
     disable_torch_init()
 
     model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, True)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, model_name, args.model_base, True)
 
 
 
@@ -97,11 +97,7 @@ def eval_model(args):
                 use_cache=True,
                 stopping_criteria=[stopping_criteria])
         
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
         outputs = outputs.strip()
         if outputs.endswith(stop_str):
             outputs = outputs[:-len(stop_str)]
diff --git a/llava/eval/model_vqa_science.py b/llava/eval/model_vqa_science.py
old mode 100644
new mode 100755
index c90e7533..07ac6ecd
--- a/llava/eval/model_vqa_science.py
+++ b/llava/eval/model_vqa_science.py
@@ -33,7 +33,7 @@ def eval_model(args):
     disable_torch_init()
     model_path = os.path.expanduser(args.model_path)
     model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
     questions = json.load(open(os.path.expanduser(args.question_file), "r"))
     questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
     answers_file = os.path.expanduser(args.answers_file)
@@ -84,11 +84,7 @@ def eval_model(args):
                 stopping_criteria=stopping_criteria,
             )
 
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+        outputs = outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
         outputs = outputs.strip()
         if outputs.endswith(stop_str):
             outputs = outputs[:-len(stop_str)]
@@ -110,11 +106,7 @@ def eval_model(args):
                     use_cache=True,
                     stopping_criteria=[stopping_criteria])
 
-            input_token_len = input_ids.shape[1]
-            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-            if n_diff_input_output > 0:
-                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+            outputs = outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
             outputs = outputs.strip()
             if outputs.endswith(stop_str):
                 outputs = outputs[:-len(stop_str)]
diff --git a/llava/eval/model_vqa_video.py b/llava/eval/model_vqa_video.py
old mode 100644
new mode 100755
index 34156f09..0dffd6e2
--- a/llava/eval/model_vqa_video.py
+++ b/llava/eval/model_vqa_video.py
@@ -10,17 +10,25 @@
 from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
 from llava.conversation import conv_templates, SeparatorStyle
 from llava.model.builder import load_pretrained_model
+from llava.data.dataset import LazySupervisedDataset
 from llava.utils import disable_torch_init
 from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+from llava.mm_utils import process_images
 
 from PIL import Image
 import math
 import numpy as np
 
 from torchvision.transforms import Resize
-import decord
-from decord import VideoReader
+from pytorchvideo.data.encoded_video import EncodedVideo
 
+import signal
+
+# This function will be called when the timeout is reached
+def handler(signum, frame):
+    raise TimeoutError()
+# Set the signal handler
+signal.signal(signal.SIGALRM, handler)
 
 def split_list(lst, n):
     """Split a list into n (roughly) equal-sized chunks"""
@@ -34,29 +42,10 @@ def get_chunk(lst, n, k):
 
 
 def get_model_output(model, image_processor, tokenizer, video_path, qs, args):
-    num_video_frames = 8
-    decord.bridge.set_bridge("torch")
-    video_reader = VideoReader(uri=video_path)
-
-    idx = np.round(np.linspace(0, len(video_reader) - 1, num_video_frames)).astype(int)
-    video_outputs = video_reader.get_batch(idx)
-
-    b, h, w, c = video_outputs.size()
-    patch_size = model.get_model().vision_tower.config.patch_size
-    image_size = model.get_model().vision_tower.config.image_size
-    n_image_tokens = (image_size // patch_size) ** 2
-    image_tensor = torch.zeros(b, c, image_size, image_size, dtype=torch.uint8)
-    video_frames = video_outputs.permute(0, 3, 1, 2).contiguous()
-    
-    video_frames = Resize(size=[image_size, image_size], antialias=True)(video_frames)
-    image_tensor[:, :, :, :] = video_frames
 
-    # image_tensor = [utils.preprocess_image(image, image_processor, use_padding=False) for image in torch.unbind(image_tensor)]
-    image_tensor = [
-        image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0].unsqueeze(0)
-        for image in torch.unbind(image_tensor)
-    ]
-    image_tensor = torch.cat(image_tensor, dim=0)
+    num_video_frames = model.config.num_video_frames
+    images, video_loading_succeed = LazySupervisedDataset._load_video(video_path, num_video_frames, args)
+    image_tensor = process_images(images, image_processor, model.config)
 
     qs = '<image>\n' * num_video_frames + qs
 
@@ -88,11 +77,7 @@ def get_model_output(model, image_processor, tokenizer, video_path, qs, args):
             stopping_criteria=[stopping_criteria]
         )
 
-    input_token_len = input_ids.shape[1]
-    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-    if n_diff_input_output > 0:
-        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
     outputs = outputs.strip()
     if outputs.endswith(stop_str):
         outputs = outputs[:-len(stop_str)]
@@ -105,16 +90,31 @@ def eval_model(args):
     disable_torch_init()
     model_path = os.path.expanduser(args.model_path)
     model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
+    args.image_processor = image_processor
 
-    gt_questions = json.load(open(args.gt_file_question, "r"))
+    gt_questions = json.load(open(os.path.expanduser(args.gt_file_question), "r"))
     gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx)
-    gt_answers = json.load(open(args.gt_file_answers, "r"))
+    gt_answers = json.load(open(os.path.expanduser(args.gt_file_answers), "r"))
     gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx)
 
+    args.output_dir = os.path.expanduser(args.output_dir)
+    print(f"Output directory: {args.output_dir}")
+    args.video_dir = os.path.expanduser(args.video_dir)
+    print(f"Video directory: {args.video_dir}")
     answers_file = os.path.join(args.output_dir, f"{args.output_name}.json")
     os.makedirs(args.output_dir, exist_ok=True)
-    ans_file = open(answers_file, "w")
+    # Read cache answer file, each line is a json object
+    if os.path.exists(answers_file):
+        cache_ans_file = open(answers_file, "r")
+        cache_ans = cache_ans_file.readlines()
+        cache_ans_file.close()
+    else:
+        cache_ans = []
+
+    # Get cached video ids
+    cache_set = set([json.loads(line)['id'] for line in cache_ans])
+        
 
     # Create the output directory if it doesn't exist
     if not os.path.exists(args.output_dir):
@@ -124,6 +124,7 @@ def eval_model(args):
     output_list = [] 
     video_formats = ['.mp4', '.avi', '.mov', '.mkv']
 
+
     # Iterate over each sample in the ground truth file
     index = 0
     for sample in tqdm(gt_questions):
@@ -137,7 +138,14 @@ def eval_model(args):
 
         # Load the video file
         for fmt in tqdm(video_formats):  # Added this line
-            temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+
+            if "Activitynet_Zero_Shot_QA" in args.video_dir:
+                temp_path = os.path.join(args.video_dir, f"{id.rsplit('_', 1)[0]}{fmt}")
+            else:
+                temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}")
+            if f"{id}" in cache_set:
+                print(f"Skipping {id} because it is in the cache")
+                continue
             if os.path.exists(temp_path):
                 video_path = temp_path
                 # try:
@@ -147,12 +155,11 @@ def eval_model(args):
                 output_list.append(sample_set)
                 # except Exception as e:
                 #     print(f"Error processing video file '{video_name}': {e}")
-                ans_file.write(json.dumps(sample_set) + "\n")
+                    # Write into the answer file.
+                with open(answers_file, 'a') as f:
+                    f.write(json.dumps(sample_set) + "\n")
                 break
 
-    ans_file.close()
-
-
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
diff --git a/llava/eval/model_vqa_videodemo.py b/llava/eval/model_vqa_videodemo.py
new file mode 100755
index 00000000..51fb16f9
--- /dev/null
+++ b/llava/eval/model_vqa_videodemo.py
@@ -0,0 +1,187 @@
+# This file is modified from https://github.com/haotian-liu/LLaVA/
+
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+import shortuuid
+
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.conversation import conv_templates, SeparatorStyle
+from llava.model.builder import load_pretrained_model
+from llava.data.dataset import LazySupervisedDataset
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+from llava.mm_utils import process_images
+
+from PIL import Image
+import math
+import numpy as np
+
+from torchvision.transforms import Resize
+from pytorchvideo.data.encoded_video import EncodedVideo
+
+import signal
+
+# This function will be called when the timeout is reached
+def handler(signum, frame):
+    raise TimeoutError()
+# Set the signal handler
+signal.signal(signal.SIGALRM, handler)
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def get_model_output(model, image_processor, tokenizer, video_path, qs, args):
+
+    num_video_frames = model.config.num_video_frames
+    images, video_loading_succeed = LazySupervisedDataset._load_video(video_path, num_video_frames, args)
+    image_tensor = process_images(images, image_processor, model.config)
+
+    qs = '<image>\n' * num_video_frames + qs
+
+    conv = conv_templates[args.conv_mode].copy()
+    conv.append_message(conv.roles[0], qs)
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+
+    input_ids = tokenizer_image_token(
+        prompt,
+        tokenizer,
+        image_token_index=IMAGE_TOKEN_INDEX,
+        return_tensors="pt",
+    )
+    input_ids = torch.unsqueeze(input_ids, 0)
+    input_ids = torch.as_tensor(input_ids).cuda()
+
+    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
+    keywords = [stop_str]
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor.half().cuda(),
+            do_sample=True,
+            temperature=0.2,
+            max_new_tokens=1024,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria]
+        )
+
+    input_token_len = input_ids.shape[1]
+    # n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
+    # if n_diff_input_output > 0:
+    #     print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
+    outputs = outputs.strip()
+    if outputs.endswith(stop_str):
+        outputs = outputs[:-len(stop_str)]
+    outputs = outputs.strip()
+    return outputs
+
+def parse_caption_template(template_type):
+    if "bin" in template_type:
+        short_bin = template_type.split("_")[1]
+        long_bin = template_type.split("_")[2]
+        short_question = "Elaborate on the visual and narrative elements of the video in detail."
+        long_question = f"Summarize the visual content of the following video. Please write the caption with no more than {long_bin} words.\n<video>"
+    else:
+        raise ValueError(f"Invalid template type: {template_type}")
+
+    return short_question, long_question
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+
+    # List video files
+    video_formats = ['.mp4', '.avi', '.mov', '.mkv']
+    video_files = os.listdir(args.video_dir)
+    video_files = [f for f in video_files if os.path.splitext(f)[1] in video_formats]
+    short_q, long_q = parse_caption_template(args.prompt_template)
+    gt_questions = []
+    for i, video_name in enumerate(video_files):
+        gt_questions.append(
+            {
+                'video_name': video_name, 
+                'short_q': short_q, 
+                'long_q': long_q,
+            }
+        )
+    
+    # Create the output directory if it doesn't exist
+
+
+    args.output_dir = os.path.expanduser(args.output_dir)
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+    print(f"Output directory: {args.output_dir}")
+    args.video_dir = os.path.expanduser(args.video_dir)
+    print(f"Video directory: {args.video_dir}")
+    short_caption_file = os.path.join(args.output_dir, f"detailed_captions.txt")
+    short_caption_file = open(short_caption_file, "w")
+    long_caption_file = os.path.join(args.output_dir, f"video_captions.txt")
+    long_caption_file = open(long_caption_file, "w")
+    
+
+    
+    # List to store the output results
+    output_list = [] 
+
+
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
+    args.image_processor = image_processor
+
+    # Iterate over each sample in the ground truth file
+    index = 0
+    for sample in tqdm(gt_questions):
+        video_name = sample['video_name']
+        short_question = sample['short_q']
+        long_question = sample['long_q']
+        index += 1
+        
+        # Load the video file
+        for question_type in ['short', 'long']:
+            temp_path = os.path.join(args.video_dir, f"{video_name}")
+            print(f"Processing video: {temp_path}")
+            if os.path.exists(temp_path):
+                video_path = temp_path
+                question = short_question if question_type == 'short' else long_question
+                output = get_model_output(model, image_processor, tokenizer, video_path, question, args)
+                print('question:', question)
+                if question_type == 'short':
+                    short_caption_file.write(f"{video_name}: {output}\n")
+                elif question_type == 'long':
+                    long_caption_file.write(f"{video_name}: {output}\n")
+                else:
+                    raise ValueError(f"Invalid question type: {question_type}")
+    short_caption_file.close()
+    long_caption_file.close()
+    print(f"Results saved to {args.output_dir}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--prompt_template", type=str, help='The template of video caption question.', required=True)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/model_vqa_videoperception.py b/llava/eval/model_vqa_videoperception.py
new file mode 100755
index 00000000..b72b25ae
--- /dev/null
+++ b/llava/eval/model_vqa_videoperception.py
@@ -0,0 +1,174 @@
+# This file is modified from https://github.com/haotian-liu/LLaVA/
+
+import argparse
+import torch
+import os
+import json
+from tqdm import tqdm
+
+from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
+from llava import conversation as conversation_lib
+from llava.model.builder import load_pretrained_model
+from llava.data.dataset import LazySupervisedDataset
+from llava.utils import disable_torch_init
+from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
+from llava.data import preprocess
+from llava.mm_utils import process_images
+
+import math
+
+
+import signal
+
+# This function will be called when the timeout is reached
+def handler(signum, frame):
+    raise TimeoutError()
+# Set the signal handler
+signal.signal(signal.SIGALRM, handler)
+
+def split_list(lst, n):
+    """Split a list into n (roughly) equal-sized chunks"""
+    chunk_size = math.ceil(len(lst) / n)  # integer division
+    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
+
+
+def get_chunk(lst, n, k):
+    chunks = split_list(lst, n)
+    return chunks[k]
+
+
+def get_model_option(model, image_processor, tokenizer, video_path, qs, options, args):
+
+    conversation_lib.default_conversation = conversation_lib.conv_templates[
+        args.conv_mode
+    ]
+    num_video_frames = model.config.num_video_frames
+    images, video_loading_succeed = LazySupervisedDataset._load_video(video_path, num_video_frames, args)
+    image_tensor = process_images(images, image_processor, model.config)
+
+    qs = '<image>\n' * num_video_frames + qs
+    loss_list = []
+    for id, option in enumerate(options):
+
+        conversation = [
+            {"from": "human", "value": qs},
+            {"from": "gpt", "value": option},
+        ]
+
+        sources = [conversation]
+
+        data_dict = preprocess(
+            sources,
+            tokenizer,
+            has_image=True,
+        )
+        input_ids = data_dict["input_ids"]
+        targets = data_dict["labels"]
+        # Remove last ending token
+        targets[0][-1] = IGNORE_INDEX
+
+        with torch.inference_mode():
+            outputs = model(
+                input_ids=input_ids.cuda(),
+                labels=targets.cuda(),
+                images=image_tensor.half().cuda(),
+                attention_mask=input_ids.cuda().ne(tokenizer.pad_token_id),
+            )
+            loss = outputs.loss.item()
+            loss_list.append(loss)
+
+    # Get index of the minimum loss
+    min_loss_index = loss_list.index(min(loss_list))
+    return min_loss_index
+
+
+def eval_model(args):
+    # Model
+    disable_torch_init()
+
+    gt_questions = json.load(open(os.path.expanduser(args.gt_file), "r"))
+    # Convert the gt_questions dict to list
+    gt_questions_list = []
+    for key in gt_questions.keys():
+        gt_questions_list.append(gt_questions[key])
+    
+    gt_questions = get_chunk(gt_questions_list, args.num_chunks, args.chunk_idx)
+    # gt_answers = json.load(open(os.path.expanduser(args.gt_file_answers), "r"))
+    # gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx)
+
+    args.output_dir = os.path.expanduser(args.output_dir)
+    print(f"Output directory: {args.output_dir}")
+    args.video_dir = os.path.expanduser(args.video_dir)
+    print(f"Video directory: {args.video_dir}")
+    answers_file = os.path.join(args.output_dir, f"{args.output_name}.json")
+    os.makedirs(args.output_dir, exist_ok=True)
+    # Read cache answer file, each line is a json object
+    if os.path.exists(answers_file):
+        cache_ans_file = open(answers_file, "r")
+        cache_ans = cache_ans_file.readlines()
+        cache_ans_file.close()
+    else:
+        cache_ans = []
+
+    # Get cached video ids
+    cache_set = set([json.loads(line)['video_name_question_id'] for line in cache_ans])
+        
+    model_path = os.path.expanduser(args.model_path)
+    model_name = get_model_name_from_path(model_path)
+    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_name, args.model_base)
+    args.image_processor = image_processor
+
+    # Iterate over each sample in the ground truth file
+    # index = 0
+    for sample in tqdm(gt_questions):
+        video_name = sample["metadata"]['video_id']
+        questions = sample["mc_question"]
+        # Check if the video is in the cache
+
+
+        for question_dict in questions:
+            # total += 1
+            question = question_dict['question']
+            options = question_dict['options']
+            question_id = question_dict['id']
+            answer_id = question_dict['answer_id']
+
+            if f"{video_name}_{question_id}" in cache_set:
+                print(f"Skipping {video_name}_{question_id} because it is in the cache")
+                continue
+            min_loss_index = get_model_option(model, image_processor, tokenizer, os.path.join(args.video_dir, f"{video_name}.mp4"), question, options, args)
+            # if min_loss_index == answer_id:
+            #     correct += 1
+
+            # Write into cache
+            sample_set = {
+                'video_name_question_id': f"{video_name}_{question_id}", 
+                'question': question, 
+                'answer_id': answer_id,
+                'prediction': min_loss_index,
+                'correct': min_loss_index == answer_id,
+            }
+            with open(answers_file, 'a') as f:
+                f.write(json.dumps(sample_set) + "\n")
+            
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-base", type=str, default=None)
+    parser.add_argument("--model_max_length", type=int, required=False, default=2048)
+    parser.add_argument('--video_dir', help='Directory containing video files.', required=True)
+    parser.add_argument('--gt_file', help='Path to the ground truth file containing question.', required=True)
+    # parser.add_argument('--gt_file_answers', help='Path to the ground truth file containing answers.', required=True)
+    parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True)
+    parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True)
+    parser.add_argument("--conv-mode", type=str, default="llava_v1")
+    parser.add_argument("--num-chunks", type=int, default=1)
+    parser.add_argument("--chunk-idx", type=int, default=0)
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top_p", type=float, default=None)
+    parser.add_argument("--num_beams", type=int, default=1)
+    args = parser.parse_args()
+
+    eval_model(args)
diff --git a/llava/eval/qa_baseline_gpt35.py b/llava/eval/qa_baseline_gpt35.py
old mode 100644
new mode 100755
diff --git a/llava/eval/run_llava.py b/llava/eval/run_vila.py
old mode 100644
new mode 100755
similarity index 61%
rename from llava/eval/run_llava.py
rename to llava/eval/run_vila.py
index 39385014..19c61d0e
--- a/llava/eval/run_llava.py
+++ b/llava/eval/run_vila.py
@@ -1,31 +1,22 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
 
 import argparse
+import re
+from io import BytesIO
+import os, os.path as osp
+
+import requests
 import torch
+from PIL import Image
 
-from llava.constants import (
-    IMAGE_TOKEN_INDEX,
-    DEFAULT_IMAGE_TOKEN,
-    DEFAULT_IM_START_TOKEN,
-    DEFAULT_IM_END_TOKEN,
-    IMAGE_PLACEHOLDER,
-)
-from llava.conversation import conv_templates, SeparatorStyle
+from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
+                             DEFAULT_IMAGE_TOKEN, IMAGE_PLACEHOLDER,
+                             IMAGE_TOKEN_INDEX)
+from llava.conversation import SeparatorStyle, conv_templates
+from llava.mm_utils import (KeywordsStoppingCriteria, get_model_name_from_path,
+                            process_images, tokenizer_image_token)
 from llava.model.builder import load_pretrained_model
 from llava.utils import disable_torch_init
-from llava.mm_utils import (
-    process_images,
-    tokenizer_image_token,
-    get_model_name_from_path,
-    KeywordsStoppingCriteria,
-)
-
-from PIL import Image
-
-import requests
-from PIL import Image
-from io import BytesIO
-import re
 
 
 def image_parser(args):
@@ -35,6 +26,7 @@ def image_parser(args):
 
 def load_image(image_file):
     if image_file.startswith("http") or image_file.startswith("https"):
+        print("downloading image from url", args.video_file)
         response = requests.get(image_file)
         image = Image.open(BytesIO(response.content)).convert("RGB")
     else:
@@ -49,15 +41,25 @@ def load_images(image_files):
         out.append(image)
     return out
 
-
 def eval_model(args):
     # Model
     disable_torch_init()
-
+    if args.video_file is None:
+        image_files = image_parser(args)
+        images = load_images(image_files)
+    else:
+        if args.video_file.startswith("http") or args.video_file.startswith("https"):
+            print("downloading video from url", args.video_file)
+            response = requests.get(args.video_file)
+            video_file = BytesIO(response.content)
+        else:
+            assert osp.exists(args.video_file), "video file not found"
+            video_file = args.video_file
+        from llava.mm_utils import opencv_extract_frames
+        images = opencv_extract_frames(video_file, args.num_video_frames)
+        
     model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(
-        args.model_path, args.model_base, model_name
-    )
+    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, model_name, args.model_base)
 
     qs = args.query
     image_token_se = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
@@ -68,10 +70,13 @@ def eval_model(args):
             qs = re.sub(IMAGE_PLACEHOLDER, DEFAULT_IMAGE_TOKEN, qs)
     else:
         if DEFAULT_IMAGE_TOKEN not in qs:
+            print("no <image> tag found in input. Automatically append one at the beginning of text.")
+            # do not repeatively append the prompt.
             if model.config.mm_use_im_start_end:
-                qs = image_token_se + "\n" + qs
+                qs = (image_token_se + "\n") * len(images) + qs
             else:
-                qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
+                qs = (DEFAULT_IMAGE_TOKEN + "\n") * len(images) + qs
+    print("input: ", qs)
 
     if "llama-2" in model_name.lower():
         conv_mode = "llava_llama_2"
@@ -96,28 +101,23 @@ def eval_model(args):
     conv.append_message(conv.roles[1], None)
     prompt = conv.get_prompt()
 
-    image_files = image_parser(args)
-    images = load_images(image_files)
-    images_tensor = process_images(
-        images,
-        image_processor,
-        model.config
-    ).to(model.device, dtype=torch.float16)
-
-    input_ids = (
-        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
-        .unsqueeze(0)
-        .cuda()
-    )
+    
+        
+        
+    images_tensor = process_images(images, image_processor, model.config).to(model.device, dtype=torch.float16)
+    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).cuda()
 
     stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
     keywords = [stop_str]
     stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
 
+    print(images_tensor.shape)
     with torch.inference_mode():
         output_ids = model.generate(
             input_ids,
-            images=images_tensor,
+            images=[
+                images_tensor,
+            ],
             do_sample=True if args.temperature > 0 else False,
             temperature=args.temperature,
             top_p=args.top_p,
@@ -127,15 +127,7 @@ def eval_model(args):
             stopping_criteria=[stopping_criteria],
         )
 
-    input_token_len = input_ids.shape[1]
-    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-    if n_diff_input_output > 0:
-        print(
-            f"[Warning] {n_diff_input_output} output_ids are not the same as the input_ids"
-        )
-    outputs = tokenizer.batch_decode(
-        output_ids[:, input_token_len:], skip_special_tokens=True
-    )[0]
+    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
     outputs = outputs.strip()
     if outputs.endswith(stop_str):
         outputs = outputs[: -len(stop_str)]
@@ -145,9 +137,11 @@ def eval_model(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
+    parser.add_argument("--model-path", type=str, default="Efficient-Large-Model/VILA-2.7b")
     parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--image-file", type=str, required=True)
+    parser.add_argument("--image-file", type=str, default=None)
+    parser.add_argument("--video-file", type=str, default=None)
+    parser.add_argument("--num-video-frames", type=int, default=6)
     parser.add_argument("--query", type=str, required=True)
     parser.add_argument("--conv-mode", type=str, default=None)
     parser.add_argument("--sep", type=str, default=",")
@@ -157,4 +151,4 @@ def eval_model(args):
     parser.add_argument("--max_new_tokens", type=int, default=512)
     args = parser.parse_args()
 
-    eval_model(args)
+    eval_model(args)
\ No newline at end of file
diff --git a/llava/eval/summarize_gpt_review.py b/llava/eval/summarize_gpt_review.py
old mode 100644
new mode 100755
diff --git a/llava/eval/table/answer/answer_alpaca-13b.jsonl b/llava/eval/table/answer/answer_alpaca-13b.jsonl
deleted file mode 100644
index b850e137..00000000
--- a/llava/eval/table/answer/answer_alpaca-13b.jsonl
+++ /dev/null
@@ -1,80 +0,0 @@
-{"question_id": 1, "text": "Improving time management skills involves setting priorities, breaking tasks into smaller chunks, delegating tasks, avoiding multitasking, and taking regular breaks. Additionally, it is important to have a positive attitude and be realistic with goals. Making a to-do list, tracking time, and using technology to automate mundane tasks can also help improve time management skills.", "answer_id": "kEL9ifUHDeYuAXzevje2se", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 2, "text": "The most effective ways to deal with stress include identifying and avoiding the sources of stress, developing healthy coping mechanisms, and engaging in relaxation techniques such as meditation, deep breathing, and physical exercise. Additionally, it is important to take care of one's mental and physical health, get enough rest and nutrition, and practice self-care.", "answer_id": "VcF3NrWGXhhxLkDVurNrwq", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 3, "text": "The main differences between Python and JavaScript programming languages are in their syntax, structure, and the type of applications they are designed for. Python is a general-purpose language designed to be easy to read and write, while JavaScript is primarily used for web development. Python is a compiled language, while JavaScript is an interpreted language. Python is strongly typed, while JavaScript is not. Python is typically faster than JavaScript in terms of execution, while JavaScript is better suited to create interactive web pages.", "answer_id": "LpvtyQi9QdSgRrgGDxiGrT", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 4, "text": "Working from home can be a great way to increase productivity, but it does require some strategic planning and tactics. Here are some tips for increasing your productivity while working from home: \n1. Establish a routine: Having a regular schedule and sticking to it can help you stay focused and motivated.\n2. Create a distraction-free zone: Eliminate distractions by turning off notifications and setting a \u201cdo not disturb\u201d mode.\n3. Take breaks: Working for long stretches without taking breaks can lead to burnout and decreased productivity.\n4. Exercise: Exercise can help improve focus and productivity.\n5. Get organized: Having a clean and organized workspace can help you stay on task.\n6. Set goals: Set short-term and long-term goals to keep you motivated and on track.\n7. Connect with colleagues: Don\u2019t be afraid to reach out to colleagues for help or advice.\n8. Track your progress: Keep track of your progress and celebrate your successes.\n9. Unplug: Taking breaks and unplugging from work can help you recharge and come back to work more refreshed.", "answer_id": "7zQm8cSTJhPtPdZdxbcfrX", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 5, "text": "Sure. Quantum computing is a new technology that uses quantum mechanical phenomena to perform operations on data. Unlike traditional computers, which process information in binary form, quantum computers can process information in multiple states at the same time, allowing them to perform operations faster and with more accuracy. It also allows them to solve problems that classical computers struggle with. Quantum computing is still in its infancy, but it has the potential to revolutionize the computing industry and open up new possibilities in artificial intelligence, cryptography, and other fields.", "answer_id": "UrLEH82RHwqqLt2LyvYSKj", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 6, "text": "Plant-based protein sources include legumes, grains, nuts, and seeds, and are generally lower in fat and calories than animal-based proteins. Animal-based proteins come from sources such as meat, eggs, and dairy, and are generally higher in fat and calories than plant-based proteins. Plant-based proteins are also generally lower in cholesterol and saturated fat than animal-based proteins. Additionally, plant-based proteins require fewer resources for production, such as water and land, than animal-based proteins.", "answer_id": "fpRdMTdnfirosQixuf2Gez", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 7, "text": "Developing critical thinking skills involves actively questioning assumptions, taking multiple perspectives, analyzing information, and coming to logical conclusions. You can practice these skills by posing questions to yourself and working through problems, as well as engaging in thoughtful discussion with others.", "answer_id": "PvGmsCJSNFcvQKmPTnnd7s", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 8, "text": "The major challenges faced by the education sector include lack of access to quality education, low educational outcomes, high costs of education, lack of adequate infrastructure and teaching materials, overcrowding in classrooms, and gender inequality. Other challenges include lack of access to technology and digital learning materials, outdated curriculum and pedagogical practices, and inadequate teacher training.", "answer_id": "n4ANAbpR3gvLPP8poPfKZ6", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 9, "text": "The primary factors that influence consumer behavior include cultural, social, and personal factors. Cultural factors include shared beliefs, values, and customs within a society, while social factors include the influence of family, friends, and peers. Personal factors include an individual's lifestyle, attitudes, beliefs, and past experiences. Other factors such as advertising, marketing, and price also have an effect on consumer behavior.", "answer_id": "STJ36GrgQMcaUi7zaoNPit", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 10, "text": "The most effective strategies for conflict resolution in the workplace include:\n1. Establishing open communication: Encouraging open and honest communication between parties is key to resolving any conflict.\n2. Listening actively: Active listening allows both parties to feel heard and allows for a better understanding of the other person's perspective.\n3. Identifying root causes: Understanding the underlying cause of the conflict can help to find more effective solutions that address the core issue.\n4. Focusing on interests, not positions: Focusing on the interests of both parties can help to find creative solutions that satisfy the needs of everyone involved.\n5. Developing mutual respect: Respect is essential to any successful resolution. Building mutual respect can help to create a healthier work environment.\n6. Negotiation: Working together to negotiate a mutually beneficial solution can help to create an agreement that both parties are happy with.\n7. Taking responsibility: Taking responsibility for one\u2019s words and actions can help to create a culture of accountability in the workplace.", "answer_id": "425SwYvqKPAXFGTYKXB7Cs", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 11, "text": "The use of single-use plastic bottles has a number of negative implications on the environment. They are not biodegradable and can take hundreds of years to break down. This means they are around for a long time, releasing potentially harmful chemicals into the environment, and they can be easily mistaken for food by animals. Single-use plastic bottles can also be a source of micro-plastics, which are very small particles of plastic that are difficult to clean up and can be consumed by fish and other aquatic life, leading to health problems in humans who consume those fish. On the other hand, reusable bottles are a much more environmentally friendly choice since they do not produce any waste. They are also often made of materials that are safer for human health, such as stainless steel or glass. Reusable bottles are also more cost-effective since they do not need to be replaced as often as single-use plastic bottles.", "answer_id": "VbNAuj6KAkMdLJQXMo22oK", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 12, "text": "When designing an inclusive and accessible public transportation system, some factors to consider include providing frequent and reliable service, accessible stops and vehicles, a variety of fare options, clear and consistent signage and information, comfortable seating and space for wheelchairs and other assistive devices, and security and safety protocols. Additionally, public transportation systems should be integrated with other services such as public parking, bike and scooter sharing, and shuttles to provide a comprehensive and accessible experience.", "answer_id": "CNGqAeu2QJbQ4QGzHJDPdq", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 13, "text": "Governments can use fiscal and monetary policies to combat economic recessions. Fiscal policies involve the government spending money or cutting taxes in order to stimulate the economy, while monetary policies involve changing the money supply or interest rates to influence economic activity. These policies can be used to stabilize the economy, create jobs, and reduce poverty.", "answer_id": "E8w2qYqnm8iqCrSkUv62sz", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 14, "text": "Language and cultural barriers can lead to misunderstandings and make it more difficult to communicate and form relationships in multicultural societies. Without a common language or shared culture, people can have difficulty understanding each other's perspectives and beliefs, which can lead to mistrust and a lack of understanding between different ethnic groups. To overcome these barriers, it is important to make an effort to learn about different cultures, be open-minded, and take the time to understand each other.", "answer_id": "8o5yMymfzo6kzmp9GK5MWr", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 15, "text": "Artificial intelligence can be used to improve the quality and efficiency of healthcare delivery in a variety of ways. AI can be used to assist with diagnosing diseases by comparing symptoms and medical history to images of known diseases and medical conditions. AI can also be used to analyze laboratory results and patient records to identify potential problems and develop treatment plans. AI can be used to automate administrative tasks and reduce paperwork, as well as identify potential drug interactions and side effects. AI can also be used to automate appointment reminders, facilitate communication between doctors and patients, and even provide virtual health coaching to help patients manage their conditions.", "answer_id": "kbJVEEsdsSScEq5Y5furr7", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 16, "text": "CRISPR-Cas9 is a recently developed gene editing technology that has revolutionized the way scientists are able to edit genomes. The technology uses a guide RNA to direct the Cas9 enzyme to a specific location in the genome, where it will cut the DNA strands. This allows for the insertion or deletion of DNA sequences, which can be used to modify the genetic code of an organism. Potential applications include treating genetic diseases, increasing crop yields, and creating pest-resistant crops. Ethically, the biggest concern is the potential misuse of the technology, which could lead to unintended consequences or be used to alter humanity in ways that could harm us.", "answer_id": "CMUL5ULZuR7YC5EPzCBN2N", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 17, "text": "Vaccinations work by stimulating the body's immune system to protect against infectious diseases. Herd immunity is a concept whereby a population is protected against a certain disease when a certain percentage of the population has immunity to the disease, either through vaccination or having already contracted the disease. This is because when enough people are vaccinated, it reduces the spread of the disease and prevents it from spreading to those who are not immune.", "answer_id": "kEmDDQyNqSkyFihYEEBpuR", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 18, "text": "Social media platforms can have a significant influence on how people consume and share news. By providing instant access to news sources and allowing users to easily share content, these platforms can shape the public's information landscape and the spread of misinformation. Some potential implications of this are that users may not always be able to discern between reliable and unreliable sources, and misleading information may be more easily spread and go viral. This can lead to confusion and mistrust among users, and can potentially be exploited for nefarious purposes. To mitigate these risks, it is important for users to be aware of the potential biases and agendas of the sources they are consuming, and for social media platforms to take proactive steps to curb the spread of misinformation.", "answer_id": "Qs3grQsqFVGK9EVkCkf9PB", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 19, "text": "Cultural, social, and economic factors all play a role in determining an individual's food choices. For example, people growing up in households with limited economic means may be more likely to consume cheaper, less nutritious foods due to economic constraints. Additionally, cultural norms and values can influence food choices, such as the emphasis on large portions typical in some American cultural settings. Finally, social factors such as peer pressure and advertising can also sway an individual's food choices. By understanding and taking into account these various factors, health promotion strategies can be tailored and targeted to help individuals make healthier dietary decisions.", "answer_id": "kzZ6dKN7hkRWjqtdHr7Qns", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 20, "text": "Natural selection is the process by which certain traits or characteristics become more or less common in a population over time. This is due to the fact that those individuals with certain advantageous traits are more likely to survive, reproduce, and pass those traits on to their offspring. Over time, this leads to the evolution and adaptation of species to their environment.", "answer_id": "DPPDG6YGFJij2GCmRL66PU", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 21, "text": "I am a brave knight, loyal to my king and country, and I have come to serve and protect them both. I am skilled in the arts of war and peace, and I am proud to be here at the royal court.", "answer_id": "D62FjDb4nZANzPpfSfsiyn", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 22, "text": "Avast ye maties! To the bottom of the sea we go! In search of hidden riches and fortune, we shall brave the depths and face any danger that comes our way. For glory and honor, let us set sail and find the lost Ark!", "answer_id": "k7E4NNw5kyj9DmvP5Pu2zb", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 23, "text": "If I were a Shakespearean character, I would declare my love for someone in a soliloquy with passionate words and metaphors. I would express my love for them with descriptions of their beauty and the joy I feel when I am with them. I would confess my hopes and dreams for our future together and declare my devotion to them. I would make my love for them known in a way that could never be forgotten.", "answer_id": "KFocjVCejYrU3YmLjAqoUF", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 24, "text": "As a superhero, my origin story is very special. I was born with special abilities that I had to keep hidden for a long time. I was scared to use them at first, but eventually I discovered that I could use my powers to protect people and make the world a better place. I've had to face a lot of challenges along the way, but I've also had a lot of help from friends and family. Now, I use my abilities to help people in need and fight for justice.", "answer_id": "dq8Sm9djS7e7y9sG9vmMJf", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 25, "text": "If I were a time traveler from the year 3000, I would tell people about the incredible advancements in technology, such as the ability to travel through time, space, and dimensions; the development of intelligent robots and autonomous vehicles; the emergence of virtual reality and augmented reality; and the rise of artificial intelligence and machine learning.", "answer_id": "XZ8fG8e6u7CyKd2moK6abe", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 26, "text": "The game was a nail-biter, with both teams trading leads throughout the game. With only seconds left on the clock, the home team made a bold move and passed the ball to their star player, who took the ball down the court and made a layup at the buzzer to seal the victory for the home team!", "answer_id": "oKaXHfoK4pXwrefFWXmeA8", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 27, "text": "My signature dish is a seamless blend of traditional and modern cooking techniques. I use only the freshest ingredients to create a unique and unforgettable dining experience. The dish is a perfect balance of flavors and textures, with a subtle hint of my personal style. It is a dish that I am proud to call my own.", "answer_id": "ZwiZfvDWm7SETKNBfDk7Mb", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 28, "text": "At the summit of Mount Everest, you are filled with a sense of accomplishment and joy. The view from the top is absolutely breathtaking - you can see for miles and miles, with the majestic Himalayan mountain range stretching out in all directions. It is a truly unforgettable experience.", "answer_id": "DxYopRe2LcTJMy3FWu6btd", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 29, "text": "As a colonist on Mars, my daily life is filled with challenges. Finding resources and creating a sustainable environment is a priority. I face a number of challenges including extreme temperature fluctuations, limited access to resources, and the difficulty of travelling to and from the planet. Additionally, I must be mindful of my physical and mental health since I am so far from home. Despite these challenges, I am grateful to be able to explore and experience this new world.", "answer_id": "WC3UJVh4jQ5RUkpcRMU98L", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 30, "text": "In the post-apocalyptic world, I am a survivor by necessity. I scavenge for food and supplies, and I'm always on the lookout for potential allies. I've encountered a few people who have managed to survive, and together we have formed an alliance to help each other. We hunt for food, build shelter, and work together to stay alive. We also share knowledge and skills, like how to start a fire or how to use a weapon. We look out for each other, and our alliance has strengthened our chances of survival.", "answer_id": "gTvgn6ksDjGGgdprw6AG5A", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 31, "text": "There are a few ways to tell if a restaurant is popular among locals or mainly attracts tourists. Firstly, look at the clientele - if the majority of people there are tourists, it's likely that the restaurant is mainly attracting tourists. Secondly, check online reviews - if the reviews are mainly from tourists, then it's likely that the restaurant is popular with tourists. Finally, look at the prices - if the prices are higher than average for the area, it could be a sign that the restaurant is popular with locals. This information can be useful to get an idea of what kind of experience to expect, as locals might know about different aspects of the restaurant that tourists wouldn't.", "answer_id": "3q7giCk2BA3Ye4Tm9HC2iw", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 32, "text": "Some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed include: not asking any questions or engaging in the conversation, avoiding eye contact, fidgeting or stammering when speaking, repeating questions or comments made by other people, and nodding along without any signs of understanding.", "answer_id": "hRGsxy86v26SC4yAQS29X4", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 33, "text": "Some people prefer the tactile and visual experience of using a paper map, and others may prefer to ask for directions from locals in order to get a more personalized experience. Additionally, GPS devices and smartphone apps can sometimes be inaccurate or have limited battery life, while a paper map or asking for directions may be more reliable.", "answer_id": "3n49A5ggJERfXYrLns3ZeU", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 34, "text": "One way to tell if someone is genuinely interested in a conversation is to observe their body language and facial expressions. Are they making an effort to maintain eye contact? Are they leaning in and actively listening to what you are saying? Do they ask questions and provide relevant answers? If so, it is likely that they are genuinely interested in the conversation. Additionally, if someone is simply being polite, they may not ask questions or engage in the conversation as much, and may not make an effort to maintain eye contact.", "answer_id": "ErCpFtPuYVru4oTTk4WrxG", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 35, "text": "Shopping at a small, locally-owned business can benefit the local community by keeping money in the area and supporting local jobs. Additionally, these businesses tend to offer a more personal experience and higher quality products than large chain stores. Furthermore, shopping at small businesses can help create a sense of place and community, and can help maintain a unique local culture.", "answer_id": "PTNoCRMZWoJk8HaKX7fW45", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 36, "text": "There are several ways to assess the credibility of a source of information. Firstly, you can look at the author's credentials and experience in the relevant field. Secondly, you can check the source of the information, such as whether it is from a reliable website or publication. Thirdly, you can look at the evidence presented in the article and whether it is backed up by reliable sources. Finally, you can read other people's reviews or comments about the article to get a better idea of its credibility.", "answer_id": "n8cFs9KENNwZ4z3SR4iXTr", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 37, "text": "Some people enjoy the sensation of being scared because it can create a feeling of excitement, enhance their emotional state, and provide a sense of thrill and adventure. Others may avoid these experiences because they are afraid of the unknown, or because they don't enjoy the feeling of being scared. Everyone is different, and some people may be more attracted to thrilling and exciting activities while others may prefer calmer activities.", "answer_id": "GzxL9mmEK5RzKqRbqBMUVC", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 38, "text": "By observing the behavior of others in a social situation, one can gain clues as to the cultural norms and expectations of a group. For example, watching how people interact with one another, how they address each other, how they handle disagreements, and how they go about solving problems can provide insight into the cultural values of the group. Additionally, observing body language, facial expressions, and other nonverbal cues can offer clues as to the accepted norms of behavior in a particular culture.", "answer_id": "QpoHFgb9SzwuaXQQUuBUQD", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 39, "text": "It is an interesting question, and one that has been debated for quite some time. I think there are valid arguments on both sides. On the one hand, exploring space is a remarkable human endeavor and could lead to tremendous scientific discoveries and technological advances. On the other hand, there are many pressing issues that need to be addressed on Earth, such as poverty, inequality, and climate change. Each side would argue that their cause is more important, and it is ultimately up to each individual to decide which one they feel more strongly about.", "answer_id": "Fxe6MS4GpP3LMDUwzY2cPA", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 40, "text": "It is important to strike a balance between job creation and technological progress. Automation can increase efficiency and productivity, but it should not come at the expense of job security and people's livelihoods. Therefore, it is essential to create policies and initiatives that promote both job creation and technological progress. This could include investing in training and education to ensure that people have the skills necessary to compete in the modern job market, as well as incentivizing companies to invest in technologies that create jobs and stimulate economic growth.", "answer_id": "mJiQ2FGR4Xb8kmhZjharkw", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 41, "text": "On average, the human eye blinks about 20 times per minute, or about 14,400 times per day. In a lifetime, this means that the average human will blink roughly 50 million times. This may seem like a lot, but it serves an important purpose. Blinking helps to keep the eyes lubricated and prevents them from drying out. It also helps to spread tears over the surface of the eye, washing away foreign particles and keeping the eye clean. Additionally, blinking helps to reduce the risk of eye infections by helping to clear away bacteria and other foreign substances.", "answer_id": "6Kph4RHRKEZ4YUoaHuEhBv", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 42, "text": "A grain of salt contains 102.98 atoms. To calculate this, we first need to know the atomic weight of a single atom. The atomic weight of an atom is the number of protons and neutrons in the nucleus of an atom, which determines its atomic mass. The atomic weight of a single atom of salt is 58.943 g/atom. Therefore, a grain of salt contains 102.98 atoms, which is equivalent to 60.98 grams.", "answer_id": "WBwpBQwhxn5kxLDb7MschC", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 43, "text": "Approximately 2000 lightning strikes occur on Earth each day. This is because the atmospheric conditions must come together in a particular way for a lightning strike to occur. Firstly, a large amount of electric charge must accumulate in the atmosphere, typically in a storm system. Then, the air must become increasingly unstable, leading to rising air and a strong updraft. This causes an electric breakdown of the air, and then an exchange of electricity occurs from the cloud to the ground, forming a lightning bolt. As these conditions are necessary for a lightning strike to occur, about 2000 lightning strikes happen on Earth each day.", "answer_id": "kf8nahQVci2ZLaYikagB7U", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 44, "text": "It would take about 10 million balloons to lift a house like in the movie Up. The balloons would need to be filled with helium in order for the house to be lifted. Each balloon would need to be filled with about 89.1 cubic feet of helium in order to lift 500 pounds. To calculate how many balloons would be needed, simply multiply the weight of the house (264.72 lbs) by the number of cubic feet of helium needed to lift 500 pounds (89.1). Therefore, it would take 10 million balloons to lift a house like in the movie Up.", "answer_id": "Gptgryd4o2dC8V5aqRmeJJ", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 45, "text": "According to a 2017 study, over 6.3 billion text messages are sent globally in a minute. This number is expected to grow to 6.8 billion by 2021. This is due to the increasing number of smartphones, better network coverage, and the increasing popularity of texting for communication. Furthermore, the increase in smartphones and better network coverage has also led to the rise of instant messaging applications such as WhatsApp, which has further increased the amount of text messages sent per minute.", "answer_id": "RfBWW8ZhdfTuTMb454Un4o", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 46, "text": "It is estimated that over 1 billion words are spoken on Earth every day. This is due to the fact that humans are highly social creatures, and language is the primary way that we communicate with each other. In addition, language is a key component of all cultures and societies, and is constantly being used to exchange information and ideas. Furthermore, language is integral to the way we think and process information, as well as to how we connect with each other on an emotional level. Therefore, it is not surprising that so many words are spoken on a daily basis.", "answer_id": "neGgLYm47JvqN8qkw8VeoW", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 47, "text": "On average, about 100 snowflakes fall per second during a typical winter. This number can vary drastically depending on the location and severity of the storm. Snowflakes are made up of tiny ice crystals, which form in the atmosphere when the temperature is below freezing and there is moisture in the air. As air moves and mixes, the crystals come together to form snowflakes. The shape, size and number of snowflakes depend on the atmospheric conditions, such as temperature and humidity, when they form.", "answer_id": "KSePbzLwsYasR3aui4HU8h", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 48, "text": "It is estimated that all the books ever written would total about 100 billion pages. This is based on the fact that there are roughly 130 million unique titles in existence, and each book on average contains about 200 pages. This means that if all the books were printed out, the stack of paper would be about 400 miles high. Additionally, if each book was represented by a single page, the total number of pages would be approximately 100 billion. Throughout history, books have served as a way to preserve knowledge and information, as well as to share ideas and stories with others. As technology advances, books are increasingly available in digital formats, which allows for easier access and storage. Regardless of the medium, books will continue to be a valuable source of information for generations to come.", "answer_id": "SWWfLpP2gnWHLbYRZXNsW3", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 49, "text": "Since the beginning of life, the Earth has orbited the Sun a total of 4.3 billion times. This is calculated by multiplying the average length of a year (365.25 days) by the number of years since life began (approximately 4.5 billion years). Life on Earth relies on the Sun's energy reaching the Earth's atmosphere and being redistributed by the atmosphere, the land surface, and the oceans. Without this energy, life on Earth would not be possible. Therefore, the Earth's orbit around the Sun is integral to the sustainability of life on Earth.", "answer_id": "WaBwKYhs7eAG22qCGLH2j3", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 50, "text": "Since the beginning of recorded music, countless songs have been recorded. The exact number is difficult to quantify, but conservative estimates put the number of songs recorded throughout history at several hundred thousand. This includes songs from all genres, from classical music to modern pop. The invention of recording technology has allowed musicians to preserve their music and share it with the world. This has enabled generations to access and enjoy music from all eras. With the rise of digital music, the number of recordings available has grown exponentially, and new songs are being recorded all the time.", "answer_id": "MfMJeE9om7qyBbqopHouf4", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 51, "text": "If the Internet had been invented during the Renaissance period, it likely would have been used primarily by the wealthy and powerful to access knowledge and information, as there was no widespread system of public education at the time. It also would have been a much slower and more expensive process to access information, as there were no high-speed connections or digital networks available.", "answer_id": "TjWPRDM6JFpPF8xeRptCKb", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 52, "text": "If the Aztecs had successfully repelled the Spanish conquistadors, their culture and way of life would have likely remained intact. The Spanish would not have been able to colonize Mexico, and their influence on the region would have been much less. It's likely that the Aztecs would have continued to expand their empire and control the areas that are now parts of Central and South America. Additionally, the Native American populations in the area would have likely been able to maintain more of their traditional customs and beliefs, and their languages may not have been as heavily impacted by Spanish.", "answer_id": "iR2tYTsWTFENEP7Qy9RgtX", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 53, "text": "If the Black Death had not occurred in the 14th century, the population of Europe may have continued to grow unabated, leading to more rapid urbanization and economic growth. It's likely that the Renaissance would have begun sooner, and the scientific revolution may have occurred earlier as well. Without the Black Death, there may have been no need for the industrial revolution, or at least it may have occurred later and on a smaller scale. Additionally, the spread of diseases may have been slower, leading to greater population density in certain areas.", "answer_id": "AZdS8xAi3GwAmCqkNSnnwv", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 54, "text": "If Isaac Newton had focused on biology instead of physics, he might have made important discoveries in the field of medicine and biology. He may have studied the human body and its functions in greater detail, and possibly even made important breakthroughs in treating diseases. He may also have studied the behavior of different animals in the wild and made important observations about their habits and habitats.", "answer_id": "VmwifF2JD5osYKDTqv2ZRS", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 55, "text": "If the Beatles had never formed as a band, the music world would have been drastically different. The British Invasion of the 1960s might not have happened, or it could have happened in a much different way. The cultural impact of the Beatles, including their influence on other musicians, fashion and society, may never have existed.", "answer_id": "mUL5UPj3qDGaCriEjL2U3B", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 56, "text": "If Alan Turing had not cracked the Enigma code during World War II, the outcome of the war could have been very different. It's possible that the Allies would have eventually defeated the Axis powers, but it likely would have taken much longer and cost many more lives. With the Enigma code cracked, the Allies were able to gain a critical advantage over the Axis powers and ultimately win the war.", "answer_id": "dVdwUoVrAQJDuWxiodykiw", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 57, "text": "If the Suez Canal had never been constructed, it would have major implications for international trade and navigation. The Suez Canal is an important shipping route connecting the Mediterranean Sea to the Red Sea, which in turn connects to the Indian Ocean. This allows for a shorter and more direct route for ships traveling between Europe and Asia, reducing travel time significantly. Without the Suez Canal, ships would have to travel around the Cape of Good Hope at the southern tip of Africa, which is much more time consuming and costly. This would make international trade more difficult and expensive, and could hinder global economic growth.", "answer_id": "EiNn9jjfy7dga6xfCtLtF8", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 58, "text": "If the Maya civilization had not mysteriously collapsed, it is likely that their influence and power would have continued to grow. They would have likely continued to build large cities and monuments, develop their writing and astronomical systems, and expand their trade networks. Additionally, their advances in agriculture and water management may have led to increased food production and a larger population.", "answer_id": "eqG9f2R9hXVyZrZMpcqAYq", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 59, "text": "If Christopher Columbus had not discovered the Americas, the indigenous people of the Americas would have continued to live in relative isolation from the rest of the world. It is likely that European colonization and the consequences of that would have been delayed, leading to a different modern world. It is also possible that other Europeans would have made the voyage across the Atlantic and discovered the Americas instead.", "answer_id": "ex42CLEzDVC2TxAvMCtYQJ", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 60, "text": "If Vincent van Gogh had been successful during his lifetime, he may have been able to achieve the recognition and financial stability that he desired. He may have been able to travel and gain more exposure for his artwork, as well as connect with other like-minded individuals. He may have been able to create more artwork and expand his portfolio. He may also have been able to find a mentor or patron who could have helped him further his career.", "answer_id": "RfFANSwHeCm3Pqe2AJ86Dk", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 61, "text": "The program should read a text file line by line. It should then check each line for the specified word, counting up each time the word is found. Finally, it should output the number of occurrences of the word.", "answer_id": "nzxdtWmgQhRkHeVAanVogZ", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 62, "text": "The following Python function can be used to find the longest common subsequence of two input strings using dynamic programming:\ndef longest_common_subsequence(s1, s2):\n\"\"\"This function finds the longest common subsequence of two strings.\n\n Arguments:\n    s1 {string} -- The first string\n    s2 {string} -- The second string\n\n Returns:\n    {int} -- The length of the longest common subsequence\n\n\"\"\"\n    n1 = len(s1)\n    n2 = len(s2)\n  \n    # Creating an empty matrix to store the results\n    results = [[None]*(n2 + 1) for _ in range(n1 + 1)]\n  \n    # Initializing the first row to 0\n    for i in range(n1 + 1):\n        results[i][0] = 0\n  \n    # Filling the first row with 0\n    for i in range(n1):\n        results[i][0] = 0\n  \n    # Calculating the longest common subsequence\n    for i in range(n1 + 1):\n        for j in range(n2 + 1):\n            if (i == 0 or j == 0):\n                results[i][j] = 0\n            elif (s1[i-1] == s2[j-1] and (results[i-1][j-1] < (i-j) or results[i-1][j-1] == 0)):\n                results[i][j] = max(results[i-1][j-1] + 1, i+j)\n            else:\n                results[i][j] = 0\n  \n    return results[n1][n2]", "answer_id": "kRo6z5qmeiSM8Xz6BPJHLC", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 63, "text": "The following regular expression can be used to validate an email address in Python:\n^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$", "answer_id": "7kj8odEE79UBdR4pT6u6TY", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 64, "text": "Here is a program to find the nth Fibonacci number using dynamic programming:\n1. Set a variable 'n' to the nth Fibonacci number.\n2. Set two other variables 'a' and 'b' to 0 and 1 respectively.\n3. Set a loop from 0 to (n-1).\n4. In the loop, set another loop from 0 to (a+b).\n5. In the inner loop, if 'a' is even and 'b' is odd, set 'a' to a+1 and 'b' to b+1.\n6. If 'a' is odd and 'b' is even, set 'a' to a-1 and 'b' to b-1.\n7. After the inner loop, set 'n' to 'a' and break out of the outer loop.\n8. Print the value of 'n'.\nThis program uses the idea of dynamic programming to compute the nth Fibonacci number. The program first sets two auxiliary variables 'a' and 'b' to 0 and 1 respectively. Then it sets a loop from 0 to (n-1). In the loop, it sets another loop from 0 to (a+b). In the inner loop, if 'a' is even and 'b' is odd, it sets 'a' to a+1 and 'b' to b+1. If 'a' is odd and 'b' is even, it sets 'a' to a-1 and 'b' to b-1. After the inner loop, it sets 'n' to 'a' and breaks out of the outer loop. Finally, it prints the value of 'n'.", "answer_id": "UULZ4GLz9j9ot7tN7x3ySD", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 65, "text": "A binary search algorithm can be used to find a specific element in a sorted array by repeatedly splitting the array in half and examining the middle element to determine whether the target element is greater than, less than, or equal to it. If the target element is found, the algorithm immediately returns the index of the element. If it isn\u2019t, the array is split in half, with one half discarded based on the comparison. The process is repeated until the element is found or the array is completely searched.", "answer_id": "Rh8P3bxSJx4p6nCZkSZBrh", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 66, "text": "Implementing a queue using two stacks in Python is relatively simple. First, you need to create two stacks, which will act as the queue. Next, you need to define an enqueue and dequeue method, which will add and remove items from the queue. Finally, you need to create an instance of the queue class and start using it.", "answer_id": "R3aYUUtY46ht4ToU8US96x", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 67, "text": "The program can be implemented using two nested for loops. The inner loop can be used to iterate through the elements of the first array, while the outer loop can be used to iterate through the elements of the second array. If the two elements being compared are the same, the program should mark those elements with a \"found\" flag and move on. Once the inner loop completes, the program should check if the \"found\" flag is set for all elements in the second array. If it is, then the common elements have been found.", "answer_id": "MQJFbH3KoFaSMmPgmK9LPa", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 68, "text": "The value of f(2) is 1.", "answer_id": "dBWqryuzG6BHRdPgrWWpbH", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 69, "text": "x = 6.", "answer_id": "W74byrCbE7XiJWjcPTZS7x", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 70, "text": "The length of the line segment is 8 units.", "answer_id": "7WC5quGKdv8hADCxWaWZ57", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 71, "text": "Yes, I can help you write a formal email to a potential business partner proposing a joint venture. Here's a template that you can use:\nSubject Line: Proposing a Joint Venture\nDear [Name],\nI am writing to propose a joint venture between our two companies. I believe that by combining our resources, we can create a powerful and profitable partnership. Our combined experience and knowledge in the industry will help us to create something unique and special.\nI am confident that our partnership will bring mutual benefits, such as increased profits and access to new markets. Please let me know if you are interested, and we can discuss the details.\nThank you for your time and consideration. I look forward to hearing from you soon.\nSincerely,\n[Your Name]", "answer_id": "AZER7D3RKZ9F9SXHiMjdqM", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 72, "text": "Yes, I can help you write a resignation letter to your current employer. The letter should be written in a polite and professional tone and should express your appreciation for the opportunities and experiences you have gained during your employment. It should also include a clear statement of your intention to resign and the date of your last day. Here is a template you can use:\nDear [Employer],\nI am writing to inform you of my intention to resign from my position as [Position], effective [date]. I have appreciated my time here and I am grateful for the opportunities and experiences I have gained during my employment. \nI thank you for your support and understanding.\nSincerely, [Your Name]", "answer_id": "MSrdDafr77UvSHCnsPMSP3", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 73, "text": "The letter of recommendation should be structured in a formal manner. Begin by introducing the student and explaining your relationship to them. Describe their qualifications, including their academic performance and relevant skills, and mention any particular accomplishments or awards they have received. Explain why the student is a strong candidate for the program and how they will make a positive contribution. End the letter by reaffirming your recommendation and offering your contact information for further questions.", "answer_id": "hxkjUkDkXhGP78Vo74B4WE", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 74, "text": "Dear valued customers, \nWe are excited to announce the launch of our new software solution \u2013 a revolutionary product designed to transform the way you do business! Our new software is an intuitive and powerful tool that can help you streamline processes, save time and money, and increase overall productivity. \nAt [Company Name], we are committed to helping you stay ahead of the competition, and we believe that our new software solution is the perfect tool to help you achieve your goals. Our experienced team of professionals has worked hard to ensure that this product meets the highest standards of quality, and we are confident that it will make a real difference for your business. \nWe invite you to join us in this journey of transformation and discover how our new software can help you achieve your vision. Sign up now and get a free demo to learn more about this revolutionary product. \nThank you for your trust and support.", "answer_id": "hv9jpG9PxeJ9AnFdNzH3Jv", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 75, "text": "mer,\nWe apologize for the delay you experienced with your order and thank you for your patience. We have identified the cause of the issue and have taken steps to ensure it does not happen again. We value your business and hope to be able to serve you better in the future.", "answer_id": "X5BVHT5pFpWxB88SzERrEG", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 76, "text": "The video will explore the history and cultural significance of jazz music. We will begin by discussing its roots in West African cultural music traditions and its journey through the 19th and 20th centuries, focusing on the key musicians, composers, and genres that have defined the genre. We will also explore the impact of jazz on popular culture, such as in film and literature, as well as its influence on other musical styles. Finally, we will examine the legacy of jazz and its continued importance today.", "answer_id": "Af2SufKwvDBriNXnZX9UoP", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 77, "text": "I have composed a travel blog post about my recent trip to Hawaii. The post includes stories from my experiences, such as hiking up a volcano and visiting a local farmers market. I also included plenty of photos to give the reader a sense of place. I highlighted some of the unique cultural experiences, such as an evening spent listening to live Hawaiian music, as well as must-see attractions, like the beautiful beaches and lush rainforests. I ended the post with a personal reflection on the trip and why it was so special to me.", "answer_id": "HVnqVdSGRwQfqSsiYxtTTn", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 78, "text": "I recently watched the science fiction movie \"The Future is Blue\" and it was an incredible cinematic experience. The movie follows the story of a scientist who travels back in time to save the world from a deadly virus. The plot was interesting and engaging, with plenty of twists and turns to keep you guessing. The characters were also well developed and likable, making it easy to get invested in their story. The special effects were stunning and added a lot of visual oomph to the movie. All in all, I highly recommend this movie - it's an exciting and thought-provoking ride from start to finish.", "answer_id": "8hcmmjzyMyhFJ8ZwqJJfvw", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 79, "text": "The podcast episode would begin by introducing the topic of how streaming platforms have changed the music industry. This would include discussing the impact of streaming services like Spotify and Apple Music on music consumption, the effect on musicians' ability to make money, and how streaming has changed the way people find and discover music. The episode would then explore the effects of streaming on the music industry from multiple angles, such as the impact on musicians and songwriters, how streaming has changed the sound of popular music, and the implications for the music industry's future. Finally, the episode would conclude by discussing how streaming has both benefited and hurt the music industry, and what the future holds for music consumption.", "answer_id": "CU6gGfJqQmwZNxEG2n2Uda", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"question_id": 80, "text": "The symphony concert was a wonderful experience. The orchestra played beautifully, with a rich and soulful sound that filled the hall. The audience was captivated, and you could feel the energy of the music in the air. The performance was a perfect balance of classic and modern pieces, and the conductor's passion was contagious. The atmosphere was festive, and the encore performance was a perfect way to end the evening. All in all, it was a brilliant concert and a delight for the ears.", "answer_id": "5SWbCoLV6NRrq5RtWQK4jp", "model_id": "alpaca-13b:v1", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
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@@ -1,80 +0,0 @@
-{"answer_id": "3oW4JY265ZPJGTYi2CgRYF", "model_id": "bard:20230327", "question_id": 1, "text": "Here are some tips on how to improve your time management skills:\n\n* **Set goals and prioritize your tasks.** What do you need to accomplish today, this week, and this month? Once you know what needs to be done, you can start to prioritize your tasks. Decide which tasks are most important and need to be done first.\n* **Break down large tasks into smaller, more manageable ones.** This will make them seem less daunting and help you to get started.\n* **Set deadlines for yourself and stick to them.** This will help you to stay on track and avoid procrastination.\n* **Take breaks when you need them.** Don't try to work for hours on end without taking a break. Getting up and moving around, or taking a few minutes to relax and clear your head, will help you to stay focused and productive.\n* **Don't be afraid to say no.** If you have too much on your plate, it's okay to say no to new commitments. It's better to focus on the tasks that you already have than to take on too much and end up feeling overwhelmed.\n* **Automate tasks whenever possible.** This will free up your time so that you can focus on more important things.\n* **Get organized.** This will help you to find what you need when you need it and avoid wasting time looking for things.\n* **Take care of yourself.** Make sure to get enough sleep, eat healthy foods, and exercise regularly. This will help you to stay focused and productive.\n\nTime management is a skill that takes time and practice to develop. But by following these tips, you can improve your time management skills and get more done in less time.", "metadata": {}}
-{"answer_id": "CtDuDqypbL958TQork7v54", "model_id": "bard:20230327", "question_id": 2, "text": "There are many effective ways to deal with stress. Some of the most common include:\n\n* **Exercise.** Exercise releases endorphins, which have mood-boosting effects. Aim for at least 30 minutes of moderate-intensity exercise most days of the week.\n* **Eat a healthy diet.** A healthy diet can help improve your overall mood and well-being. Make sure to eat plenty of fruits, vegetables, and whole grains.\n* **Get enough sleep.** Sleep is essential for reducing stress and improving your mood. Aim for 7-8 hours of sleep each night.\n* **Talk to someone.** Talking to a friend, family member, therapist, or counselor can help you process your thoughts and feelings.\n* **Practice relaxation techniques.** Relaxation techniques such as yoga, meditation, and deep breathing can help you calm down and manage stress.\n* **Take a break.** If you're feeling overwhelmed, take a few minutes to step away from the situation and relax. Go for a walk, listen to music, or do something else that you enjoy.\n\nIt's important to find what works best for you and to make time for these activities on a regular basis. Stress is a normal part of life, but it's important to manage it in a healthy way.", "metadata": {}}
-{"answer_id": "jqb5c6eBeyVTFo7SsqxqHA", "model_id": "bard:20230327", "question_id": 3, "text": "Here are some of the main differences between Python and JavaScript programming languages:\n\n* **Python is a general-purpose, high-level programming language, while JavaScript is a text-based, object-oriented programming language.** Python is designed to be easy to read and learn, while JavaScript is more complex.\n* **Python is dynamically typed, while JavaScript is statically typed.** This means that the type of a variable in Python can change at runtime, while the type of a variable in JavaScript is fixed at compile time.\n* **Python has a large standard library, while JavaScript does not.** The standard library contains a wide range of modules that can be used for common tasks, such as file I/O, networking, and data manipulation.\n* **Python is an interpreted language, while JavaScript is a compiled language.** This means that Python code is executed line-by-line by an interpreter, while JavaScript code is converted into machine code before it is executed.\n* **Python is used for a wide range of applications, including web development, data science, and artificial intelligence.** JavaScript is primarily used for web development, but it can also be used for other applications, such as desktop development and mobile development.\n\nUltimately, the best language for you to learn depends on your specific needs and goals. If you are new to programming, Python is a good choice because it is easy to learn and use. If you want to develop web applications, JavaScript is a good choice because it is the most popular language for front-end development.", "metadata": {}}
-{"answer_id": "P5rC8W6gTuD4aY6HR5HQj9", "model_id": "bard:20230327", "question_id": 4, "text": "Here are some tips on how to increase your productivity while working from home:\n\n* **Set up a dedicated workspace.** This will help you stay focused and avoid distractions. If you can, set up your workspace in a quiet area of your home.\n* **Start your day early.** This will give you time to get organized and tackle your most important tasks.\n* **Take breaks.** It's important to take breaks throughout the day to stretch, walk around, or get a snack. This will help you stay refreshed and focused.\n* **Avoid distractions.** This means turning off your phone, closing your email, and anything else that might take your attention away from your work.\n* **Take care of yourself.** Make sure to eat healthy, get enough sleep, and exercise regularly. This will help you stay healthy and productive.\n* **Reward yourself.** When you accomplish a task, give yourself a small reward, such as taking a break or watching a TV show. This will help you stay motivated.\n* **Communicate with your team.** Make sure to stay in touch with your team members and let them know what you're working on. This will help you stay on track and avoid duplication of effort.\n* **Set goals.** Having goals will help you stay focused and motivated. Make sure your goals are specific, measurable, achievable, relevant, and time-bound.\n* **Track your progress.** It's important to track your progress so you can see how you're doing. This will help you stay motivated and make adjustments as needed.\n* **Be patient.** Working from home can be a challenge, but it's important to be patient with yourself. It takes time to adjust to a new way of working.\n\nFollowing these tips can help you increase your productivity while working from home.", "metadata": {}}
-{"answer_id": "3uaqwfbwxtyDdqB8UVN3jM", "model_id": "bard:20230327", "question_id": 5, "text": "Quantum computing is a field of computer science that deals with the development of quantum algorithms and quantum computers. Quantum algorithms are algorithms that can be executed by quantum computers, and quantum computers are computers that can execute quantum algorithms.\n\nQuantum computing is based on the principles of quantum mechanics, which is the study of the behavior of matter and energy at the atomic and subatomic level. Quantum mechanics is a very different way of thinking about the world than classical mechanics, which is the study of the behavior of matter and energy at the macroscopic level.\n\nOne of the key concepts in quantum mechanics is superposition. Superposition means that a quantum system can exist in multiple states at the same time. For example, an electron can be in a state of superposition where it is both a wave and a particle.\n\nAnother key concept in quantum mechanics is entanglement. Entanglement means that two quantum systems can be linked together in such a way that they share the same fate, even if they are separated by a large distance.\n\nQuantum computing is still in its early stages of development, but it has the potential to revolutionize many fields, including cryptography, chemistry, and materials science.\n\nHere are some of the basics of quantum computing:\n\n* Quantum bits, or qubits, are the basic unit of information in quantum computing. Qubits can be in a superposition of states, meaning they can be both 0 and 1 at the same time.\n* Quantum gates are the basic operations that can be performed on qubits. Quantum gates can be used to create superpositions, entangle qubits, and measure qubits.\n* Quantum algorithms are algorithms that can be executed by quantum computers. Quantum algorithms can be used to solve problems that are difficult or impossible to solve with classical computers.\n* Quantum computers are computers that can execute quantum algorithms. Quantum computers are still in their early stages of development, but they have the potential to revolutionize many fields, including cryptography, chemistry, and materials science.", "metadata": {}}
-{"answer_id": "HTRJ5q97DrgzoSofxkgedz", "model_id": "bard:20230327", "question_id": 6, "text": "The main difference between plant-based and animal-based protein sources is that plant-based protein sources do not contain all nine essential amino acids, while most meat proteins do. (Most plant-based protein sources tend to lack at least 1 of the essential amino acids, although there are some that are considered complete.)\n\nAnimal proteins are considered complete proteins, meaning they contain all nine essential amino acids that the body cannot produce on its own. Plant proteins, on the other hand, are often incomplete proteins, meaning they do not contain all nine essential amino acids. However, there are some plant-based protein sources that are considered complete, such as quinoa and buckwheat.\n\nAnimal proteins also tend to be higher in saturated fat and cholesterol than plant proteins. Plant proteins are lower in saturated fat and cholesterol, and they also contain fiber, which can help to lower cholesterol levels.\n\nThere are several benefits to eating plant-based protein sources. Plant-based protein sources are often lower in calories and fat than animal protein sources. They are also a good source of fiber, which can help to keep you feeling full and satisfied. Plant-based protein sources can also help to lower cholesterol levels and reduce the risk of heart disease.\n\nThere are also some benefits to eating animal-based protein sources. Animal protein sources are a good source of B vitamins, iron, and zinc. They can also help to build muscle mass and strength. However, it is important to choose lean cuts of meat and poultry to reduce saturated fat and cholesterol intake.\n\nUltimately, the best type of protein for you depends on your individual needs and preferences. If you are looking for a diet that is low in calories and fat, plant-based protein sources are a good option. If you are looking for a diet that is high in protein and B vitamins, animal protein sources are a good option.", "metadata": {}}
-{"answer_id": "EhhyKNc3m8c9tnxm8uPfmt", "model_id": "bard:20230327", "question_id": 7, "text": "Here are some tips on how to develop your critical thinking skills:\n\n* **Be open to new ideas and information.** Don't be afraid to question everything, even your own beliefs.\n* **Be curious and ask questions.** The more you learn, the better equipped you'll be to think critically about the information you're presented with.\n* **Be skeptical of information that seems too good to be true.** If something seems too good to be true, it probably is.\n* **Be aware of your own biases.** We all have biases, but it's important to be aware of them so that they don't cloud your judgment.\n* **Be able to identify and evaluate arguments.** When you're presented with an argument, be able to identify the premises and the conclusion, and evaluate whether the premises support the conclusion.\n* **Be able to think creatively and solve problems.** Critical thinking is not just about analyzing information, but also about being able to come up with new ideas and solutions to problems.\n* **Practice regularly.** The more you practice critical thinking, the better you'll become at it.\n\nThere are many ways to develop your critical thinking skills. You can read books and articles on the subject, take classes, or even just spend time thinking about the world around you. The important thing is to be willing to learn and grow.", "metadata": {}}
-{"answer_id": "JQXPknRQd24NUPKZxi6RNf", "model_id": "bard:20230327", "question_id": 8, "text": "The education sector faces many challenges today, including:\n\n* **Funding:** Schools are often underfunded, which can lead to a lack of resources and support for students and teachers.\n* **Class size:** Classrooms are often overcrowded, which can make it difficult for teachers to give individual students the attention they need.\n* **Teacher shortages:** There is a shortage of qualified teachers in many areas, which can lead to schools hiring teachers who are not adequately prepared.\n* **Student achievement:** The achievement gap between students from different socioeconomic backgrounds persists, which means that some students are not getting the education they need to succeed.\n* **School safety:** School shootings and other acts of violence have become more common, which has made schools a less safe place for students and staff.\n* **Technology:** Technology is changing the way we learn, but it also presents new challenges for schools and educators.\n* **Charter schools and voucher programs:** Charter schools and voucher programs have become more popular in recent years, which has led to competition between public and private schools.\n\nThese are just some of the challenges that the education sector faces today. It is important to address these challenges in order to ensure that all students have access to a quality education.", "metadata": {}}
-{"answer_id": "Lb3C2xQKdLCqFj4v3rmaof", "model_id": "bard:20230327", "question_id": 9, "text": "There are many factors that influence consumer behavior, but some of the primary ones include:\n\n* **Personal factors:** These factors include a person's age, gender, income, occupation, and lifestyle. For example, a young person with a high income may be more likely to spend money on luxury items, while an older person with a lower income may be more likely to spend money on necessities.\n* **Psychological factors:** These factors include a person's motivation, perception, learning, attitudes, and beliefs. For example, a person who is motivated by a desire for self-improvement may be more likely to buy a product that they believe will help them achieve their goals.\n* **Social factors:** These factors include a person's family, friends, and social class. For example, a person who is influenced by their family may be more likely to buy a product that they believe their family will approve of.\n* **Cultural factors:** These factors include a person's culture, subculture, and social class. For example, a person who is from a culture that values luxury may be more likely to buy a product that is considered to be a luxury item.\n* **Economic factors:** These factors include a person's income, savings, and debt. For example, a person who has a high income may be more likely to buy a product that is expensive.\n\nIt is important to note that these factors are not always the same for everyone. A person's individual circumstances and preferences will also play a role in their consumer behavior.", "metadata": {}}
-{"answer_id": "DhuZJtL3jhnrsTBvDct9oV", "model_id": "bard:20230327", "question_id": 10, "text": "There are many effective strategies for conflict resolution in the workplace. Some of the most common include:\n\n* **Communication:** The first step in resolving conflict is to communicate effectively with all parties involved. This means listening to each other's perspectives, sharing your own feelings and needs, and being open to compromise.\n* **Active listening:** Active listening is a key skill in conflict resolution. It involves paying attention to what the other person is saying, both verbally and nonverbally, and trying to understand their point of view.\n* **Problem-solving:** Once you have a better understanding of the conflict, you can start to work on solutions. This may involve brainstorming ideas, negotiating compromises, or finding ways to meet everyone's needs.\n* **Time-out:** If things start to get heated, it may be helpful to take a time-out. This gives everyone a chance to cool down and collect their thoughts.\n* **Mediation:** If you are unable to resolve the conflict on your own, you may want to consider mediation. This is a process in which a neutral third party helps you and the other person to come to a resolution.\n* **Arbitration:** If mediation is unsuccessful, you may want to consider arbitration. This is a process in which a neutral third party makes a decision about how to resolve the conflict.\n\nIt is important to remember that there is no one-size-fits-all approach to conflict resolution. The best strategy will vary depending on the specific situation. However, by following these general tips, you can increase the chances of resolving conflict peacefully and effectively.", "metadata": {}}
-{"answer_id": "mDSj4BKim2eANUnEjW7xBm", "model_id": "bard:20230327", "question_id": 11, "text": "The use of single-use plastic bottles has a number of negative environmental and health implications.\n\nEnvironmentally, single-use plastic bottles contribute to the problem of plastic pollution. Every year, millions of plastic bottles end up in landfills, incinerators, or the ocean. These bottles can take hundreds of years to decompose, and they can leach harmful chemicals into the environment.\n\nHealth-wise, the use of single-use plastic bottles can increase the risk of exposure to harmful chemicals. These chemicals can leach into the water from the bottle, or they can be released into the air when the bottle is heated. Exposure to these chemicals can cause a number of health problems, including cancer, reproductive problems, and neurological problems.\n\nUsing a reusable bottle instead of a single-use plastic bottle can help to reduce the environmental and health impacts of plastic pollution. Reusable bottles are made from materials that are less harmful to the environment, and they can be used over and over again. This helps to reduce the amount of plastic waste that ends up in landfills, incinerators, or the ocean.\n\nReusable bottles are also better for human health. They do not leach harmful chemicals into the water, and they do not release chemicals into the air when they are heated. This helps to reduce the risk of exposure to harmful chemicals.\n\nOverall, using a reusable bottle instead of a single-use plastic bottle is a better choice for both the environment and human health.", "metadata": {}}
-{"answer_id": "MnkceSK7WwyXqAhbuKVYX7", "model_id": "bard:20230327", "question_id": 12, "text": "There are many factors to consider when designing an inclusive and accessible public transportation system. Some of the most important factors include:\n\n* **Accessibility**. The system must be accessible to people with disabilities, including those who use wheelchairs, mobility scooters, or other assistive devices. This means that all aspects of the system, including the vehicles, the stations, and the signage, must be designed to be accessible.\n* **Affordability**. The system must be affordable for all users, including low-income and marginalized communities. This means that the fares must be reasonable and that there must be a variety of payment options available.\n* **Convenience**. The system must be convenient to use, with frequent service and easy connections between different modes of transportation. This means that the system must be well-integrated with other transportation options, such as car sharing and bike lanes.\n* **Safety**. The system must be safe for all users, including women and children. This means that the vehicles must be well-maintained and that there must be adequate security measures in place.\n* **Sustainability**. The system must be sustainable, both environmentally and economically. This means that the system must use renewable energy sources and that it must be affordable to operate.\n\nThese are just some of the factors that must be considered when designing an inclusive and accessible public transportation system. By taking these factors into account, we can create a system that is accessible to everyone and that meets the needs of all communities.", "metadata": {}}
-{"answer_id": "EsyaBVpTN8BGbTSiFMnZUF", "model_id": "bard:20230327", "question_id": 13, "text": "Fiscal and monetary policies can be used by governments to combat economic recessions in a number of ways.\n\n* **Fiscal policy** refers to the use of government spending and taxation to influence the economy. During a recession, governments can use fiscal policy to increase demand and stimulate the economy. This can be done by increasing government spending on infrastructure, education, and healthcare. Governments can also reduce taxes to give consumers more money to spend.\n* **Monetary policy** refers to the use of interest rates and the money supply to influence the economy. During a recession, central banks can use monetary policy to lower interest rates and make it cheaper for businesses to borrow money. This can encourage businesses to invest and expand, which can create jobs and boost the economy.\n\nBoth fiscal and monetary policies can be effective in combatting economic recessions. However, the effectiveness of these policies depends on a number of factors, including the severity of the recession, the underlying causes of the recession, and the effectiveness of the policies themselves.\n\nIn recent years, there has been a debate about the best way to use fiscal and monetary policies to combat economic recessions. Some economists argue that fiscal policy should be the primary tool used to combat recessions, while others argue that monetary policy should be the primary tool. There is no single answer to this question, as the best policy approach will vary depending on the specific circumstances.\n\nHowever, both fiscal and monetary policies can be effective in combatting economic recessions, and governments should use both tools to help stimulate the economy and create jobs.", "metadata": {}}
-{"answer_id": "dX8M752A6tzqLg9KhwgG5p", "model_id": "bard:20230327", "question_id": 14, "text": "Language and cultural barriers can affect the way people communicate and form relationships in multicultural societies in a number of ways.\n\n* **Language barriers** can make it difficult for people to understand each other's words and meanings. This can lead to misunderstandings and frustration, and can make it difficult to build trust and rapport.\n* **Cultural barriers** can make it difficult for people to understand each other's values, beliefs, and customs. This can lead to misunderstandings and conflict, and can make it difficult to build strong relationships.\n* **Language and cultural barriers** can also make it difficult for people to feel comfortable and included in multicultural societies. This can lead to feelings of isolation and alienation, and can make it difficult to participate fully in the community.\n\nThere are a number of things that can be done to overcome language and cultural barriers in multicultural societies. These include:\n\n* **Learning about other cultures**. This can help people to understand and appreciate different cultures, and can make it easier to build relationships with people from other cultures.\n* **Learning about other languages**. This can help people to communicate effectively with people from other cultures, and can help to build trust and rapport.\n* **Building bridges**. This can help to bring people from different cultures together, and can help to create a more inclusive and supportive environment.\n* **Celebrating diversity**. This can help to promote understanding and appreciation for different cultures, and can help to create a more vibrant and exciting community.", "metadata": {}}
-{"answer_id": "dzwhq5XbaEBVpargyczz9B", "model_id": "bard:20230327", "question_id": 15, "text": "Artificial intelligence (AI) can be used to improve the quality and efficiency of healthcare delivery in a number of ways. For example, AI can be used to:\n\n* **Diagnose diseases:** AI-powered algorithms can be trained to analyze large amounts of medical data, such as patient records and medical images, to identify patterns that may indicate disease. This can help doctors to diagnose diseases more accurately and quickly, which can lead to better patient outcomes.\n* **Treat diseases:** AI can also be used to develop new treatments for diseases. For example, AI-powered algorithms can be used to screen large libraries of compounds for potential drug candidates. This can help to speed up the drug discovery process and bring new drugs to market more quickly.\n* **Monitor patients:** AI can be used to monitor patients for signs of disease progression or complications. For example, AI-powered algorithms can be used to analyze data from wearable devices, such as smartwatches and fitness trackers, to identify changes in heart rate, sleep patterns, and other vital signs. This can help doctors to identify problems early on and intervene before they become serious.\n* **Provide administrative support:** AI can also be used to provide administrative support to healthcare providers. For example, AI-powered chatbots can be used to answer patient questions, schedule appointments, and process insurance claims. This can free up healthcare providers to spend more time with patients and focus on providing care.\n\nOverall, AI has the potential to revolutionize healthcare delivery by improving the quality and efficiency of care. By automating tasks and providing support to healthcare providers, AI can help to free up time and resources, which can lead to better patient outcomes.", "metadata": {}}
-{"answer_id": "8zqxUtHxgtoHBkbf2bkqNW", "model_id": "bard:20230327", "question_id": 16, "text": "CRISPR-Cas9 is a gene editing technology that can be used to make precise changes to DNA. It works by cutting DNA at a specific location and then allowing the cell's natural DNA repair mechanisms to take over. This can be used to delete, add, or replace genes.\n\nCRISPR-Cas9 has a wide range of potential applications, including:\n\n* Treating genetic diseases: CRISPR-Cas9 could be used to correct genetic mutations that cause diseases such as cystic fibrosis, sickle cell anemia, and Huntington's disease.\n* Improving crop yields: CRISPR-Cas9 could be used to make crops more resistant to pests and diseases, or to increase the nutritional content of crops.\n* Creating new biofuels: CRISPR-Cas9 could be used to engineer bacteria that can produce biofuels from waste materials.\n\nHowever, CRISPR-Cas9 also raises ethical concerns, such as:\n\n* The potential to create designer babies: CRISPR-Cas9 could be used to edit the genes of human embryos, potentially creating \"designer babies\" with desired traits.\n* The potential to create genetically modified organisms (GMOs): CRISPR-Cas9 could be used to edit the genes of plants and animals, creating GMOs that some people believe could pose risks to human health or the environment.\n* The potential to create new weapons: CRISPR-Cas9 could be used to create new biological weapons that could be used to harm or kill people.\n\nIt is important to weigh the potential benefits and risks of CRISPR-Cas9 before using it.", "metadata": {}}
-{"answer_id": "WJc37t4n5PqmKKS3V4eMG2", "model_id": "bard:20230327", "question_id": 17, "text": "Vaccines work by teaching your body's immune system how to recognize and fight off a disease. When you get a vaccine, your body produces antibodies against the disease. These antibodies will stay in your blood and help protect you if you are ever exposed to the disease again.\n\nVaccinations are very effective at preventing diseases. For example, the measles vaccine is 97% effective at preventing measles, and the mumps vaccine is 88% effective at preventing mumps.\n\nVaccinations also help protect communities from infectious diseases. When a large number of people in a community are vaccinated, it makes it harder for the disease to spread. This is because even if a few people in the community are infected, the disease will not be able to spread to as many people because most people are immune. This is called herd immunity.\n\nHerd immunity is important because it can help protect people who cannot be vaccinated, such as young children, pregnant women, and people with certain medical conditions. These people are at increased risk of getting sick if they are exposed to a disease.\n\nHerd immunity can also help protect people who are vaccinated but whose immune system does not respond well to the vaccine. These people may still get sick if they are exposed to the disease, but they are less likely to get seriously ill.\n\nVaccination is one of the most important ways to protect yourself and your community from infectious diseases. If you are not sure whether you are up-to-date on your vaccinations, talk to your doctor.", "metadata": {}}
-{"answer_id": "CvVLf8FgoHywJy8j8JJ4qL", "model_id": "bard:20230327", "question_id": 18, "text": "Social media platforms have a significant influence on the way people consume and share news. They provide a platform for people to share news stories with their friends and followers, and they also allow users to curate their own news feeds. This can lead to people being exposed to a wider range of news stories than they would otherwise be, but it can also make it more difficult for them to distinguish between legitimate news sources and fake news.\n\nSocial media platforms also use algorithms to determine what news stories are shown to users. These algorithms are designed to keep users engaged on the platform, and they often prioritize news stories that are likely to get a lot of attention, such as those that are sensational or controversial. This can lead to people being exposed to a lot of news stories that are not necessarily accurate or reliable.\n\nThe spread of misinformation on social media is a serious problem. Fake news stories can be designed to look like they are from legitimate news sources, and they can spread quickly and easily on social media. This can have a significant impact on public opinion, and it can also make it more difficult for people to trust the information they see online.\n\nThere are a number of things that can be done to combat the spread of misinformation on social media. One is to educate people about how to identify fake news stories. Another is to make it easier for people to report fake news stories. And finally, social media platforms can do more to remove fake news stories from their platforms.\n\nIt is important to remember that social media is not the only way that people get news. Many people still get their news from traditional sources, such as newspapers and television. However, social media is becoming an increasingly important part of how people consume news, and it is important to understand how it is changing the way people consume and share news.", "metadata": {}}
-{"answer_id": "P5rytR6vTJjxgWxRoxT3vX", "model_id": "bard:20230327", "question_id": 19, "text": "Cultural, social, and economic factors influence people's food choices in many ways. Here are a few examples:\n\n* **Culture** plays a role in food choices by shaping what people consider to be acceptable or desirable to eat. For example, some cultures have strong taboos against eating certain foods, such as pork or beef.\n* **Social factors** such as family, friends, and peers can also influence food choices. People are more likely to eat foods that are popular among their social group.\n* **Economic factors** such as the cost of food and the availability of healthy food options can also affect food choices. People who are on a tight budget may choose to eat less expensive, less healthy foods.\n\nThis knowledge can be used to promote healthier diets by making healthy foods more accessible and affordable, and by educating people about the importance of eating a healthy diet. For example, governments can provide subsidies for healthy foods, or they can build more grocery stores in underserved communities. Schools can also teach students about nutrition and healthy eating habits.\n\nIn addition, individuals can make healthier food choices by being more mindful of the cultural, social, and economic factors that influence their decisions. For example, people can try to eat more foods that are culturally appropriate, and they can avoid foods that are not healthy simply because they are popular. People can also try to eat more healthy foods that are affordable, and they can make healthy food choices even when they are on a tight budget.\n\nBy making healthy food choices, people can improve their overall health and well-being.", "metadata": {}}
-{"answer_id": "5biCd7QRZP6rquaz8eC9Vm", "model_id": "bard:20230327", "question_id": 20, "text": "Natural selection is the process by which organisms with favorable traits survive and reproduce, passing on those traits to their offspring. This process leads to the evolution of species over time.\n\nNatural selection is based on the following principles:\n\n* Variation: There is variation within a population of organisms. This variation can be caused by genetic mutation, environmental factors, or both.\n* Differential reproduction: Not all organisms survive and reproduce equally. Some organisms are more likely to survive and reproduce than others.\n* Heredity: The traits that an organism inherits from its parents are passed down to its offspring.\n\nNatural selection can lead to the evolution of new species. For example, if a population of giraffes is living in an environment with a lot of tall trees, the giraffes with longer necks will be more likely to survive and reproduce. This is because they will be able to reach the leaves on the taller trees. Over time, the giraffes with longer necks will become more common in the population. This is an example of natural selection leading to the evolution of a new species, in this case, giraffes with longer necks.\n\nNatural selection can also lead to the adaptation of species to their environment. For example, if a population of fish is living in an environment with a lot of predators, the fish with darker colors will be more likely to survive and reproduce. This is because they will be less visible to the predators. Over time, the fish with darker colors will become more common in the population. This is an example of natural selection leading to the adaptation of a species to its environment.\n\nNatural selection is a powerful mechanism that can lead to the evolution and adaptation of species. It is a key part of the process of life on Earth.", "metadata": {}}
-{"answer_id": "363RwB6kr8nV6qFNdjXZnS", "model_id": "bard:20230327", "question_id": 21, "text": "I would introduce myself as follows:\n\n\"I am Sir [your name], a knight in the service of [your lord's name]. I have come to this banquet to pay my respects to your majesty and to enjoy your hospitality.\"\n\nI would then bow to the king or queen and take my seat at the table. If I was asked to speak, I would give a brief account of my deeds and accomplishments.\n\nI would be careful to avoid anything that could be seen as boastful or arrogant. I would also be mindful of the customs and etiquette of the time, and make sure to behave in a way that would be considered appropriate.", "metadata": {}}
-{"answer_id": "gDnYxMu5Dd52xhMqQAJaZP", "model_id": "bard:20230327", "question_id": 22, "text": "Here is what I would say to my crew to motivate them to search for hidden treasure:\n\n\"Mates, I know you're all tired and hungry, but we have a chance to find something that will make us all rich beyond our wildest dreams. There's a treasure buried out there, and I know we can find it. We just need to keep our heads up and keep searching. I believe in you, and I know you can do this. So let's get to it!\"\n\nI would also try to appeal to their sense of adventure and excitement. I would tell them about the stories I've heard of other pirates who have found great treasure, and how they have used it to live a life of luxury and adventure. I would tell them that this could be their chance to live that life too.\n\nFinally, I would try to motivate them by offering them a share of the treasure. I would tell them that if we find the treasure, they will all be rich beyond their wildest dreams. I would tell them that they will be able to buy anything they want, and that they will never have to worry about money again.\n\nI believe that if I can motivate my crew with these words, they will be more likely to search for the hidden treasure.", "metadata": {}}
-{"answer_id": "kCV5RSrnmmTyv3HA5oU38P", "model_id": "bard:20230327", "question_id": 23, "text": "If I were a Shakespearean character, I would declare my love for someone in a soliloquy by speaking from the heart and expressing my true feelings. I would use poetic language and imagery to describe my love, and I would convey the depth of my emotion. I would also be sure to include a declaration of my love, so that my beloved would know how I feel.\n\nHere is an example of how I might declare my love in a soliloquy:\n\nMy dearest, I love you with all my heart.\nYou are the sun that lights my days,\nAnd the moon that guides me through the night.\nYou are the stars that sparkle in my eyes,\nAnd the rain that washes away my tears.\nYou are the wind that fills my sails,\nAnd the earth that grounds me.\nYou are my everything,\nAnd I will love you forever.\n\nI hope this helps!", "metadata": {}}
-{"answer_id": "CTGLYNAWrWeuCYZAR9bD2r", "model_id": "bard:20230327", "question_id": 24, "text": "I would tell the child that I was once a regular person, just like them. I went to school, played games, and had friends. But one day, I got into an accident. I was hit by a car and almost died. But instead of dying, I got superpowers! I can fly, I can lift heavy things, and I can even shoot lasers out of my eyes! I use my powers to help people and make the world a better place.\n\nI would also tell the child that I was inspired by other superheroes, like Superman and Batman. They showed me that even though bad things happen in the world, there are always people who are willing to help others. I wanted to be one of those people, so I decided to use my powers to make a difference.\n\nI would hope that the child would be inspired by my story and would want to use their own powers to help others. I would tell them that even though they don't have superpowers, they can still make a difference in the world by being kind to others, helping out when they can, and standing up for what they believe in.", "metadata": {}}
-{"answer_id": "Eh5qCENdftYf9JKrBspYna", "model_id": "bard:20230327", "question_id": 25, "text": "As a time traveler from the year 3000, I would tell people about the following technological advancements:\n\n* **Dyson spheres:** Dyson spheres are theoretical megastructures that would surround a star and harness its energy. They are still in the theoretical stage, but scientists believe that they could be built in the next few centuries.\n* **Interstellar travel:** Interstellar travel is the ability to travel between stars and planets. It is currently impossible with our current technology, but scientists are working on developing new technologies that could make it possible in the future.\n* **Artificial intelligence:** Artificial intelligence (AI) is a branch of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. AI has made great progress in recent years, and it is likely to continue to advance in the future.\n* **Robotics:** Robotics is the field of engineering that deals with the design, construction, operation, and application of robots. Robots are machines that can be programmed to perform a variety of tasks. They are already used in a variety of industries, and they are likely to become even more common in the future.\n* **Gene editing:** Gene editing is the ability to modify the genes of an organism. This technology has the potential to cure genetic diseases, improve crop yields, and even create new species of plants and animals.\n* **Virtual reality:** Virtual reality (VR) is a computer-generated simulation that can be similar to or completely different from the real world. VR is already being used for a variety of purposes, such as training, entertainment, and education. It is likely to become even more common in the future.\n\nThese are just a few of the technological advancements that I would tell people about. I am sure that there will be many other advancements that I cannot even imagine.", "metadata": {}}
-{"answer_id": "M6hCe8Z4JDpt75HZ5PsSWr", "model_id": "bard:20230327", "question_id": 26, "text": "Here is a possible description of the winning play in the final seconds of a championship game:\n\n\"With only seconds remaining in the game, the score is tied. The ball is on the opposing team's 40-yard line, and they have one last chance to score. The quarterback takes the snap and drops back to pass. He looks downfield and sees his wide receiver open. He throws the ball, and the wide receiver catches it in stride. He runs into the end zone for a touchdown! The crowd goes wild as the team celebrates their victory.\"\n\nThis is just one example of how a winning play in the final seconds of a championship game could be described. There are many other ways to describe the play, and the specific details of the play would depend on the specific game.", "metadata": {}}
-{"answer_id": "QCDsmbALfut5758pyiKvc5", "model_id": "bard:20230327", "question_id": 27, "text": "My signature dish is a pan-seared filet mignon wrapped in prosciutto and topped with a mushroom duxelles. It is served with a side of roasted potatoes and asparagus.\n\nThe filet mignon is cooked to perfection, and the prosciutto adds a salty, smoky flavor. The mushroom duxelles is a rich and flavorful sauce that pairs perfectly with the beef. The roasted potatoes and asparagus are a delicious and healthy accompaniment.\n\nThis dish is a true labor of love. I take great care in selecting the ingredients and preparing the dish. I want my guests to experience the best of what I have to offer.\n\nI am confident that you will enjoy this dish as much as I do. It is a perfect combination of flavors and textures that will leave you feeling satisfied.\n\nI hope you will consider my dish for your next competition. I believe it is a dish that would stand out from the competition and impress your judges.", "metadata": {}}
-{"answer_id": "NWUbhwZQCuXsuQimrjQRza", "model_id": "bard:20230327", "question_id": 28, "text": "I stand at the top of the world, my breath coming in ragged gasps. The air is thin and cold, but I don't care. I'm on top of Mount Everest, the highest point on Earth. I've made it.\n\nI look around at the vast expanse of snow and ice. The sun is just starting to rise, and the sky is a brilliant blue. The mountains around me are like giant sleeping giants.\n\nI feel a sense of awe and wonder. I've never seen anything like this before. It's so beautiful, so peaceful.\n\nI take a deep breath and savor the moment. I've worked so hard for this, and it's all worth it.\n\nI know that I'll never forget this feeling. It's the feeling of accomplishment, of reaching a goal, of overcoming adversity. It's the feeling of being alive.\n\nI turn and start my descent. I know that I'll never be able to stay at the top of the world forever. But I'll always remember this moment.", "metadata": {}}
-{"answer_id": "VYwSjZrSLW9ZSvqryyjEaB", "model_id": "bard:20230327", "question_id": 29, "text": "As a space colonist on Mars, my daily life is a constant struggle for survival. The challenges I face are many and varied, but the most pressing are the lack of oxygen, the extreme cold, and the radiation.\n\nI wake up each morning in a pressurized habitat, where I eat a quick breakfast and then get ready for the day's work. My first task is to check the air filters, as they are constantly being clogged by the dust and sand. Once that is done, I start my work on the crops. We have been able to grow some plants in the greenhouse, but they are not enough to sustain us. We are constantly on the lookout for new ways to grow food, as we know that our survival depends on it.\n\nAfter a long day of work, I return to the habitat for dinner and a few hours of relaxation. I am always tired, as the work is physically and mentally demanding. But I know that I have to keep going, as I am part of something bigger than myself. I am part of the first generation of humans to live on another planet, and that is an honor.\n\nThe challenges I face are many, but I am determined to overcome them. I know that the future of humanity depends on it.\n\nHere are some of the challenges I face as a space colonist on Mars:\n\n* **Lack of oxygen.** The atmosphere on Mars is very thin, and it contains mostly carbon dioxide. This means that I have to wear a pressurized suit whenever I go outside, and I have to bring my own oxygen with me.\n* **Extreme cold.** The average temperature on Mars is -63 degrees Celsius. This means that I have to wear multiple layers of clothing, and I have to keep my habitat heated.\n* **Radiation.** The surface of Mars is constantly bombarded by radiation from the sun and from space. This radiation can cause cancer, so I have to wear special shielding to protect myself.\n* **Dust storms.** The surface of Mars is very dusty, and there are frequent dust storms. These dust storms can block out the sun and make it difficult to breathe.\n* **Isolation.** Mars is very far away from Earth. This means that I am cut off from my family and friends, and I have to rely on my fellow colonists for support.\n\nDespite these challenges, I am determined to make a life for myself on Mars. I know that it will not be easy, but I am confident that I can overcome the challenges I face.", "metadata": {}}
-{"answer_id": "FA7PXuUbEVGKHaWpxaimy8", "model_id": "bard:20230327", "question_id": 30, "text": "I am a survivor in a post-apocalyptic world. I have been living on my own for the past few years, and I have learned to be resourceful and self-sufficient. I have a small garden where I grow my own food, and I have a few chickens that I raise for eggs. I also scavenge for food and supplies when I can.\n\nI have made a few allies in this world. There are a few other survivors who live in the area, and we help each other out when we can. We share food and supplies, and we look out for each other's safety. We are all trying to survive in this new world, and we know that we are stronger together.\n\nOne of my allies is a young woman named Sarah. She is a skilled hunter and gatherer, and she has taught me a lot about living off the land. She is also a kind and compassionate person, and she has always been there for me when I needed her.\n\nAnother one of my allies is a man named John. He is a former soldier, and he is a skilled fighter and tactician. He has taught me a lot about self-defense, and he has also helped me to develop my leadership skills. He is a loyal and trustworthy friend, and I am grateful to have him in my life.\n\nI am grateful for my allies, and I know that I am lucky to have them in my life. We are all trying to survive in this new world, and we know that we are stronger together.", "metadata": {}}
-{"answer_id": "j5EV5cZNsn9DcF6WsvXRzS", "model_id": "bard:20230327", "question_id": 31, "text": "Here are some ways to determine if a restaurant is popular among locals or mainly attracts tourists:\n\n* **Check the restaurant's website or social media pages.** If the restaurant has a lot of reviews and comments from locals, that's a good sign that it's popular among locals.\n* **Look for the restaurant's name in local publications or websites.** If the restaurant is mentioned in local newspapers or magazines, that's another sign that it's popular among locals.\n* **Ask locals for recommendations.** If you're looking for a good place to eat, ask locals for their recommendations. They're likely to know the best places to eat in their area.\n* **Look for the restaurant's location.** If the restaurant is located in a tourist area, that's a good sign that it mainly attracts tourists. However, if the restaurant is located in a residential area, that's a good sign that it's popular among locals.\n* **Look for the restaurant's atmosphere.** If the restaurant has a casual atmosphere, that's a good sign that it's popular among locals. However, if the restaurant has a formal atmosphere, that's a good sign that it mainly attracts tourists.\n\nKnowing whether a restaurant is popular among locals or mainly attracts tourists can be useful for a number of reasons. For example, if you're looking for a good place to eat that's authentic to the local culture, you'll want to find a restaurant that's popular among locals. On the other hand, if you're looking for a good place to eat that's convenient to your hotel, you'll want to find a restaurant that's popular among tourists.", "metadata": {}}
-{"answer_id": "2eAYCYmwTkPa3ejQDv8LyB", "model_id": "bard:20230327", "question_id": 32, "text": "There are a few subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed. Here are a few examples:\n\n* Using vague or general terms without providing any specific examples.\n* Asking questions that are already answered in the conversation.\n* Making statements that don't seem to be relevant to the topic.\n* Not making eye contact or appearing uncomfortable.\n* Fidgeting or making other nervous gestures.\n* Trying to change the subject or end the conversation quickly.\n\nIf you notice someone exhibiting these behaviors, it's possible that they are pretending to understand something they don't. If you're not sure, it's always best to err on the side of caution and ask them to clarify what they mean.", "metadata": {}}
-{"answer_id": "d562WYnhsvgJ8J6Ubitmvw", "model_id": "bard:20230327", "question_id": 33, "text": "There are a few reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app:\n\n* **They may not have a smartphone or GPS device.** Not everyone has a smartphone or GPS device, especially if they are traveling to a developing country or rural area.\n* **They may not have a reliable internet connection.** Even if you have a smartphone or GPS device, you may not have a reliable internet connection to use them. This can be a problem in areas with poor cell phone reception or where internet access is expensive.\n* **They may not want to use their battery life.** Using a smartphone or GPS device can drain your battery life quickly, especially if you are using them for navigation. If you are trying to conserve battery life, you may prefer to use a paper map or ask for directions.\n* **They may prefer the tactile experience of using a paper map.** Some people simply prefer the tactile experience of using a paper map. They may find it more satisfying to fold and unfold a map, and they may find it easier to visualize their route when they can see it laid out on paper.\n* **They may prefer to interact with other people.** When you ask for directions, you are interacting with another person. This can be a good way to meet new people and learn about the local culture.\n\nIn addition, there are some specific situations where using a paper map or asking for directions may be the best option:\n\n* **If you are hiking or camping.** If you are hiking or camping in an area with no cell phone reception, you will need to use a paper map or ask for directions from other hikers or campers.\n* **If you are driving in a remote area.** If you are driving in a remote area with no cell phone reception, you will need to use a paper map or ask for directions from other drivers.\n* **If you are traveling to a country with a different language.** If you are traveling to a country with a different language, you may not be able to use a smartphone or GPS device to get directions. In this case, you will need to ask for directions from someone who speaks your language.", "metadata": {}}
-{"answer_id": "hPMvV6zL2C4qTP4mRmhJwG", "model_id": "bard:20230327", "question_id": 34, "text": "Here are some tips on how to determine if a person is genuinely interested in a conversation or simply being polite:\n\n* **Pay attention to their body language.** If they are leaning in towards you, making eye contact, and nodding their head, they are likely interested in what you have to say. If they are fidgeting, checking their phone, or looking around the room, they may not be as engaged.\n* **Listen to their questions.** If they are asking questions about you and your interests, they are likely interested in getting to know you. If they are only asking questions about the topic of the conversation, they may not be as engaged.\n* **Notice how they respond to your answers.** If they are attentively listening to your answers and asking follow-up questions, they are likely interested in what you have to say. If they are not responding to your answers or changing the topic of the conversation, they may not be as interested.\n* **Trust your gut.** If you feel like someone is not being genuine, they probably are not. If you feel like someone is genuinely interested in you and the conversation, they probably are.\n\nIt is important to remember that everyone is different and there is no one-size-fits-all answer to this question. However, by paying attention to these tips, you can get a good sense of whether someone is genuinely interested in a conversation or simply being polite.", "metadata": {}}
-{"answer_id": "npWNeKceGyqCYaRpY4w54g", "model_id": "bard:20230327", "question_id": 35, "text": "There are many reasons why someone might prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher. Here are a few:\n\n* **Supporting the local economy.** When you shop at a small business, more of your money stays in your community. This helps to support local jobs, schools, and infrastructure.\n* **Getting unique products.** Small businesses are often more likely to carry unique or one-of-a-kind products that you won't find at a chain store.\n* **Getting better service.** Small businesses are often more customer-oriented than chain stores. Owners and employees are more likely to take the time to get to know you and your needs.\n* **Feeling good about supporting a local family or community.** When you shop at a small business, you're supporting the dreams of a local family or community. This can give you a sense of pride and satisfaction.\n\nOf course, there are also some downsides to shopping at small businesses. The prices may be higher, and the selection may be smaller. But for many people, the benefits outweigh the costs.", "metadata": {}}
-{"answer_id": "WVuaK9m8Sedcws27tNu7Ev", "model_id": "bard:20230327", "question_id": 36, "text": "Here are some tips on how to assess the credibility of a source of information without relying solely on the reputation of the author or publisher:\n\n* **Check the source's credentials.** Does the author have any relevant experience or education? Is the publisher a reputable organization?\n* **Look for bias.** Does the source seem to be trying to push an agenda? Is it more likely to present one side of an issue than the other?\n* **Check the sources of the information.** Does the source cite any other sources? Are those sources credible?\n* **Look for errors.** Is there any evidence that the source has made any mistakes?\n* **Read the source critically.** Ask yourself if the information makes sense. Does it seem consistent with what you know about the topic?\n\nIt is important to remember that even the most reputable sources can make mistakes. It is always a good idea to do your own research and to corroborate information from multiple sources.", "metadata": {}}
-{"answer_id": "HLtTf83Y5QRP4TxX6nw5TC", "model_id": "bard:20230327", "question_id": 37, "text": "There are a few reasons why people might enjoy being scared. One reason is that it can release endorphins, which are hormones that have mood-boosting effects. Endorphins can make us feel happy and euphoric, and they can also help to reduce stress and pain.\n\nAnother reason people might enjoy being scared is that it can provide a sense of excitement and adrenaline. When we are scared, our bodies release adrenaline, which can make us feel more alert and energized. This can be a pleasurable experience for some people.\n\nFinally, some people might enjoy being scared because it allows them to experience a sense of control over their fear. When we are scared, we often feel like we are at the mercy of our emotions. However, if we are able to face our fears and overcome them, it can give us a sense of empowerment and accomplishment.\n\nOn the other hand, some people avoid scary experiences because they find them to be unpleasant. They might be afraid of feeling scared, or they might worry that they will not be able to handle the fear. Others might simply not find the experience of being scared to be enjoyable.\n\nUltimately, whether or not someone enjoys being scared is a personal preference. There is no right or wrong answer, and people should do what makes them happy.", "metadata": {}}
-{"answer_id": "Fmdtexq6QQNuoqZkZfDURY", "model_id": "bard:20230327", "question_id": 38, "text": "Observing the behavior of other people in a social situation can provide clues about cultural norms and expectations by:\n\n* **Identifying patterns of behavior.** If you see that people are all doing something in a certain way, it can be a sign that there is an unspoken rule about how to behave in that situation. For example, if you see that everyone in a restaurant is using their napkins to wipe their mouths, it can be a sign that there is an expectation that people will use their napkins to eat.\n* **Paying attention to how people react to violations of cultural norms.** If you see someone violate a cultural norm, take note of how other people react. Do they seem surprised? Angry? Upset? This can give you clues about how important the norm is to people in that culture. For example, if you see someone in a restaurant talking loudly on their phone, and other people around them seem annoyed, it can be a sign that people in that culture value quietness in restaurants.\n* **Talking to people about their culture.** If you are curious about cultural norms and expectations, you can always talk to people who are from that culture. They can tell you about the rules that they have learned and how they are expected to behave in different situations. For example, if you are visiting a country that has a different language than your own, you could talk to a native speaker of that language about how to greet people in their culture.\n\nObserving the behavior of other people in a social situation can be a helpful way to learn about cultural norms and expectations. By paying attention to patterns of behavior, how people react to violations of cultural norms, and talking to people about their culture, you can gain a deeper understanding of how people in different cultures behave.", "metadata": {}}
-{"answer_id": "WxnC69jTMkyJvcqvMCgCwY", "model_id": "bard:20230327", "question_id": 39, "text": "There is no easy answer to this question, as it is a complex and multifaceted issue. There are many factors to consider, such as the potential benefits of space exploration, the potential costs, and the ethical implications of exploring space.\n\nSome of the potential benefits of space exploration include:\n\n* Expanding our knowledge of the universe and our place in it\n* Developing new technologies that can be used to solve Earth's problems\n* Creating new industries and jobs\n* Inspiring people and motivating them to pursue their dreams\n\nSome of the potential costs of space exploration include:\n\n* The financial costs of developing and launching spacecraft\n* The environmental costs of space debris\n* The risks to human life\n* The potential for conflict between nations as they compete for resources in space\n\nThe ethical implications of exploring space are also complex. Some people believe that it is our moral duty to explore space, as it is the next frontier for humanity. Others believe that we should focus on solving Earth's problems first, before we embark on a journey into the unknown.\n\nUltimately, the decision of whether or not to explore space is a personal one. There is no right or wrong answer, and each person must weigh the potential benefits and costs before making a decision.", "metadata": {}}
-{"answer_id": "npZdTFPRqZfoqzt5YurYEL", "model_id": "bard:20230327", "question_id": 40, "text": "In a world where automation is becoming increasingly prevalent, it is important to prioritize both job creation and technological progress. Technological progress can lead to job creation by creating new industries and demand for new products and services. However, it can also lead to job destruction by automating tasks that were previously done by humans. It is important to find ways to mitigate the negative effects of automation on employment while also harnessing its potential to create new jobs.\n\nOne way to mitigate the negative effects of automation on employment is to invest in education and training so that workers can develop the skills they need to succeed in the new economy. Another way is to provide financial assistance to workers who are displaced by automation.\n\nIt is also important to create new industries and demand for new products and services that can be created by technological progress. This can be done by encouraging entrepreneurship and innovation. It is also important to invest in research and development so that new technologies can be developed that create new jobs.\n\nBy prioritizing both job creation and technological progress, we can ensure that automation has a positive impact on the economy and on society as a whole.", "metadata": {}}
-{"answer_id": "iy9aa5sqFeNA2uPQZLpxEz", "model_id": "bard:20230327", "question_id": 41, "text": "The average human blinks 15-20 times per minute, 1,200 times per hour, 28,800 times per day, 10,512,000 times a year, and 518,017,359 times in their entire lifetime.\n\nWe blink to keep our eyes moist and clean, and to protect them from foreign objects. Blinking also helps us to focus our vision.\n\nThe number of times we blink per minute varies depending on a number of factors, including our age, our activity level, and the environment we are in. For example, we tend to blink more often when we are tired or when we are exposed to bright lights.\n\nThe number of times we blink per day also varies depending on our age. For example, infants blink more often than adults, and older adults blink less often than younger adults.\n\nThe number of times we blink per year is a constant number, regardless of our age or activity level.\n\nThe number of times we blink per lifetime is a large number, but it is not an infinite number. If we lived for 100 years, we would blink an average of 518,017,359 times.", "metadata": {}}
-{"answer_id": "XAALo4GKWE3QNb7wbkWVNk", "model_id": "bard:20230327", "question_id": 42, "text": "There are approximately 10^18 atoms in a grain of salt. This number can be calculated by using the following steps:\n\n1. The atomic mass of sodium chloride (NaCl) is 58.44 g/mol.\n2. One mole contains Avogadro's number of atoms, which is 6.022 x 10^23 atoms.\n3. Therefore, the mass of one grain of salt is 58.44 x 10^-3 g.\n4. Dividing the mass of one grain of salt by the atomic mass of sodium chloride, we get the number of moles of sodium chloride in one grain of salt.\n5. Dividing the number of moles of sodium chloride by Avogadro's number, we get the number of atoms in one grain of salt.\n\nThis number is very large, and it can be difficult to comprehend. However, it is important to remember that atoms are very small, and that a grain of salt is also very small. The average grain of salt is about 0.05 mm in diameter, which is about the size of a grain of sand. Therefore, the number of atoms in a grain of salt is still relatively small compared to the size of the grain.", "metadata": {}}
-{"answer_id": "XRWjLnJNyGNMfktTvWpbRV", "model_id": "bard:20230327", "question_id": 43, "text": "There are about 8.6 million lightning strikes on Earth each day. This is based on the average number of lightning strikes worldwide every second, which is 100. \n\nLightning strikes occur when there is an imbalance between the Earth's surface and the atmosphere. The Earth's surface is negatively charged, while the atmosphere is positively charged. When these two charges come into contact, they create an electrical discharge, which is lightning.\n\nLightning strikes are most common in areas with a lot of thunderstorms, such as the tropics. They can also occur in other areas, but they are less common.\n\nLightning strikes can be dangerous, and they can cause a lot of damage. They can start fires, injure people, and even kill people. It is important to be aware of the dangers of lightning strikes and to take precautions to stay safe.", "metadata": {}}
-{"answer_id": "CHeyn9eR3u5eFq99UJ3pbB", "model_id": "bard:20230327", "question_id": 44, "text": "In the movie \"Up,\" the house is a typical American house that weighs about 10,000-25,000 kg. Helium balloons create about 10 g of lift each, so you would need at least 1,000,000 balloons to lift the house. In the film, we see an enormous collection of balloons above the house, perhaps 10 times the volume of the house. This would be about 10 million balloons, which is enough to lift the house.\n\nHowever, there are a few things to consider. First, the balloons would need to be very strong to withstand the weight of the house. Second, the house would need to be very light to be lifted by the balloons. Third, the balloons would need to be very buoyant to lift the house against the force of gravity.\n\nEven if all of these factors were met, it is still unlikely that a house could be lifted by balloons in the real world. The cost of the balloons, the time it would take to fill them, and the risk of them bursting would make it an impractical solution.", "metadata": {}}
-{"answer_id": "kfoNRLCHFucfWcxFBPoJgP", "model_id": "bard:20230327", "question_id": 45, "text": "According to Domo, 15,220,700 texts are sent every minute of every day worldwide, not including app-to-app messaging. This means that in a single minute, more than 15 million text messages are sent around the globe.\n\nThere are a few reasons why this number is so high. First, texting is a convenient and efficient way to communicate. It allows people to send quick messages to multiple people at once, and it doesn't require the sender or receiver to be available at the same time. Second, texting is a relatively inexpensive way to communicate. Most carriers offer unlimited texting plans, so people can send as many messages as they want without having to worry about running out of minutes or data. Finally, texting is a popular way to communicate with friends and family. Many people prefer to text rather than call or email, because it is a more casual and informal way to communicate.\n\nThe number of text messages sent globally is expected to continue to grow in the coming years. This is due to a number of factors, including the increasing popularity of smartphones, the growing availability of high-speed internet, and the continued development of new text messaging features.", "metadata": {}}
-{"answer_id": "A4sDEGXpWaNVA39uRsDNjB", "model_id": "bard:20230327", "question_id": 46, "text": "The number of words spoken daily on Earth is a difficult question to answer, as there are many factors that can affect the number of words spoken. However, there are some estimates that can be made.\n\nOne estimate is that the average person speaks about 7,000 words per day. This is based on a study that found that the average person speaks about 182,500 words per week. If we assume that the average person speaks for 5 days per week, then this would mean that they speak about 7,000 words per day.\n\nHowever, this is just an estimate, and the actual number of words spoken per day can vary greatly depending on the individual. Some people may speak more than 10,000 words per day, while others may speak less than 5,000 words per day.\n\nThere are a number of factors that can affect the number of words spoken per day. These include the person's occupation, their culture, and their personality. For example, people who work in sales or customer service may speak more words per day than people who work in jobs that are more solitary. People who live in cultures that are more talkative may also speak more words per day than people who live in cultures that are more reserved. And people who are more extroverted may also speak more words per day than people who are more introverted.\n\nSo, while the exact number of words spoken daily on Earth is unknown, it is estimated that the average person speaks about 7,000 words per day. This number can vary greatly depending on the individual, but it is a good starting point for estimating the number of words spoken on our planet.", "metadata": {}}
-{"answer_id": "hUAc6BCs5NuY7tS62PPXbT", "model_id": "bard:20230327", "question_id": 47, "text": "There are an estimated 1 septillion snowflakes that fall each year. A septillion is a 1 with 24 zeros after it. To put that into perspective, there are only about 8 billion people on Earth. So, there are more snowflakes that fall in a single year than there are people on the entire planet!\n\nThe number of snowflakes that fall each year depends on a number of factors, including the amount of snowfall, the temperature, and the wind. In general, areas that receive a lot of snowfall will have more snowflakes falling each year. And, of course, areas that are colder will also have more snowflakes falling.\n\nSnowflakes are formed when water droplets in the atmosphere freeze. The water droplets need something to cling to in order to freeze, such as a dust particle or a piece of ice. Once the water droplets freeze, they start to grow and form snowflakes.\n\nThe shape of a snowflake depends on the temperature and the amount of water vapor in the atmosphere. If the temperature is very cold, the snowflakes will be very small and have a simple shape. If the temperature is warmer, the snowflakes will be larger and more complex.\n\nSnowflakes are a beautiful and fascinating part of winter. They are a reminder of the beauty of nature and the power of the cold.", "metadata": {}}
-{"answer_id": "TMtjmb5tDizQ9RETRxpt2s", "model_id": "bard:20230327", "question_id": 48, "text": "The number of pages in all the books ever written is an impossible number to know with certainty. There are many factors that would affect the total, including the number of books that have been written, the average length of a book, and the number of books that have been lost or destroyed.\n\nHowever, we can make some educated guesses based on available data. For example, Google Books has indexed over 129 million books, and the average length of a book is 300 pages. This would mean that the total number of pages in all the books ever written is over 38 trillion.\n\nHowever, this number is likely an underestimate. First, it does not take into account self-published books, which are often longer than traditionally published books. Second, it does not take into account books that have been lost or destroyed.\n\nA more accurate estimate of the total number of pages in all the books ever written would be somewhere in the range of 100 trillion to 1 quadrillion pages. This is a truly staggering number, and it is difficult to imagine how many books it would take to fill such a library.\n\nBut even this number is just an estimate. The true number of pages in all the books ever written is likely much higher, and we will never know for sure.", "metadata": {}}
-{"answer_id": "AwQMPhhaJ32ByA3VjKF5Ph", "model_id": "bard:20230327", "question_id": 49, "text": "The Earth has orbited the Sun 4.543 billion times since the beginning of life. This number is based on the age of the Earth, which is 4.543 billion years old, and the time it takes for the Earth to orbit the Sun, which is 365.2422 days.\n\nThe first life on Earth is thought to have arisen about 3.8 billion years ago. This means that the Earth has orbited the Sun about 1 billion times since the first life arose.\n\nThe Earth's orbit around the Sun is elliptical, which means that it is not a perfect circle. The Earth's orbit is closest to the Sun in January and farthest from the Sun in July. This difference in distance causes the Earth's seasons.\n\nThe Earth's orbit is also tilted relative to the Sun's equator. This tilt causes the Earth's seasons to change.\n\nThe Earth's orbit around the Sun is a complex process that has been shaped by the forces of gravity, inertia, and tidal forces. The Earth's orbit is constantly changing, but it has been remarkably stable for billions of years. This stability is essential for life on Earth.", "metadata": {}}
-{"answer_id": "UCqxXfMAnqJKbApsjrPFcq", "model_id": "bard:20230327", "question_id": 50, "text": "There is no definitive answer to the question of how many songs have been recorded throughout history. However, there are a few ways to estimate this number.\n\nOne way is to look at the number of songs that have been registered with the United States Copyright Office. As of 2023, the US Copyright Office has registered over 97 million songs. However, this is likely an underestimate, as many songs are not registered with the Copyright Office.\n\nAnother way to estimate the number of songs that have been recorded is to look at the number of songs that are available for streaming on services like Spotify and Apple Music. As of 2023, Spotify has over 82 million songs in its catalog, and Apple Music has over 60 million songs. This suggests that there are at least 142 million songs that have been recorded and released to the public.\n\nHowever, this is still likely an underestimate, as many songs are not available for streaming on these services. For example, many songs are only available on physical media, such as vinyl records or CDs. Additionally, many songs are not released to the public, and are only available to be recorded by professional musicians.\n\nUltimately, the number of songs that have been recorded throughout history is likely in the billions. This is a staggering number, and it is a testament to the creativity and ingenuity of human beings.", "metadata": {}}
-{"answer_id": "YMRg5Xi9BBvvqqny2tqJZ3", "model_id": "bard:20230327", "question_id": 51, "text": "If the Internet had been invented during the Renaissance period, it would have had a profound impact on the world. The Renaissance was a time of great intellectual and cultural growth, and the Internet would have accelerated this growth. It would have allowed people to share ideas more easily, and to collaborate on projects from all over the world. It would have also helped to spread education and knowledge, and to promote understanding between different cultures.\n\nThe Internet would have also had a significant impact on the arts. It would have allowed artists to share their work more easily, and to connect with other artists from all over the world. It would have also helped to promote the arts, and to make them more accessible to the public.\n\nOverall, the Internet would have had a positive impact on the world if it had been invented during the Renaissance period. It would have accelerated the growth of knowledge and understanding, and it would have promoted the arts and culture.", "metadata": {}}
-{"answer_id": "fhr7iMPb9SVJ663mXKBuct", "model_id": "bard:20230327", "question_id": 52, "text": "If the Aztecs had successfully repelled the Spanish conquistadors, the course of history in the Americas would have been very different. The Aztecs were a powerful empire with a strong military, and their defeat by the Spanish was a major blow to the indigenous peoples of the Americas. If the Aztecs had won, they might have been able to resist the further expansion of European colonialism, and the Americas might have developed in a more independent and indigenous-centered way.\n\nHowever, it is important to note that the Aztecs were not immune to disease, and they were also facing internal political and economic problems. Even if they had defeated the Spanish, it is possible that their empire would have collapsed in the years to come.\n\nUltimately, the question of what would have happened if the Aztecs had successfully repelled the Spanish is a matter of speculation. However, it is clear that their defeat had a profound impact on the course of history in the Americas.", "metadata": {}}
-{"answer_id": "4rrmyZw9zhyJGqdrcazFvt", "model_id": "bard:20230327", "question_id": 53, "text": "The Black Death was a devastating event that had a profound impact on the course of European history. It is impossible to say for certain what would have happened if the Black Death had not occurred, but it is likely that Europe would have been a very different place.\n\nOne of the most significant effects of the Black Death was the decline of the population. Millions of people died from the plague, and many more were left weakened or orphaned. This led to a labor shortage, which had a major impact on the economy.\n\nThe Black Death also had a profound impact on society. The deaths of so many people led to a sense of despair and hopelessness. Many people turned to religion for comfort, and there was a rise in superstition and fear.\n\nThe Black Death also led to changes in the way people lived. People began to avoid crowded areas, and they took steps to prevent the spread of the plague, such as burning the bodies of the dead and avoiding contact with people who were sick.\n\nIt is impossible to say for certain what would have happened if the Black Death had not occurred, but it is clear that it had a profound impact on the course of European history.", "metadata": {}}
-{"answer_id": "Za3mY9xwxpZdPmGW48wtzu", "model_id": "bard:20230327", "question_id": 54, "text": "If Isaac Newton had focused on biology instead of physics, he might have made some important discoveries in the field of medicine. He could have studied the mechanics of the human body and how it moves, or he could have investigated the causes of disease. He might have even developed new treatments or cures for illnesses.\n\nHowever, it is also possible that Newton would not have made any significant discoveries in biology. He might have been too focused on physics to devote himself to another field. Additionally, biology is a much more complex field than physics, and Newton might not have had the same level of success in it.\n\nUltimately, it is impossible to say for sure what would have happened if Newton had focused on biology instead of physics. However, it is clear that he was a brilliant scientist with a lot to offer both fields.", "metadata": {}}
-{"answer_id": "cbAaJS9ULjR4XYSHATujSG", "model_id": "bard:20230327", "question_id": 55, "text": "It is impossible to say for certain what the world would be like without the Beatles, but it is clear that they had a profound impact on popular culture. The band's music and style were hugely influential, and their success helped to popularize rock and roll music around the world.\n\nIf the Beatles had never formed, it is possible that another band would have taken their place and become just as popular. However, it is also possible that the world would have been a very different place. The Beatles' music was often seen as a symbol of hope and optimism, and their message of peace and love resonated with many people. Without them, the world might be a darker and more cynical place.\n\nIt is also worth noting that the Beatles were not just a band. They were also cultural icons who helped to shape the way we view the world. Their fashion, their haircuts, and their language all became part of the fabric of popular culture. Without them, the world might be a more boring place.\n\nUltimately, it is impossible to say for certain what the world would be like without the Beatles. However, it is clear that they had a profound impact on popular culture, and their absence would be felt by many people.", "metadata": {}}
-{"answer_id": "ZEgb9fvopGo7HF5wPeoeHs", "model_id": "bard:20230327", "question_id": 56, "text": "If Alan Turing had not cracked the Enigma code during World War II, the Allies would have been at a significant disadvantage. The Enigma code was used by the German military to communicate securely, and it was very difficult to break. Turing's work was essential to the Allied victory, and it is likely that the war would have lasted much longer and resulted in more casualties if he had not been able to break the code.\n\nTuring's work at Bletchley Park was also important because it helped the Allies to understand the German military's plans and intentions. This information helped the Allies to make better decisions about where to deploy their troops and how to use their resources.\n\nOverall, Turing's work on the Enigma code was essential to the Allied victory in World War II. If he had not been able to break the code, the war would have lasted much longer and resulted in more casualties.", "metadata": {}}
-{"answer_id": "igMXoEiszFM65ZS2KUTvtm", "model_id": "bard:20230327", "question_id": 57, "text": "If the Suez Canal had never been constructed, it would have had a significant impact on the global economy. The canal is a major shipping route, and its absence would have forced ships to take a much longer and more expensive route around Africa. This would have made goods more expensive and slower to arrive.\n\nThe Suez Canal is also a major source of income for Egypt. The government collects tolls from ships that use the canal, and this money is used to fund important projects such as schools, hospitals, and infrastructure. If the canal had never been built, Egypt would have lost a significant source of income.\n\nThe Suez Canal is also a major symbol of Egyptian pride. The canal was built by Egyptian workers, and it is a source of national pride. If the canal had never been built, it would have been a major blow to Egyptian morale.\n\nOverall, the absence of the Suez Canal would have had a significant impact on the global economy and on Egypt. The canal is a major shipping route, a source of income for Egypt, and a symbol of Egyptian pride.", "metadata": {}}
-{"answer_id": "Up4h8RpgVVafBtUj4tiGPZ", "model_id": "bard:20230327", "question_id": 58, "text": "If the Maya civilization had never mysteriously collapsed, it is likely that they would have continued to thrive and develop. They were a highly advanced civilization with a sophisticated understanding of mathematics, astronomy, and engineering. They also had a rich culture and a strong sense of community.\n\nIf the Maya had not collapsed, they would have likely continued to expand their territory and influence. They might have even become a global power. Their advanced technology and culture would have had a profound impact on the world.\n\nHowever, it is also possible that the Maya would have eventually collapsed even if they had not experienced the sudden and mysterious decline that they did. They were a very complex civilization with a lot of interconnected parts. If one part of the system collapsed, it could have had a cascading effect on the rest of the civilization.\n\nUltimately, it is impossible to say for sure what would have happened if the Maya civilization had never collapsed. However, it is clear that they were a very advanced and influential civilization. If they had not collapsed, they would have had a profound impact on the world.", "metadata": {}}
-{"answer_id": "CMg3ypKyykGUzQsDmX7i35", "model_id": "bard:20230327", "question_id": 59, "text": "If Christopher Columbus had not discovered the Americas, it is likely that other European explorers would have eventually found the continent. However, the timing of Columbus's voyage was important, as it coincided with the beginning of the Age of Exploration. Columbus's discovery helped to spur further exploration and colonization of the Americas, which had a profound impact on the history of the world.\n\nIf Columbus had not discovered the Americas, it is possible that the continent would have remained largely unknown to Europeans for centuries. This would have delayed the development of trade and communication between Europe and the Americas, and it would have also delayed the spread of European diseases to the Americas.\n\nIt is also possible that the Americas would have been settled by other peoples, such as the Chinese or the Japanese. These civilizations had the technology and the resources to reach the Americas, and they were also interested in exploring new lands.\n\nUltimately, it is impossible to say for sure what would have happened if Christopher Columbus had not discovered the Americas. However, it is clear that his discovery had a profound impact on the history of the world.", "metadata": {}}
-{"answer_id": "WhLiJXznzRBkzxDdnc5ndX", "model_id": "bard:20230327", "question_id": 60, "text": "If Vincent van Gogh had been a successful artist during his lifetime, he would have likely sold more paintings and had a greater impact on the art world. He might have been able to support himself financially and pursue his art career without having to rely on his brother Theo for financial support. He might have also been able to travel more and experience more of the world, which would have influenced his art. \r\n\r\nHowever, it is also possible that Van Gogh's tragic story is part of the appeal of his art. His paintings are often seen as a window into his tortured soul and his passionate love of nature. If he had been successful during his lifetime, he might not have been able to create such powerful and moving works of art. \r\n\r\nUltimately, it is impossible to say for sure what would have happened if Vincent van Gogh had been a successful artist during his lifetime. However, it is clear that his tragic story has not diminished his impact on the art world. His paintings continue to be admired and loved by people all over the world.", "metadata": {}}
-{"answer_id": "Vzmhr5RMndkR866p4pUef6", "model_id": "bard:20230327", "question_id": 61, "text": "It looks like you want my help with coding, but I'm not trained to do that, yet. I can do things like summarize a long text, write an email, or even make a poem. Is there something else you would like to try?", "metadata": {}}
-{"answer_id": "jPrfhv9oaZGrkXxDUw5Vbt", "model_id": "bard:20230327", "question_id": 62, "text": "Sure, here is a Python function to find the longest common subsequence of two input strings using dynamic programming:\n\n```python\ndef lcs(s1, s2):\n  m = len(s1)\n  n = len(s2)\n  dp = [[0] * (n + 1) for i in range(m + 1)]\n\n  for i in range(1, m + 1):\n    for j in range(1, n + 1):\n      if s1[i - 1] == s2[j - 1]:\n        dp[i][j] = dp[i - 1][j - 1] + 1\n      else:\n        dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])\n\n  return dp[m][n]\n```\n\nThis function uses dynamic programming to solve the problem. The idea is to first fill a table with the lengths of the longest common subsequences of all possible subsequences of the two strings. Then, we can find the longest common subsequence of the two strings by looking at the last element of the table.\n\nThe time complexity of this algorithm is O(nm), where m and n are the lengths of the two strings. The space complexity is O(mn).", "metadata": {}}
-{"answer_id": "KdZNrf6udTfCxGGcQxRbXk", "model_id": "bard:20230327", "question_id": 63, "text": "Sure, here is a regular expression in Python to validate an email address:\n\n```python\nimport re\n\nregex = '^[a-zA-Z0-9]+[\\._]?[ a-zA-Z0-9]+[@]\\w+[. ]\\w{2,3}$'\n\ndef check(email):\n    if re.search(regex, email):\n        return True\n    else:\n        return False\n```\n\nThis regular expression matches the following format:\n\n* A username, which can contain letters, numbers, and underscores.\n* The @ symbol\n* A domain name, which can contain letters, numbers, and dots.\n* The top-level domain, which must be 2-3 characters long.\n\nThis regular expression does not validate the following:\n\n* The existence of the email server.\n* The validity of the domain name.\n* The availability of the username.\n\nIf you want to validate all of these things, you will need to use a different method.", "metadata": {}}
-{"answer_id": "AFzqAfbYVL25DBqFA2cv4G", "model_id": "bard:20230327", "question_id": 64, "text": "Here is a Python program to find the nth Fibonacci number using dynamic programming:\n\n```python\ndef fib(n):\n\n    # Create a table of size n to store the Fibonacci numbers\n    dp = [0] * n\n\n    # Fill the first two elements of the table\n    dp[0] = 0\n    dp[1] = 1\n\n    # Fill the remaining elements of the table\n    for i in range(2, n):\n        dp[i] = dp[i-1] + dp[i-2]\n\n    # Return the nth Fibonacci number\n    return dp[n-1]\n\n# Driver code\nn = int(input(\"Enter the nth Fibonacci number: \"))\nprint(\"The nth Fibonacci number is\", fib(n))\n```\n\nThis program uses dynamic programming to solve the Fibonacci sequence problem. Dynamic programming is a technique that can be used to solve problems that have overlapping subproblems. In this case, the subproblems are the Fibonacci numbers. The program stores the results of previously solved subproblems in a table, and then uses that table to calculate the nth Fibonacci number.", "metadata": {}}
-{"answer_id": "Lt56JzWfXBxbvxrLf4fn3t", "model_id": "bard:20230327", "question_id": 65, "text": "Sure, here is a binary search algorithm to find a specific element in a sorted array:\n\n```python\ndef binary_search(array, low, high, x):\n    if high >= low:\n        mid = (high + low) // 2\n        if array[mid] == x:\n            return mid\n        elif array[mid] < x:\n            low = mid + 1\n        else:\n            high = mid - 1\n    else:\n        return -1\n```\n\nThis algorithm works by repeatedly dividing the search interval in half until the target value is found. The algorithm starts by comparing the target value to the middle element of the array. If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found. If the search ends with the remaining half being empty, the target is not in the array.\n\nThe time complexity of binary search is O(log n), where n is the number of elements in the array. This means that the algorithm takes logarithmic time to find the target value, even if the array is very large.", "metadata": {}}
-{"answer_id": "bmaShWUyEqPE6kbHD6FjLk", "model_id": "bard:20230327", "question_id": 66, "text": "Here is an implementation of a queue data structure using two stacks in Python:\n\n```python\nclass Queue:\n\n    def __init__(self):\n        self.in_stack = []\n        self.out_stack = []\n\n    def enqueue(self, element):\n        self.in_stack.append(element)\n\n    def dequeue(self):\n        if len(self.out_stack) == 0:\n            while len(self.in_stack) > 0:\n                self.out_stack.append(self.in_stack.pop())\n        return self.out_stack.pop()\n\n    def __len__(self):\n        return len(self.in_stack) + len(self.out_stack)\n\n    def __iter__(self):\n        return iter(self.in_stack)\n\n    def __contains__(self, element):\n        return element in self.in_stack\n```\n\nThis implementation uses two stacks, an `in_stack` and an `out_stack`. The `in_stack` is used to store elements that are waiting to be processed, and the `out_stack` is used to store elements that have been processed. When an element is enqueued, it is added to the `in_stack`. When an element is dequeued, it is removed from the `out_stack`. If the `out_stack` is empty, the elements in the `in_stack` are popped into the `out_stack` and the `in_stack` is emptied.\n\nThis implementation of a queue data structure using two stacks is efficient, as it uses only two data structures. It is also easy to implement and maintain.", "metadata": {}}
-{"answer_id": "hiSVom5YqSrKZKor8CjHdU", "model_id": "bard:20230327", "question_id": 67, "text": "Sure, here is a program to find the common elements in two arrays without using any extra data structures:\n\n```python\ndef common_elements(array1, array2):\n    \"\"\"Finds the common elements in two arrays.\n\n    Args:\n        array1 (list): The first array.\n        array2 (list): The second array.\n\n    Returns:\n        set: The set of common elements.\n    \"\"\"\n    # Initialize the set of common elements.\n    common_elements = set()\n\n    # Iterate through the first array.\n    for element in array1:\n        # Iterate through the second array.\n        for element2 in array2:\n            # If the elements are equal, add them to the set of common elements.\n            if element == element2:\n                common_elements.add(element)\n\n    # Return the set of common elements.\n    return common_elements\n```\n\nThis program uses a simple algorithm to find the common elements in two arrays. The algorithm first initializes a set of common elements. Then, it iterates through the first array and compares each element to each element in the second array. If an element is found in both arrays, it is added to the set of common elements. The algorithm then returns the set of common elements.\n\nThis algorithm is efficient, as it does not require any extra data structures. However, it can be slow for large arrays, as it has to compare all of the elements in the two arrays.", "metadata": {}}
-{"answer_id": "JRHeURg4TrVAgWNp5yqjFx", "model_id": "bard:20230327", "question_id": 68, "text": "To find the value of f(2), we simply substitute x=2 into the equation:\n\nf(2) = 5(2)^3 - 2(2) + 3 = 40 - 4 + 3 = 39\n\nTherefore, the value of f(2) is 39.", "metadata": {}}
-{"answer_id": "hZwFKeRYBLKihBEWSEGMYJ", "model_id": "bard:20230327", "question_id": 69, "text": "Distribute the 5 on the right-hand side of the equation:\n\n3x + 10 = 5x - 10\n\nCombine like terms:\n\n-2x = -10\n\nDivide both sides by -2:\n\nx = 5\n\nTherefore, the solution to the equation is $x=5$.", "metadata": {}}
-{"answer_id": "iG4SJq9xCKCDDtiqU3szJZ", "model_id": "bard:20230327", "question_id": 70, "text": "The length of a line segment is the distance between its endpoints. The distance formula is:\n\n$$d = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$\n\nwhere $(x_1, y_1)$ and $(x_2, y_2)$ are the coordinates of the endpoints. In this case, $(x_1, y_1) = (2, -2)$ and $(x_2, y_2) = (10, 4)$. Substituting these values into the distance formula, we get:\n\n$$d = \\sqrt{(10 - 2)^2 + (4 - (-2))^2} = \\sqrt{8^2 + 6^2} = \\sqrt{100} = 10$$\n\nTherefore, the length of the line segment is 10 units.", "metadata": {}}
-{"answer_id": "CjhV8cJ6UEp7Y4f3eWB6E5", "model_id": "bard:20230327", "question_id": 71, "text": "Sure, here is a sample email that you can use as a template for your own joint venture proposal:\n\nDear [Potential Partner Name],\n\nI am writing to you today to propose a joint venture between our two companies. Our two companies have been working together for many years, and we have a proven track record of success. I believe that a joint venture between our two companies would be a mutually beneficial arrangement.\n\nThe purpose of this joint venture would be to develop a new product or service that would be marketed to a specific market. We would each contribute our own unique skills and resources to the project, and we would share the profits from the venture equally.\n\nI believe that this joint venture would be a success for several reasons. First, our two companies have a strong track record of success. We have both been in business for many years, and we have both developed a loyal customer base. Second, our two companies have complementary skills and resources. We each have our own strengths and weaknesses, and a joint venture would allow us to combine our strengths to create something even greater. Finally, I believe that the market for this new product or service is growing rapidly. There is a lot of demand for this type of product or service, and I believe that we can capture a significant share of the market.\n\nI would like to meet with you to discuss this proposal in more detail. I am available to meet at your convenience. Please let me know if you are interested in discussing this further.\n\nThank you for your time and consideration.\n\nSincerely,\n[Your Name]", "metadata": {}}
-{"answer_id": "5waLH9pZsr8jVTpy67QH58", "model_id": "bard:20230327", "question_id": 72, "text": "Dear [Boss's name],\n\nI am writing to inform you of my decision to resign from my position as [Your job title] at [Company name]. My last day of employment will be [Date].\n\nI have accepted a position at another company that will allow me to [Reason for leaving]. I have enjoyed my time at [Company name] and I am grateful for the opportunities I have been given. I have learned a great deal and I am confident that the skills I have developed will be valuable in my new role.\n\nI would like to thank you for your support and guidance during my time at [Company name]. I wish you and the company all the best in the future.\n\nSincerely,\n[Your name]", "metadata": {}}
-{"answer_id": "ZV4sUNujQGS2w7Wg4jbXbf", "model_id": "bard:20230327", "question_id": 73, "text": "Dear Admissions Committee,\n\nI am writing to recommend [student name] for admission to your prestigious graduate program in computer science. I have known [student name] for [number] years, first as their undergraduate advisor at [university name] and now as their research advisor at [university name].\n\n[Student name] is an outstanding student with a strong academic record. They have consistently earned top grades in their coursework, and they have a deep understanding of the theoretical and practical foundations of computer science. [Student name] is also an accomplished researcher. They have worked on several projects that have been published in top academic journals, and they have presented their work at several prestigious conferences.\n\nIn addition to their academic and research accomplishments, [student name] is also a well-rounded individual with a strong work ethic and a commitment to service. They are a member of several student organizations, and they volunteer their time at a local homeless shelter.\n\nI have no doubt that [student name] would be an asset to your graduate program. They are a highly intelligent and motivated individual with a strong work ethic and a commitment to excellence. I highly recommend [student name] for admission to your program.\n\nSincerely,\n[Your name]", "metadata": {}}
-{"answer_id": "X94xm9m7tayWsKFNACqAG4", "model_id": "bard:20230327", "question_id": 74, "text": "Subject: Introducing our new software solution: [Product Name]\n\nBody:\n\nHi [Customer Name],\n\nWe're excited to announce the launch of our new software solution, [Product Name]. [Product Name] is a revolutionary new tool that will help you [solve a problem or improve a process].\n\nWith [Product Name], you can [list of benefits].\n\nWe've been working hard to make [Product Name] the best it can be, and we're confident that you'll love it.\n\nTo learn more about [Product Name] and to sign up for a free trial, please visit our website at [website address].\n\nWe can't wait to hear what you think!\n\nThanks,\n[Your Name]", "metadata": {}}
-{"answer_id": "EFL7fU6gnicQY6DRpHvbTK", "model_id": "bard:20230327", "question_id": 75, "text": "Dear [Customer Name],\n\nI am writing to apologize for the delay in your order. I know that you were expecting your order to arrive on [date], and I am truly sorry that it did not.\n\nThere was an issue with our shipping system that caused your order to be delayed. We have since fixed the issue and your order is now on its way to you. You should receive it within the next few days.\n\nI sincerely apologize for the inconvenience this has caused you. We value your business and we are committed to providing you with the best possible service.\n\nIf you have any questions or concerns, please do not hesitate to contact me.\n\nSincerely,\n[Your Name]", "metadata": {}}
-{"answer_id": "XJLFjCr6vgQZjEXbaMjTa6", "model_id": "bard:20230327", "question_id": 76, "text": "Sure, here is a script for a YouTube video exploring the history and cultural significance of jazz:\n\n* * *\n\n**Intro**\n\nJazz is a music genre that originated in the African-American communities of New Orleans, Louisiana, in the late 19th and early 20th centuries. Since the 1920s Jazz Age, it has been recognized as a major form of musical expression in traditional and popular music.\n\nJazz is characterized by swing and blue notes, complex chords, call and response vocals, polyrhythms and improvisation. Jazz has roots in European harmony and African rhythmic rituals.\n\n**History of Jazz**\n\nJazz has its roots in the blues, which was a form of music that originated in the American South in the late 19th century. The blues was a way for African Americans to express their pain and suffering under slavery and segregation.\n\nJazz also has its roots in ragtime, which was a form of music that originated in the United States in the late 19th century. Ragtime was a fast-paced, syncopated style of music that was often played on the piano.\n\nJazz emerged in New Orleans in the early 20th century. The city was a melting pot of cultures, and jazz was a way for people from different backgrounds to come together and express themselves.\n\nJazz quickly spread to other parts of the United States and the world. In the 1920s, jazz became popular in Europe, and in the 1930s, it became popular in Latin America.\n\n**Cultural Significance of Jazz**\n\nJazz has had a profound impact on American culture. It has been used in film, television, and advertising. It has also been used in political protests and social movements.\n\nJazz has also been a major influence on other genres of music, such as rock and roll, hip hop, and soul music.\n\n**Conclusion**\n\nJazz is a living, breathing art form that has been around for over a century. It has evolved over time, but its core elements remain the same: swing, blue notes, complex chords, call and response vocals, polyrhythms, and improvisation.\n\nJazz is a music that is constantly being reinvented by new musicians. It is a music that is constantly being reinterpreted by new listeners. And it is a music that is constantly being celebrated by new generations of fans.\n\n* * *", "metadata": {}}
-{"answer_id": "bz3rS9MRs9QmaLGg4A8a7B", "model_id": "bard:20230327", "question_id": 77, "text": "**Hawaii: A Cultural Experience of a Lifetime**\n\nHawaii is a state of the United States that is known for its beautiful beaches, lush rainforests, and stunning volcanoes. But what many people don't know is that Hawaii is also home to a rich and vibrant culture.\n\nIf you're planning a trip to Hawaii, be sure to add some cultural experiences to your itinerary. Here are a few suggestions:\n\n* Visit a luau: A luau is a traditional Hawaiian feast that includes food, music, and dancing. It's a great way to experience Hawaiian culture and cuisine.\n* Take a hula class: Hula is a traditional Hawaiian dance that tells stories through movement. It's a beautiful and graceful art form that anyone can learn.\n* Visit a museum: Hawaii has several museums that showcase the state's unique history and culture. These museums are a great place to learn about Hawaiian culture and history.\n* Go on a hike: Hawaii is home to some of the most beautiful hiking trails in the world. These hikes are a great way to experience the natural beauty of the islands.\n* Go snorkeling or scuba diving: The waters around Hawaii are home to a variety of marine life. Snorkeling or scuba diving is a great way to see this marine life up close.\n* Visit a coffee farm: Hawaii is home to some of the best coffee in the world. Visiting a coffee farm is a great way to learn about the coffee-making process and sample some of the local coffee.\n\nThese are just a few of the many cultural experiences that you can have in Hawaii. If you're looking for a truly unique and memorable vacation, be sure to add some cultural experiences to your itinerary.\n\nHere are some must-see attractions in Hawaii:\n\n* Haleakala National Park: Haleakala National Park is home to the dormant Haleakala volcano. The park is a great place to see the sunrise and hike through the rainforest.\n* The North Shore: The North Shore of Oahu is home to some of the most beautiful beaches in the world. The North Shore is also a great place to go surfing.\n* The Big Island of Hawaii: The Big Island of Hawaii is the largest of the Hawaiian Islands. The island is home to volcanoes, rainforests, and beaches.\n* Kauai: Kauai is the fourth largest of the Hawaiian Islands. Kauai is known for its beautiful scenery, including the Na Pali Coast.\n* Maui: Maui is the second largest of the Hawaiian Islands. Maui is known for its beaches, volcanoes, and rainforests.\n\nNo matter what you're interested in, Hawaii has something to offer. Whether you're looking for a relaxing beach vacation or an adventure-filled journey, Hawaii is the perfect destination.", "metadata": {}}
-{"answer_id": "kCS2ZqzKTdDpaXwu3TiFXP", "model_id": "bard:20230327", "question_id": 78, "text": "Denis Villeneuve's Dune is a visually stunning and epic sci-fi film that is sure to please fans of the genre. The film is based on the classic novel of the same name by Frank Herbert, and it tells the story of Paul Atreides, a young man who is destined to become the leader of the Fremen, a desert people who have adapted to life on the harsh planet of Arrakis.\n\nThe film's plot is complex and well-developed, and the characters are well-acted and believable. Timoth\u00e9e Chalamet gives a particularly strong performance as Paul, and he is ably supported by a talented cast that includes Rebecca Ferguson, Oscar Isaac, Josh Brolin, Zendaya, and Jason Momoa.\n\nThe film's special effects are simply stunning. The sandworms of Arrakis are particularly impressive, and the film's desert scenes are incredibly realistic. The film's score by Hans Zimmer is also excellent, and it helps to create a truly immersive experience.\n\nOverall, Dune is a must-see for fans of sci-fi and epic films. It is a visually stunning and well-crafted film that is sure to please audiences.\n\nHere are some specific examples of the film's special effects:\n\n* The sandworms of Arrakis are incredibly realistic. They are massive creatures that can swallow entire buildings, and they are a terrifying sight to behold.\n* The desert scenes of Arrakis are incredibly realistic. The film's director, Denis Villeneuve, went to great lengths to capture the harsh beauty of the desert, and the results are stunning.\n* The film's score by Hans Zimmer is excellent. It helps to create a truly immersive experience and helps to set the tone for the film's epic scope.", "metadata": {}}
-{"answer_id": "7WWWS5UhWcVzkhTypqcFU4", "model_id": "bard:20230327", "question_id": 79, "text": "Sure, here is a possible podcast script for an episode discussing the influence of streaming platforms on the music industry:\n\n**Intro:**\n\nHello and welcome to the podcast! In today's episode, we're going to be discussing the influence of streaming platforms on the music industry. Streaming platforms have become increasingly popular in recent years, and they've had a major impact on how people listen to music. In this episode, we'll discuss the pros and cons of streaming platforms, and how they're changing the music industry.\n\n**Body:**\n\nOne of the biggest benefits of streaming platforms is that they give people access to a wide variety of music. In the past, people were limited to the music that was available in their local stores or on the radio. But with streaming platforms, people can listen to anything they want, whenever they want. This has led to a more diverse and eclectic music scene, as people are exposed to new artists and genres that they might not have heard otherwise.\n\nAnother benefit of streaming platforms is that they're very convenient. You can listen to music on your phone, your computer, or your TV. You don't have to worry about carrying around CDs or MP3 players, and you can easily create playlists of your favorite songs. This convenience has made streaming platforms very popular with people of all ages.\n\nHowever, there are also some drawbacks to streaming platforms. One of the biggest problems is that they don't pay artists very much money. When you listen to a song on a streaming platform, the artist only gets a fraction of a penny. This makes it difficult for artists to make a living from their music, and it's one of the reasons why many artists are unhappy with streaming platforms.\n\nAnother problem with streaming platforms is that they can be addictive. It's easy to spend hours scrolling through your favorite songs, and this can lead to problems like sleep deprivation and anxiety. If you find yourself spending too much time on streaming platforms, it's important to take a step back and find other ways to relax and de-stress.\n\n**Conclusion:**\n\nOverall, streaming platforms have had a major impact on the music industry. They've made it easier for people to listen to music, but they've also made it more difficult for artists to make a living. It's important to be aware of the pros and cons of streaming platforms, so that you can make informed decisions about how you use them.\n\n**Outro:**\n\nThanks for listening to this episode of the podcast! We hope you found it informative and helpful. If you have any questions or comments, please feel free to leave them below. We'll see you next time!", "metadata": {}}
-{"answer_id": "QcSFqbgqLWqn96V6BvWT7B", "model_id": "bard:20230327", "question_id": 80, "text": "The Boston Symphony Orchestra gave a stunning performance of Beethoven's Symphony No. 9 in D minor, Op. 125, \"Choral,\" on Saturday night at Symphony Hall. The orchestra was in top form, and the performance was a thrilling and moving experience for the audience.\n\nThe BSO opened the concert with a lively performance of Beethoven's Symphony No. 5 in C minor, Op. 67. The orchestra played with great energy and precision, and the performance was a perfect way to set the stage for the evening's main event.\n\nAfter intermission, the BSO launched into a powerful and moving performance of Beethoven's Symphony No. 9. The orchestra played with great passion and conviction, and the performance was a truly transcendent experience. The audience was on its feet at the end of the performance, cheering and applauding for minutes on end.\n\nThe BSO's performance of Beethoven's Symphony No. 9 was a truly memorable experience. The orchestra played with great skill and artistry, and the performance was a thrilling and moving experience for the audience. If you have the opportunity to see the BSO in concert, I highly recommend it.\n\nThe overall audience experience was excellent. The hall was packed with people, and the atmosphere was electric. The audience was clearly excited to be there, and they responded enthusiastically to the orchestra's performance. The concert was a great example of the power of music to bring people together and create a shared experience.", "metadata": {}}
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-{"answer_id": "BZGowHM7L3RvtWRktKZjLT", "model_id": "gpt-3.5-turbo:20230327", "question_id": 1, "text": "Here are some tips to improve your time management skills:\n\n1. Create a schedule: Make a to-do list for the day, week or month and prioritize tasks by importance and deadline.\n\n2. Set realistic goals: Be realistic about what you can accomplish in a given amount of time. Set goals that can be achieved but are also challenging.\n\n3. Minimize distractions: Minimize distractions such as emails, phone calls, and social media notifications. Turn off notifications or use apps that block these distractions.\n\n4. Take breaks: Taking a break can help you recharge and increase productivity. Take a break every hour or so to stretch, go for a walk, or listen to some music.\n\n5. Learn to say 'no': Learn to say 'no' to tasks and requests that are not a priority or are not within your capabilities.\n\n6. Delegate tasks: If possible, delegate tasks to others who are capable of completing them. This will help to reduce your workload.\n\n7. Prioritize self-care: Make sure to take care of yourself by prioritizing self-care activities such as exercise, meditation or spending time with friends and family.\n\nRemember, time management is a skill that takes time and practice to develop.", "metadata": {}}
-{"answer_id": "GsrPLfiNEcRDYAC5aUoY23", "model_id": "gpt-3.5-turbo:20230327", "question_id": 2, "text": "Here are some effective ways to deal with stress:\n\n1. Exercise regularly: Physical activity can help reduce stress and improve mood.\n\n2. Practice relaxation techniques: Deep breathing, meditation, and yoga are some effective ways to relax and reduce stress levels.\n\n3. Get enough sleep: Lack of sleep can increase stress levels. Try to get at least 7-8 hours of sleep each night.\n\n4. Eat a healthy diet: A well-balanced diet can help reduce stress levels and improve overall health.\n\n5. Connect with others: Spending time with friends and family, joining a support group, or participating in social activities can help reduce stress levels and improve mood.\n\n6. Time management: Prioritizing tasks and creating a schedule can help reduce stress and increase productivity.\n\n7. Seek professional help: If stress is impacting your daily life, consider seeking professional help such as therapy or counseling.", "metadata": {}}
-{"answer_id": "5SGfexmPoJTWoVbPMB87KB", "model_id": "gpt-3.5-turbo:20230327", "question_id": 3, "text": "Python and JavaScript are both popular programming languages, but they differ in a number of ways:\n\n1. Programming paradigm: Python is primarily an object-oriented programming (OOP) language, while JavaScript is a multi-paradigm language that supports both functional and OOP styles.\n\n2. Syntax: Python uses indentation (whitespace) to indicate the structure of the code, while JavaScript uses curly braces.\n\n3. Typing: Python is a dynamically typed language, which means that the type of a variable is determined at runtime, while JavaScript is both dynamically and weakly typed, meaning that variable types can change at runtime and type errors may not be caught immediately.\n\n4. Application areas: Python is often used for scientific computing, data analysis, and machine learning, while JavaScript is most commonly used for web development and client-side scripting.\n\nOverall, both Python and JavaScript have their own strengths and weaknesses, and which language to use largely depends on the specific requirements of the project.", "metadata": {}}
-{"answer_id": "RcqQg364kgCkYPshqvmGSp", "model_id": "gpt-3.5-turbo:20230327", "question_id": 4, "text": "Here are some tips that can help you increase your productivity while working from home:\n\n1. Create a dedicated workspace: Pick a place where you can work undisturbed and create an environment that resembles your office.\n\n2. Set a schedule: Establish a specific working schedule and stick to it as much as possible.\n\n3. Take breaks: Take short breaks throughout the day to recharge and refocus your energy.\n\n4. Eliminate distractions: Turn off your phone notifications and avoid any unnecessary distractions.\n\n5. Make a to-do list: Prioritize your tasks in order of importance and create a to-do list.\n\n6. Get dressed: Dressing as you would for work helps put you in the right mindset for productivity.\n\n7. Challenge yourself: Set small and achievable goals and push yourself to complete them.\n\n8. Stay positive: Maintain a positive attitude towards your work, and try to avoid negativity.\n\nWith these tips, you can stay productive and enjoy the benefits of working from home.", "metadata": {}}
-{"answer_id": "3R9jAndzLHQqJwmMi5mNox", "model_id": "gpt-3.5-turbo:20230327", "question_id": 5, "text": "Certainly! Quantum computing is a type of computing that uses quantum-mechanical phenomena to process information. \n\nIn classical computing, a bit can exist in one of two states - 0 or 1. In quantum computing, a qubit can exist in a superposition of states, which means it can exist in a combination of 0 and 1 at the same time. This ability to exist in multiple states simultaneously allows for the processing of large amounts of information simultaneously and can significantly speed up certain types of computations.\n\nQuantum computers use a series of quantum gates - which are similar to the logic gates used in classical computers - to manipulate the state of the qubits, allowing for the computations to be completed. There are several different technologies used to build quantum computers, including superconducting quantum processors, trapped-ion quantum processors, and photonic quantum processors.\n\nOne of the most important applications of quantum computing is in solving problems that are intractable for classical computers. For example, quantum computers can be used to factor large numbers, which is the basis for many encryption protocols - leading to exciting possibilities in code-breaking, cryptography, and cybersecurity in the future.", "metadata": {}}
-{"answer_id": "Uw8SaYLPMGR5sdV9FDx8pb", "model_id": "gpt-3.5-turbo:20230327", "question_id": 6, "text": "Plant-based protein sources are derived from plants, including legumes, nuts, seeds, and whole grains. They tend to be lower in saturated fat, higher in fiber and several micronutrients, and have less of an impact on the environment when compared to animal-based protein sources.\n\nAnimal-based protein sources are derived from animals, including meat, dairy, and eggs. They tend to be higher in saturated fat and cholesterol, which can increase the risk of heart disease and other health problems when consumed in excess. They are also associated with a higher environmental impact.\n\nHowever, animal-based protein sources tend to be more readily absorbed and complete (containing all essential amino acids), making them a good choice for those with higher protein needs, such as athletes or individuals who have difficulty meeting their protein requirements through plant-based sources alone.", "metadata": {}}
-{"answer_id": "53gmokt2KBgKu6NMPopxcu", "model_id": "gpt-3.5-turbo:20230327", "question_id": 7, "text": "Sure, here are a few practical tips to develop critical thinking skills:\n\n1. Ask questions: Don't simply accept information at face value. Ask questions that allow you to evaluate the reason, perspective and logic behind the information.\n\n2. Analyze: Take the time to analyze and evaluate the information that you receive. This will help you to identify assumptions, biases, strengths and weaknesses of an argument.\n\n3. Consider different perspectives: When analyzing information, try to consider different perspectives to gain a wider understanding of any topic.\n\n4. Research and read extensively: Read a wide range of sources to get a better understanding of different viewpoints and opinions.\n\n5. Practice logic and reasoning: Practice thinking through problems logically by breaking them down into smaller parts.\n\n6. Establish a point of view: Consider developing an opinion or thesis statement, and then collect evidence to support that position.\n\n7. Reflect on your own thinking: Consider how you arrived at a conclusion, and whether you have any biases or assumptions that may have influenced your thinking.\n\nWith time, and practicing these skills consistently, you can become a better critical thinker.", "metadata": {}}
-{"answer_id": "bKrbrGsN7zjKLvBk2h3tvo", "model_id": "gpt-3.5-turbo:20230327", "question_id": 8, "text": "There are several major challenges faced by the education sector today, including:\n\n1. Access to Education: Many children, particularly those in developing countries, do not have access to quality education.\n\n2. Funding: Education is expensive, and many governments struggle to find the necessary funding to provide quality education to all students.\n\n3. Teacher Shortages: Many countries face a shortage of qualified teachers, which can negatively impact the quality of education.\n\n4. Technological Integration: Many schools struggle to effectively integrate technology into their classrooms, which can limit student learning and engagement.\n\n5. Student Engagement and Motivation: Getting students to engage with their learning and stay motivated can be a significant challenge for educators.\n\n6. Student Diversity: Students come from diverse backgrounds and have different learning styles, making it difficult for educators to provide a one-size-fits-all approach to teaching.\n\n7. Assessment and Evaluation: Measuring student learning and progress can be a challenge, particularly in non-standardized environments where students are assessed based on different criteria.", "metadata": {}}
-{"answer_id": "HEGL3aPUnNrdNtNt3XLDKi", "model_id": "gpt-3.5-turbo:20230327", "question_id": 9, "text": "There are several primary factors that influence consumer behavior, including:\n\n1. Personal factors - These include individual characteristics such as age, gender, income, education, personality, and lifestyle.\n\n2. Psychological factors - These include the consumer's perception of the product or service, motives, attitudes, beliefs, and values.\n\n3. Social factors - These include the consumer's family, friends, social class, culture, and reference groups.\n\n4. Situational factors - These include the physical environment, time, and mood of the consumer.\n\n5. Marketing mix factors - These include product, price, promotion, and distribution, and how they affect the consumer's decision-making process.", "metadata": {}}
-{"answer_id": "W9zpMVa2cJUJW8B2uGMCJy", "model_id": "gpt-3.5-turbo:20230327", "question_id": 10, "text": "Here are some effective strategies for conflict resolution in the workplace:\n\n1. Listen actively: Allow both parties to express their perspective without interrupting or judging them.\n\n2. Find common ground: Look for areas of agreement and shared goals between the parties.\n\n3. Identify the root cause: Determine the underlying issue that led to the conflict.\n\n4. Brainstorm solutions: Collaborate with both parties to come up with a solution that addresses their needs and interests.\n\n5. Communicate clearly: Make sure that everyone involved understands the resolution and the steps needed to move forward.\n\n6. Follow up: Check in with both parties to determine if the resolution worked and if any adjustments need to be made.\n\n7. Seek outside help if necessary: If the conflict is too complex, it may be necessary to involve a mediator or other neutral third party to facilitate the resolution process.", "metadata": {}}
-{"answer_id": "LacdmoweqKYGzt3aMBYjEa", "model_id": "gpt-3.5-turbo:20230327", "question_id": 11, "text": "Using a single-use plastic bottle has negative implications on both the environment and human health. Here are some of the potential consequences:\n\n1. Environmental Implications: Single-use plastic bottles contribute to plastic pollution in the environment. These bottles take thousands of years to decompose, and they pose a severe threat to wildlife, especially marine life. They often end up in oceans, rivers, and other water bodies, where they harm marine animals by entangling them or being ingested. It also contributes to the depletion of natural resources, as these bottles are typically made from non-renewable fossil fuels.\n\n2. Human Health Implications: There are potential health implications for both consumers and workers involved in the production and disposal of single-use plastic bottles. The production of these bottles releases toxic chemicals that can harm the environment and the workers involved. Ingesting plastic particles can also affect human health, resulting in hormonal imbalances and other negative health impacts.\n\nIn contrast, using a reusable bottle has several positive implications:\n\n1. Environmental Benefits: Reusable bottles can reduce plastic waste and help conserve natural resources. They reduce the number of disposable bottles that end up in landfills and oceans, helping reduce plastic pollution.\n\n2. Health Benefits: Using a reusable bottle helps to reduce the exposure to harmful chemicals associated with plastic production, as well as reducing the potential health impact of ingesting plastic particles.\n\nOverall, using a reusable bottle is better for the environment, wildlife, and human health. It is a more sustainable and responsible choice that supports efforts to conserve natural resources and reduce plastic waste.", "metadata": {}}
-{"answer_id": "JqVreebbPuNdjw8E8K4Ssf", "model_id": "gpt-3.5-turbo:20230327", "question_id": 12, "text": "There are several factors that should be considered when designing an inclusive and accessible public transportation system, including:\n\n1. Physical accessibility: ensuring that transportation infrastructure, such as bus stops or train stations, are physically accessible to people with disabilities or mobility impairments, with features such as ramps, elevators, and tactile paving.\n\n2. Signage and wayfinding: providing clear, visible and easy-to-read signs, maps, and audio announcements that help passengers navigate the system and find their way around.\n\n3. Sensory inclusivity: designing the system with the needs of passengers who have sensory sensitivities or disabilities in mind, such as minimizing noise levels from trains or buses and providing calming spaces.\n\n4. Universal Design: adopting universal design principles and practices, making sure that the system is designed to be user-friendly for all, regardless of ability or disability.\n\n5. Employee training and awareness: ensuring that staff, including drivers, are properly trained and have the necessary knowledge to assist passengers with disabilities or other accessibility needs.\n\nOther factors to consider may include affordability, availability, reliability, and safety in order to provide a comprehensive and inclusive transportation system for all.", "metadata": {}}
-{"answer_id": "hEMThhsN85Ud5X8xBv9BZJ", "model_id": "gpt-3.5-turbo:20230327", "question_id": 13, "text": "Governments typically use a combination of fiscal and monetary policies to combat economic recessions.\n\n1. Fiscal policies: \n\nFiscal policies refer to the government's decisions related to taxes and government spending. In a recession, governments can use fiscal policies to boost economic activity by:\n\n- Increasing government spending: Governments can increase spending on infrastructure projects, social welfare programs, and other public projects to create jobs and stimulate economic activity.\n\n- Cutting taxes: Reducing taxes, particularly for low- and middle-income households, can boost consumer spending and encourage businesses to invest and create new jobs.\n\n- Providing subsidies: Governments can provide subsidies to businesses and industries in order to encourage investment and growth. \n\n2. Monetary policies: \n\nMonetary policies refer to the management of the money supply and interest rates. In a recession, governments can use monetary policies to stimulate economic activity by:\n\n- Lowering interest rates: Lower interest rates can make borrowing cheaper and businesses may be more likely to invest in new projects and hire workers.\n\n- Quantitative easing: Central banks can engage in quantitative easing by purchasing government bonds or other assets, which can increase the money supply and stimulate economic activity.\n\nOverall, the goal of both fiscal and monetary policies is to inject demand into the economy and get people and businesses spending and investing again in order to help lift the economy out of recession.", "metadata": {}}
-{"answer_id": "BvFV7sx53PAK5bNn89urFs", "model_id": "gpt-3.5-turbo:20230327", "question_id": 14, "text": "Language and cultural barriers can have a significant impact on communication and relationships in multicultural societies. \n\nIn terms of language barriers, individuals who do not share a common language may find it difficult to communicate effectively. This can lead to misunderstandings, miscommunications, and frustrations in trying to convey their thoughts and feelings. Language barriers can also make it challenging to establish trust and build strong relationships, as people may feel hesitant to share personal information or express their opinions if they are unable to do so fluently in a shared language.\n\nCultural barriers can also play a role in shaping communication and relationships. People from different cultural backgrounds may have different beliefs, values, and norms around topics such as social interaction, personal space, and nonverbal communication, among others. These cultural differences can create confusion or misunderstandings that can impact communication and relationships.\n\nHowever, it's important to note that language and cultural barriers do not need to be insurmountable obstacles. Through efforts such as language classes, cultural exchange programs, and sensitivity training, individuals can learn to navigate these differences and build stronger relationships across cultures.", "metadata": {}}
-{"answer_id": "dM5GHbLuPNfzUbBnJz6w7K", "model_id": "gpt-3.5-turbo:20230327", "question_id": 15, "text": "Sure, here is one possible scenario:\n\nImagine a hospital that uses AI-powered chatbots to assist patients with their medical inquiries quickly and accurately. When patients arrive at the hospital for appointments, they could interact with the chatbot on their smartphones or on a tablet provided in the waiting area.\n\nThe chatbot could ask patients a series of questions to understand their symptoms or medical condition. It could then provide basic medical advice, schedule a doctor's appointment, order lab tests or prescription refills, or refer patients to specialists as required.\n\nBy using AI, the hospital could improve the quality and efficiency of healthcare delivery in several ways. The chatbot could triage patients based on their symptoms and urgency, reducing the burden on doctors and nurses to answer basic inquiries. This would free up medical staff to focus on more complex cases, leading to improved patient outcomes and satisfaction.\n\nMoreover, by automating routine tasks such as scheduling appointments, ordering lab tests or refilling prescriptions, hospitals could improve their operational efficiency and reduce costs. This would lead to better resource allocation, less waiting time for patients, and ultimately a more satisfied patient population.\n\nOverall, by leveraging AI for patient triage and routine tasks, care providers could offer more targeted and personalized care, while reducing the burden on medical staff and improving the efficiency of healthcare delivery.", "metadata": {}}
-{"answer_id": "BX7maaP5kGY6bBTLJRwkit", "model_id": "gpt-3.5-turbo:20230327", "question_id": 16, "text": "Gene editing is a process of changing the DNA of an organism in order to alter its traits. CRISPR-Cas9 is a gene editing technology that has revolutionized the field of genetic engineering. In this technique, the Cas9 enzyme is guided by a molecule called guide RNA (gRNA), which is programmed to recognize and cut specific sequences of DNA. This cut then triggers the cell\u2019s natural DNA repair mechanism, leading to the addition, deletion, or replacement of specific DNA sequences.\n\nCRISPR-Cas9 has a wide range of potential applications, including the ability to cure genetic diseases, develop new treatments for cancer, and create new crop varieties with improved yield and resistance to pests and disease. It can also be used to enhance human traits, such as intelligence or physical performance, which raises ethical concerns about the potential for eugenics.\n\nOne of the main ethical concerns associated with CRISPR-Cas9 is the risk of unintended consequences. Gene editing can have unforeseeable impacts on the environment and future generations, which could lead to potentially harmful and irreversible effects. There are also concerns about the potential misuse of gene editing technology for unethical purposes, such as creating designer babies or enhancing traits for non-therapeutic purposes.\n\nAnother ethical consideration is the issue of access and equity. CRISPR-Cas9 is an expensive technology, and its use could widen the gap between the haves and the have-nots, with potentially devastating consequences for global health and social justice.\n\nIn conclusion, while the potential benefits of CRISPR-Cas9 are immense, its ethical implications are complex and multifaceted. Careful consideration and regulation of its use will be required to maximize its benefits, while minimizing its potential risks and harms.", "metadata": {}}
-{"answer_id": "STuX8oc7Gu3SN6EWzwpUpp", "model_id": "gpt-3.5-turbo:20230327", "question_id": 17, "text": "Vaccinations work by introducing a weakened or deactivated form of a virus or bacteria into the body, which triggers the immune system to create a defense against it. This defense, in the form of antibodies, allows the immune system to recognize and destroy the actual virus or bacteria if a person is exposed to it later on. By receiving the vaccine, individuals become immune to the disease without having to experience its symptoms.\n\nWhen a large percentage of a population is vaccinated against a particular disease, it becomes difficult for the disease to spread because there are fewer people who can catch and transmit it. This concept is called herd immunity. Herd immunity protects individuals who cannot receive vaccines, such as those with weakened immune systems, and those for whom vaccines are not effective. It also helps prevent the spread of infectious diseases in the community at large, making outbreaks less likely to occur.", "metadata": {}}
-{"answer_id": "TFUUXWS7yn2u2b4n7eM3ZB", "model_id": "gpt-3.5-turbo:20230327", "question_id": 18, "text": "Social media platforms play a significant role in the way people consume and share news. They have revolutionized the distribution of news by providing instant access to real-time happenings around the world. Social media platforms promote the democratization of information, providing a platform where everyone can share their opinions, thoughts, and ideas.\n\nHowever, the downside to social media platforms is the implications they create for the spread of misinformation. Since everyone can share information, it is often challenging to verify the accuracy and relevance of the information presented online. It's also difficult for readers to identify credible sources of information and, thus, the authenticity of the news.\n\nThe spread of misinformation has grave implications for society, as it can significantly impact public opinion, harm reputations, and even incite violence. As a result, social media platforms have a responsibility to control the spread of false information and misinformation.\n\nConclusion\n\nIn conclusion, social media platforms play a critical role in the distribution of news. While the democratization of information is a fantastic feat, the spread of misinformation is an on-going challenge, inhibiting the accurate distribution of news. Social media platforms must take accountability and act to address the issue of misinformation by creating control mechanisms to contain the problem.", "metadata": {}}
-{"answer_id": "3yRq2XXPi83H7Rr5SZS9rE", "model_id": "gpt-3.5-turbo:20230327", "question_id": 19, "text": "Cultural, social, and economic factors are among the most important factors that influence people's food choices. Cultural factors refer to the customs, beliefs, and traditions that people have when it comes to the foods they eat. Social factors include peer pressure, advertising, and media. Economic factors include cost, availability, and accessibility.\n\nFor example, some cultures emphasize the consumption of large amounts of meat and animal products as a sign of wealth and prosperity. In such circumstances, it can be challenging to promote healthier diets that focus on plant-based sources of protein.\n\nSimilarly, social factors like advertising can influence people's food choices by promoting unhealthy food options that are high in sugar, salt, and fat. These foods tend to be cheap and readily available, making them an attractive option for people who are cost-conscious.\n\nFinally, economic factors affect food choices by limiting access to healthy foods in certain areas or pricing them out of reach for many people.\n\nTo promote healthier diets, it is crucial to understand these cultural, social, and economic factors and address them through education, policy changes, and community-based interventions. For example, raising awareness of the benefits of a plant-based diet, imposing taxes on unhealthy foods, and increasing funding for community gardens and farmers' markets can help encourage healthier food choices. Additionally, working with local businesses and restaurants to provide healthier, affordable meal options can also have a significant impact on improving population health.", "metadata": {}}
-{"answer_id": "Sw34dAwQPCfGGotwRwhvtv", "model_id": "gpt-3.5-turbo:20230327", "question_id": 20, "text": "Sure, here\u2019s an explanation:\n\nNatural selection is the process through which certain traits or characteristics become more common in a population over time, because individuals with those traits are more likely to survive and reproduce. This process contributes to the evolution and adaptation of species.\n\nHere\u2019s how it works: Within a population, there is genetic variation \u2013 some individuals will have different traits, such as size, color, or behavior. In environments with limited resources or where there are predators, individuals with certain advantageous traits are better suited to survive, find resources, and avoid danger. These individuals are more likely to reproduce and pass on their advantageous traits to their offspring. Over time, this process results in the spread and eventual dominance of those advantageous traits in the population. This is the essence of natural selection.\n\nAs a result, natural selection is an important driver of evolution and adaptation of species to their environments. Through natural selection, advantageous traits become more common and disadvantageous ones become less common, leading to the emergence of new species or the modification of existing ones.", "metadata": {}}
-{"answer_id": "cZw4Jw8Zyz6ZUy4WDsC6ta", "model_id": "gpt-3.5-turbo:20230327", "question_id": 21, "text": "Greetings, noble lords and ladies. I am Sir [Name], a humble knight in service to this great kingdom. It is an honor to be in the presence of such esteemed company, and I am grateful for the opportunity to share in this feast with all of you. I have served my lord with valor and courage, and I pledge to continue to defend and protect this land with my life and sword. May our cups overflow and our merriment be endless on this joyous occasion.", "metadata": {}}
-{"answer_id": "nj9y2HTWFGsD5B278ozm73", "model_id": "gpt-3.5-turbo:20230327", "question_id": 22, "text": "Ahoy, my fellow pirates! As we set sail in search of treasure, let us remember the thrill of adventure and the rewards that await us. There are riches beyond our wildest dreams waiting to be discovered, and we are the brave souls who will claim them. Each of us has a role to play in this mission, whether it be navigating, scouting, or fighting. Together, we will overcome any obstacle in our path and emerge victorious, with treasure beyond measure. So, let us hoist the sails and set course for glory!", "metadata": {}}
-{"answer_id": "Ep9rLcNzEGhG7AgbLTpRtm", "model_id": "gpt-3.5-turbo:20230327", "question_id": 23, "text": "Oh, my dearest love, my heart doth beat\n\nWith every step thou tak'st, my soul doth meet\n\nA sudden warmth, a new and fiery thrust\n\nThat fills my being with an ardent lust.\n\nThou art the sun that lights my every day\n\nThe moon that guides me on my nightly way\n\nThou art the very air that I do breathe\n\nThou art the one for whom my heart doth cleave.\n\nSo hear me now, ye gods above\n\nAnd witness to this heartening love\n\nThat I do feel, with all my heart\n\nFor her, my love, the fairest art.", "metadata": {}}
-{"answer_id": "oNULT72cYUvit7D9SHb5aM", "model_id": "gpt-3.5-turbo:20230327", "question_id": 24, "text": "Well, little one, my origin story begins with an ordinary person like you or me. One day, something extraordinary happened that changed my life forever. For me, it was an accident that infused me with amazing powers.\n\nAt first, I wasn't sure what to do with my new abilities, but I knew I wanted to use them to help people. So, I put on a costume and became a superhero. Every day, I use my powers to fight crime, protect the innocent, and make the world a better place.\n\nBut the most important thing to remember is that anyone can be a hero, even if they don't have powers. All you need is the courage to do what's right and help those in need.", "metadata": {}}
-{"answer_id": "TX86xjPKTk2UxWwV4e8zRK", "model_id": "gpt-3.5-turbo:20230327", "question_id": 25, "text": "As a time traveler from the year 3000, I would tell people about the following technological advancements:\n\n1. Advanced Artificial Intelligence: In the future, AI is so advanced that it can completely automate many jobs that humans currently do. This has resulted in increased productivity and efficiency across many industries.\n\n2. Quantum Computing: Quantum computers are used extensively in the future and have led to huge advancements in fields like physics, chemistry, and cryptography.\n\n3. Space Travel: With new propulsion systems and faster-than-light travel, space travel is much faster and more accessible to the general public. We have even established a permanent settlement on Mars, and plans are underway for further exploration and colonization of the galaxy.\n\n4. Renewable Energy: Energy production has shifted primarily to renewable sources like solar and wind, with new breakthroughs in energy storage and distribution making it easier to power societies without relying on fossil fuels.\n\n5. Medical Advancements: With gene editing and advanced prosthetics, people are able to live much longer and healthier lives. Diseases like cancer and Alzheimer's have been largely eradicated, and there are even experimental treatments that can reverse the aging process.\n\nThese technological advancements have transformed society in countless ways, and I believe they will continue to shape the course of human history for years to come.", "metadata": {}}
-{"answer_id": "e5YFb6PojDThkcZdSH8kpC", "model_id": "gpt-3.5-turbo:20230327", "question_id": 26, "text": "It's the final moments of the championship game and the score is tied. The tension in the arena is palpable as both teams desperately try to secure the victory.\n\nWith just seconds left on the clock, the ball is in the hands of the star player. He looks up and sees an opening in the defense.\n\nHe makes a quick cut to the right and drives towards the basket. The defense tries to keep up, but he's too quick. \n\nAs he jumps into the air, he releases the ball with perfect form. It floats through the air, seemingly in slow motion, before finding its way into the basket.\n\nThe crowd erupts in cheers as the buzzer sounds, signaling the end of the game. The team rushes onto the court, hugging and high-fiving each other in celebration of their victory.\n\nThe winning shot will go down in history as one of the greatest moments in sports, and the championship trophy will be proudly displayed in their locker room for years to come.", "metadata": {}}
-{"answer_id": "NnkbyoNFRHay4toKTFfaeE", "model_id": "gpt-3.5-turbo:20230327", "question_id": 27, "text": "Welcome, esteemed judges. I'm excited to present my signature dish, which has been inspired by my travels and memories from my childhood. This dish is a fusion of flavors and ingredients that create a unique and unforgettable experience for your taste buds.\n\nMy dish starts with a perfectly seared and succulent piece of protein - tender Wagyu beef, which has been marinated with a special blend of spices, and cooked to medium-rare perfection. The beef is then served on a bed of exotic mushrooms, braised in a rich red wine sauce, with hints of tangy tomato and sweet caramelized onions.\n\nBut that's not all - to balance out the richness of the beef and the savory flavors of the sauce, I've added a bright and zesty salad, which features Japanese seaweed, picked sesame seeds, and a delicate yuzu dressing. And finally, to bring it all together, a dash of my secret spice blend, which ties all the flavors together.\n\nThis dish is like a journey that takes you from the earthy flavors of the beef and mushrooms to the vibrant freshness of the salad, and leaves you with a satisfying warmth and lingering sweetness from the savory wine sauce. Judges, I hope you enjoy my signature dish as much as I enjoyed creating it!", "metadata": {}}
-{"answer_id": "Gpb8MuNU3Pt7k93dpRN9WM", "model_id": "gpt-3.5-turbo:20230327", "question_id": 28, "text": "I'm sorry, but as an AI language model, I am not capable of reaching the summit of a mountain, experiencing emotions or seeing the view from the top. However, I can provide details about the summit of Mount Everest based on information available to me. \n\nStanding at the summit of Mount Everest, the highest peak in the world at an elevation of 29,029 feet, would be an incredible feat for any climber. The ascent to the top of this majestic mountain is challenging, dangerous and requires physical and mental preparation. The weather on the summit is unpredictable, and the oxygen is thin, making it difficult to breathe. But with that being said, the view from the summit is said to be breathtaking, with a panoramic view of the Himalayas and the surrounding landscape that stretches out for miles below. The sense of accomplishment and pride in achieving such a difficult climb would undoubtedly be an emotional experience for any mountain climber.", "metadata": {}}
-{"answer_id": "SYvkCCHBUZPd9DQuidZM8K", "model_id": "gpt-3.5-turbo:20230327", "question_id": 29, "text": "As a space colonist on Mars, my daily life would be vastly different from life on Earth. Some of the main challenges I would face living on another planet are:\n\n1. Limited Resources: One of the primary challenges of living on Mars would be the scarcity of resources such as food, water, and oxygen. As a Mars colonist, I would have to rely on recycling and sustainable practices to make the most of the resources available.\n\n2. Communication Delays: As Mars is quite far from Earth, communication delays between the two planets would be significant. This would make real-time communication difficult, and we would have to rely on automated systems or pre-planned communication schedules.\n\n3. Extreme Environment: The Martian environment is harsh and hostile, with high levels of radiation, extreme temperature fluctuations, and violent dust storms. Living in such an environment would require advanced protective gear and equipment, as well as being always prepared to handle emergency situations.\n\n4. Monotony: Mars would not have the diverse range of plant and animal life we have on Earth. The monotony of the daily routine, the lack of variety in the environment, and the isolation could lead to increased stress levels and mental health issues.\n\nDespite the many challenges, living on Mars could also be exhilarating and full of opportunities to explore and study a new world. My daily life would include conducting scientific experiments, studying the Martian landscape, maintaining the habitats and equipment, and possibly even going on spacewalks or exploring the planet's surface. We would have to work together as a close-knit team to ensure our survival, and our work would contribute to a better understanding of our universe and our place in it.", "metadata": {}}
-{"answer_id": "NjdsG8tYfrHMT5zGZPavk6", "model_id": "gpt-3.5-turbo:20230327", "question_id": 30, "text": "I am a survivor in a post-apocalyptic world, where resources are limited and danger lurks around every corner. To survive, I rely on my cunning, resourcefulness, and connections.\n\nMy first ally was a fellow survivor named Jake. We met fortuitously when we both stumbled upon the same abandoned warehouse, both seeking shelter for the night. After a long and tense standoff, we decided to share the space and split the resources we had scrounged up. We quickly became friends, relying on each other for survival and companionship.\n\nAs we made our way through the wasteland, we met other survivors, some friendly and some not. I learned that trust was a luxury I couldn't afford, and that my instincts would have to guide me in all my interactions.\n\nOne of our most valuable allies was a woman named Eva, who had managed to survive on her own for years. She taught us how to scavenge for food, how to make weapons out of scraps, and how to avoid danger on the road.\n\nThough our group often faced challenges and setbacks, we persisted, relying on each other for survival in a world where simple existence was a triumph. And as we journeyed together, we learned that the bonds forged in such extreme circumstances were unbreakable, and that together we had a fighting chance.", "metadata": {}}
-{"answer_id": "8eovAhyvrKJEMWiVdYzByH", "model_id": "gpt-3.5-turbo:20230327", "question_id": 31, "text": "There are a few ways to determine if a restaurant is popular among locals or mainly attracts tourists:\n\n1. Look for reviews online from local residents. If a restaurant has a high number of reviews from locals and they are generally positive, it's a good indication that it's popular among residents.\n\n2. Observe the clientele while visiting the restaurant. If you notice a lot of tourists, especially those who are carrying guidebooks and taking photos of the food, it's likely that the restaurant is more of a tourist destination.\n\n3. Talk to the staff if possible. They may be able to give you some insight into the restaurant's clientele and whether they tend to be more locals or tourists.\n\nKnowing whether a restaurant is popular among locals or mainly attracts tourists can be useful for a few reasons. If you are a tourist, you may be looking for a more authentic local experience and want to avoid restaurants that are primarily geared towards tourists. On the other hand, if you are a local resident, you may be interested in trying out new restaurants that are popular among your peers. Additionally, if you are a business owner looking to open a restaurant, this information can be helpful in determining the restaurant's target market and marketing strategy.", "metadata": {}}
-{"answer_id": "nvyaGEveLWBaxgXzriB93d", "model_id": "gpt-3.5-turbo:20230327", "question_id": 32, "text": "Here are some subtle clues you can look for to identify when someone is pretending to understand a topic or conversation:\n\n1. They use vague language or buzzwords to mask their lack of knowledge.\n2. They repeat what others have said without adding anything new or asking any questions.\n3. They nod their head or make other nonverbal cues to suggest they are following along, but don\u2019t actively participate.\n4. They avoid eye contact or become defensive when asked to expand on their thoughts or clarify their understanding.\n5. They don\u2019t ask any questions or seek clarification when they don\u2019t understand something.\n6. They use filler phrases such as \u201cyeah\u201d or \u201cokay\u201d in response to questions or statements without contributing anything of value.\n7. They change the subject abruptly or avoid engaging in the conversation altogether.", "metadata": {}}
-{"answer_id": "3xU2t6Yvx9EWpqfqvinNfH", "model_id": "gpt-3.5-turbo:20230327", "question_id": 33, "text": "There are a few reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app. These reasons include:\n\n1. GPS devices and smartphone apps require a source of power, and therefore, can run out of battery. Paper maps, on the other hand, do not require any power and can be used at any time.\n\n2. Sometimes, GPS devices or smartphone apps can provide incorrect information or lose signal. In these cases, paper maps or directions from locals might be more reliable.\n\n3. Reading a paper map can help you get a better sense of the geography and the layout of the area you are traveling through. This can help you identify landmarks or other important features that may not be visible on a digital map.\n\n4. Some people prefer the tactile experience of using a paper map and finding their way through an area using their own navigation skills.\n\n5. Finally, trusting your own sense of direction and intuition can be a rewarding experience, and can help you build a better understanding of the area you are traveling through.", "metadata": {}}
-{"answer_id": "Mq6hzNziUxzQ2juPMDrv3h", "model_id": "gpt-3.5-turbo:20230327", "question_id": 34, "text": "There are several ways to determine if a person is genuinely interested in a conversation or simply being polite:\n\n1. Body language - If the person is genuinely interested in the conversation, they will usually lean in slightly, make eye contact, and use open and relaxed body language.\n\n2. Active listening - If the person is actively listening to what you're saying, asking questions and responding with their own thoughts, then they are probably genuinely interested in the conversation.\n\n3. Follow-up - If the person follows up with you after the conversation or remembers details from your conversation in future interactions, then they were likely genuinely interested.\n\n4. Timing - If the person is in a rush to end the conversation or seems distracted, they may be polite and trying to end the conversation quickly.\n\nOverall, it's important to pay attention to both verbal and nonverbal cues to determine if someone is genuinely interested in a conversation or simply being polite.", "metadata": {}}
-{"answer_id": "KU6BNNN8d6MLHyrA8nV4DB", "model_id": "gpt-3.5-turbo:20230327", "question_id": 35, "text": "There are several reasons why someone may prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher:\n\n1. Personalized service: Local businesses often offer personalized service because they know their customers by name and can offer individualized recommendations and support.\n\n2. Supporting the local economy: Shopping at locally-owned businesses helps to keep money in the local economy and supports job creation in the community.\n\n3. Unique products: Independent businesses often offer unique, one-of-a-kind products that cannot be found at larger chain stores.\n\n4. Environmental impact: Small businesses tend to have a lower carbon footprint as they source their products and materials locally, reducing transportation and shipping emissions.\n\n5. Community investment: Local businesses are often more invested in their community and are more likely to donate to local charities and civic organizations, further supporting the local community.", "metadata": {}}
-{"answer_id": "RpHbPLJamuknRRa3xU5bUF", "model_id": "gpt-3.5-turbo:20230327", "question_id": 36, "text": "As an AI language model, I can suggest that there are several factors to consider when assessing the credibility of a source of information:\n\n1. Check the date: Make sure the information is up-to-date and relevant to the topic you're researching.\n\n2. Identify the author: Look for information about the author\u2019s credentials, expertise, and affiliation. Conduct a quick search on the author to see if they have any biases, conflicts of interest, or a history of producing misleading or inaccurate content.\n\n3. Evaluate the source: Determine where the information is coming from. Check the domain name or URL extension to see if it\u2019s affiliated with a reputable organization or institution. Avoid sites with questionable or overly sensational headlines.\n\n4. Verify the information: Verify the information by fact-checking it using reputable sources like academic journals, government websites, or reliable news sources.\n\n5. Evaluate the tone: Evaluate the tone of the article or post. Is it objective, balanced, and free of inflammatory language? Be wary of articles that rely heavily on emotional language or use sensationalized claims to try to persuade you to take action.\n\nTaking a critical approach and triangulating information from multiple sources can help ensure that you have a reliable and accurate understanding of the subject matter.", "metadata": {}}
-{"answer_id": "AFR3AJW4sSPLDLiAUvrL8s", "model_id": "gpt-3.5-turbo:20230327", "question_id": 37, "text": "There is no one definitive answer to this question, as individual preferences for fear-inducing experiences are rooted in a variety of factors, including personality traits, past experiences, and cultural backgrounds. Some people may enjoy the thrill of being scared because it provides an adrenaline rush and a feeling of excitement, while others may find it cathartic or empowering to confront their fears in a safe environment. On the other hand, some people may avoid scary experiences because they find them stressful or anxiety-provoking, or because they have had negative experiences with fear in the past. Ultimately, each person's response to fear-inducing stimuli is unique, and depends on a variety of psychological and physiological factors.", "metadata": {}}
-{"answer_id": "esqiBYHa56ygcPU2ux2Pdx", "model_id": "gpt-3.5-turbo:20230327", "question_id": 38, "text": "Observing the behavior of other people in a social situation can provide clues about cultural norms and expectations in various ways. For example:\n\n1. Dress codes: Observing how people dress in a particular social situation can provide insight into what is considered appropriate or expected attire. For instance, if people are dressed conservatively, it may indicate that this is the norm for that culture.\n\n2. Social hierarchy: Observing how people interact with each other can provide clues about the social hierarchy or power dynamics within a culture. For instance, if people defer to older individuals or those in positions of authority, it may indicate that respect for elders and hierarchy are important cultural values.\n\n3. Communication styles: Observing how people communicate with each other can provide clues about their communication styles and what is considered appropriate behavior in that culture. For instance, if people speak softly and avoid eye contact, it may indicate that the culture values modesty and avoiding confrontation.\n\n4. Customs and traditions: Observing how people celebrate holidays or special occasions can provide insight into cultural customs and traditions. For instance, if people exchange gifts or participate in specific rituals, it may indicate that these are important cultural practices.\n\nOverall, by paying attention to social cues and observing the behavior of other people in cultural contexts, one can gain insight into the norms and expectations of that culture.", "metadata": {}}
-{"answer_id": "NmuuKUipqt62QKuEHCuBWh", "model_id": "gpt-3.5-turbo:20230327", "question_id": 39, "text": "This is a complex question that requires a thoughtful and nuanced answer. There are arguments to be made on both sides, and ultimately, it comes down to personal values and priorities.\n\nOn one hand, exploring space is a noble and inspiring goal that has the potential to expand our understanding of the universe and our place in it. It could also bring about new advancements in technology and inspire young people to pursue careers in science and engineering.\n\nOn the other hand, there are many urgent problems facing our planet that require immediate attention and resources, including climate change, poverty, inequality, and access to healthcare and education. Some argue that we should prioritize solving these issues before committing resources to space exploration.\n\nUltimately, it's up to individuals, governments, and societies to decide how to balance these competing priorities. It is possible to do both - to invest in space exploration while also addressing pressing problems on Earth.", "metadata": {}}
-{"answer_id": "3HypDqXt6tHieMDN7hWYCh", "model_id": "gpt-3.5-turbo:20230327", "question_id": 40, "text": "It's important to strike a balance between job creation and technological progress. Technological progress can bring about many benefits such as increased efficiency, convenience, and innovation. However, it can also result in job displacement or in certain cases, re-skilling for employees. It's important for companies to recognize the impact of their technological advancements and to consider implementing measures to support their employees through the transition.\n\nOn the other hand, job creation is also important as it supports a healthy economy and ensures that individuals have the opportunity to support themselves and their families. Encouraging entrepreneurship, creating incentives for businesses, and investing in education and job training programs can all be effective methods for promoting job creation.\n\nOverall, the key is to recognize the importance of both job creation and technological progress and to strive for a balance that benefits both individuals and society as a whole.", "metadata": {}}
-{"answer_id": "DmQtupeyNDrQFBccBRAsbD", "model_id": "gpt-3.5-turbo:20230327", "question_id": 41, "text": "The average human blinks approximately 15-20 times per minute. Let's assume that the average waking hour of a person is around 16 hours per day, which means that they blink around 15 x 16 = 240 times per day.\n\nIn one year, this would translate to 240 x 365 = 87,600 blinks per year.\n\nAssuming a lifespan of 75 years, the total number of blinks would be 87,600 x 75 = 6,570,000.\n\nSo, on average, a human blinks about 6.6 million times in their lifetime.\n\nIt's important to note that this is just an estimate based on the assumptions that were made, and not everyone's blinking frequency is the same. Factors such as age, health, and environment can affect how often someone blinks.", "metadata": {}}
-{"answer_id": "froHv7kwRMYGWPXDQXk2Gw", "model_id": "gpt-3.5-turbo:20230327", "question_id": 42, "text": "A grain of salt is a very small amount of salt, so it is difficult to visualize how many atoms are in it. However, we can approximate the number of atoms in a grain of salt by using some scientific formulas and data.\n\nFirstly, we need to know the mass of a grain of salt. According to the National Institute of Standards and Technology (NIST), the average weight of a single grain of salt is about 58.5 milligrams or 0.0585 grams.\n\nSecondly, we need to know the molar mass of salt. Salt is actually a compound made up of two elements, sodium (Na) and chlorine (Cl), with the chemical formula NaCl. The molar mass of NaCl is 58.44 grams per mole, which means that one mole of salt contains 6.022 x 10^23 individual NaCl molecules.\n\nTo find out how many atoms are in a single grain of salt, we need to use Avogadro's number, which is the number of particles in one mole of a substance. Avogadro's number is approximately 6.022 x 10^23 atoms/molecules per mole.\n\nSo, if we take the mass of a single grain of salt (0.0585 grams) and divide it by the molar mass of NaCl (58.44 grams/mol), we can calculate the number of moles of salt in a grain:\n\n0.0585 g / 58.44 g/mol = 0.000999 moles\n\nThen, if we multiply this by Avogadro's number, we get the number of molecules (or atoms) of NaCl in a single grain of salt:\n\n0.000999 moles x 6.022 x 10^23 molecules/mole = 6.008 x 10^20 molecules\n\nTherefore, there are approximately 6 x 10^20 atoms of salt in a single grain of salt.\n\nIt's important to note that this is an approximation, and the actual number of atoms in a grain of salt may vary slightly depending on the size of the grain and the purity of the salt. Nevertheless, this calculation gives us a general idea of the incredible number of atoms that make up even the smallest visible piece of matter.", "metadata": {}}
-{"answer_id": "ahktv9NqxZ2cYquTXwF42r", "model_id": "gpt-3.5-turbo:20230327", "question_id": 43, "text": "According to research, it is estimated that there are approximately 8.6 million lightning strikes that occur on Earth each day. Here is how I reached this conclusion:\n\nFirst, it is important to understand what creates lightning. Lightning is produced in thunderstorms when the atmosphere becomes electrically charged. This is typically caused by the interaction between warm and cold air. When these two air masses meet and create instability in the atmosphere, it can cause lightning to occur.\n\nNext, it's worth noting that thunderstorms are quite common across the globe, with an estimated 16 million thunderstorms happening each year. This means that there is a significant potential for lightning strikes to occur during these storms.\n\nTo estimate the number of lightning strikes that occur each day, we can take the total number of yearly thunderstorms (16 million) and divide it by the number of days in a year (365). This calculation gives us an average of around 43,835 thunderstorms per day.\n\nNow, not all thunderstorms will produce lightning, but many of them do. Based on data collected from a study conducted by the World Meteorological Organization (WMO), about 45% of all thunderstorms produce lightning. So, if we multiply the number of thunderstorms per day (43,835) by the percentage that produce lightning (45%), we get approximately 19,726 thunderstorms with lightning each day.\n\nFinally, we need to estimate how many strikes occur within each thunderstorm. It is difficult to determine an exact number since some thunderstorms produce only a few strikes while others produce hundreds or even thousands. However, according to National Geographic, the average number of lightning strikes per thunderstorm is around 100. Multiplying this by the estimated number of thunderstorms per day that produce lightning (19,726) gives us approximately 1,972,600 lightning strikes per day.\n\nThat being said, it is important to note that some estimates put the number of daily lightning strikes anywhere from 6 million to 100 million. The exact number varies depending on the source and the methods used to calculate it. Nonetheless, it is clear that lightning strikes occur frequently and can be quite dangerous.", "metadata": {}}
-{"answer_id": "kqqPRaFqb3w9Ky9LGB3yKU", "model_id": "gpt-3.5-turbo:20230327", "question_id": 44, "text": "To calculate the number of balloons needed to lift a house like in the movie \"Up,\" we need to consider the weight of the house and the lifting capacity of a single balloon.\n\nFirst, let us consider the weight of the house. In the movie, the house is depicted as a small wooden house with a chimney and a porch. Based on similar houses, we can estimate that the weight of the house would be around 4-5 tons (8,000-10,000 pounds).\n\nNow, let us consider the lifting capacity of a single balloon. A typical helium-filled party balloon can lift a maximum of approximately 14 grams (0.5 ounces) of weight. However, in the movie, the balloons were much larger and had a greater lifting capacity. Assuming a lifting capacity of 1 kilogram (2.2 pounds) per balloon, we can calculate the number of balloons needed to lift the house.\n\nTo lift a 5-ton house, we would need 5,000 kilograms (11,000 pounds) of lifting capacity. Dividing this by the lifting capacity of a single balloon (1 kilogram), we get a total of 5,000 balloons needed to lift the house.\n\nHowever, this calculation does not take into account the weight of the balloon material, ropes, and other equipment needed to tether the balloons to the house. Additionally, the balloons would need to be organized and arranged in a specific way to lift the house evenly and prevent it from tipping over.\n\nTherefore, while 5,000 balloons may be needed in theory, it is unlikely that this number of balloons alone would be able to safely lift a house. The scenario depicted in the movie is purely fictional and should not be duplicated in real life.", "metadata": {}}
-{"answer_id": "946tQg8kS7GYPSm4qcV6Pt", "model_id": "gpt-3.5-turbo:20230327", "question_id": 45, "text": "According to the latest statistics, more than 18.7 billion text messages are sent globally every day. This means that approximately 781 million text messages are sent every hour. To calculate how many text messages are sent in a minute, we need to divide these numbers by 60 (the number of minutes in an hour).\n\nSo, 781 million text messages divided by 60 minutes yields approximately 13,017,543 text messages sent globally in a minute. It's important to note that this number is an estimate and may vary depending on the time of day, day of the week, and other factors. Additionally, the rapid growth of instant messaging apps may be impacting the number of traditional text messages being sent globally.", "metadata": {}}
-{"answer_id": "cU3wut3Ta3ySbRHGxfwgjc", "model_id": "gpt-3.5-turbo:20230327", "question_id": 46, "text": "Estimating the number of words spoken daily on Earth can be a daunting task, as it involves taking into account a wide range of factors such as population, language, age, location, and more. However, there are a few ways to approach this question that can give us a rough estimate. \n\nOne approach is to start by looking at the world population, which as of 2021 is approximately 7.9 billion people. Assuming that each person speaks on average 2,000 words a day (which is a reasonable estimate for adults who engage in regular conversation), we can begin to calculate the daily volume of words spoken on Earth. \n\nTo do this, we can use the following formula: \n\nTotal words spoken daily = (World population) x (Average daily words spoken per person) \n\nPlugging in the values, we get: \n\nTotal words spoken daily = (7.9 billion) x (2,000) \n\nTotal words spoken daily = 15.8 trillion \n\nSo according to this estimate, there are roughly 15.8 trillion words spoken on Earth each day. However, it's worth noting that this number may be significantly higher or lower depending on a variety of factors. For example, not everyone speaks the same number of words per day, and some people may not have access to language or communication tools.\n\nAdditionally, it's important to keep in mind that this estimate is just that - an estimate. There is no definitive answer to how many words are spoken daily on Earth, and the true number is likely impossible to determine with complete accuracy. Nonetheless, by taking a logical approach and making reasonable assumptions, we can arrive at a rough estimate that gives us a sense of the massive scale of human communication.", "metadata": {}}
-{"answer_id": "hQP784Ch2yq2b3BaXVBVX3", "model_id": "gpt-3.5-turbo:20230327", "question_id": 47, "text": "It's difficult to determine the exact number of snowflakes that fall during a typical winter, as it depends on a variety of factors such as location, temperature, humidity, and duration of the winter season.\n\nFirstly, it's important to consider the size of snowflakes. Snowflakes come in different shapes and sizes, ranging from small crystals to larger, more intricate structures. Generally, the smaller the snowflake, the more there are in a given amount of precipitation.\n\nSecondly, it's also worth noting that snowfall is typically measured in inches, rather than the number of snowflakes. This is because the exact number of snowflakes that contribute to a certain amount of precipitation can vary depending on the size and shape of the snowflakes.\n\nWith these factors in mind, we can estimate the number of snowflakes that fall during a typical winter by looking at an average snowfall amount. According to the National Oceanic and Atmospheric Administration (NOAA), the average snowfall in the United States is around 28 inches per year.\n\nAssuming that each inch of snowfall contains anywhere from hundreds to thousands of individual snowflakes, we can estimate that there are millions (if not billions) of snowflakes that fall during a typical winter.\n\nOverall, while we can't pinpoint an exact number of snowflakes that fall during a typical winter, we can estimate that it is likely to be a very large number due to the variability in size and shape of snowflakes, as well as the amount of snowfall that typically occurs.", "metadata": {}}
-{"answer_id": "a92bStUFdq4LBcv3pa9y3Z", "model_id": "gpt-3.5-turbo:20230327", "question_id": 48, "text": "Determining the total number of pages in all the books ever written would be an impossible task as it's practically impossible to know the exact number of books that have been written throughout human history. However, we can make an attempt to estimate the total number of pages based on some assumptions and facts.\n\nFirst, we need to estimate the total number of books ever written. According to Google's estimation in 2010, there were around 130 million books in the world at that time. However, this is just an educated guess, and the actual number of books is likely to be much higher.\n\nNext, we need to consider the average number of pages in a book. This can vary widely depending on the genre, audience, and author's writing style. Some books may have only a few pages, while others may have hundreds or even thousands of pages. However, according to Book Authority, the average number of pages in a book is around 320 pages.\n\nWith these assumptions, we can estimate the total number of pages in all the books ever written by multiplying the estimated number of books with the average number of pages. So, if we assume there were around 500 million books ever written and each book had 320 pages on average, then the total number of pages in all books ever written would be:\n\n500,000,000 x 320 = 160 billion pages\n\nHowever, it's important to recognize that this is just an estimation and the actual number is likely much higher or lower. Additionally, new books are being written and published every day, so this estimation would become outdated very quickly.", "metadata": {}}
-{"answer_id": "a2QAcAm9wJeP2BpyWQnhot", "model_id": "gpt-3.5-turbo:20230327", "question_id": 49, "text": "The beginning of life on Earth is estimated to have occurred around 3.8 billion years ago. The question asks how many times the Earth has orbited the Sun since that time, so we need to calculate the number of orbits the Earth has completed over that time period. \n\nOne orbit of the Earth around the Sun takes approximately 365.25 days, or one year. To calculate how many orbits the Earth has completed over 3.8 billion years, we first need to convert this amount of time into years. \n\n3.8 billion years is equivalent to 3,800,000,000 years. We can use this number and the length of one Earth year to calculate the number of orbits:\n\n3,800,000,000 years / 1 year = 3,800,000,000 orbits\n\nSo, the Earth has completed approximately 3.8 billion orbits around the Sun since the beginning of life on Earth. \n\nIt's worth noting that this is a rough estimate, as the length of a year and the Earth's orbit have both changed slightly over time due to factors like gravitational interactions with other planets and the Moon. However, this answer provides a reasonable approximation of the number of orbits that have occurred over the scale of billions of years.", "metadata": {}}
-{"answer_id": "CrmusnxbTtGXF2varfcUd4", "model_id": "gpt-3.5-turbo:20230327", "question_id": 50, "text": "It is impossible to determine the exact number of songs that have been recorded throughout history because new songs are being created on a daily basis, and many ancient songs have been lost over time. However, we can make some educated estimates based on available data.\n\nFirst, we must consider the length of human history. Humans have been creating music for tens of thousands of years, with the oldest known musical instrument, a bone flute, dating back to about 40,000 years ago. As such, it is safe to assume that countless songs have been created and lost over that vast length of time.\n\nNext, let's consider modern recording technology. The first sound recording was made in 1857, and since then, millions of songs have been recorded. In fact, the Library of Congress in the USA has more than 3 million sound recordings in its collection alone.\n\nHowever, even that number is likely to be a vast underestimate since it\u2019s impossible to account for music recordings that existed in cultures without a writing system, or those outside the major recording and distribution systems of the Western music industry.\n\nFinally, we must recognize that music is created in every culture around the world, and each culture has its unique musical traditions. As a result, it's impossible to estimate the number of songs created around the globe in different languages and styles.\n\nIn conclusion, although it's impossible to determine the exact number of songs recorded throughout history, we can conclusively say that it's likely in the billions, considering the long history of music, the vast amount of music recorded in recent centuries and the diversity of musical traditions around the world.", "metadata": {}}
-{"answer_id": "J9pZp6z2UUW7YcXgzUouqs", "model_id": "gpt-3.5-turbo:20230327", "question_id": 51, "text": "If the internet had been invented during the Renaissance period, it would have revolutionized the way in which people gained knowledge, conducted research, and shared ideas. However, there would have likely been many limitations of the technology, such as the lack of accessibility and the lack of infrastructure to support such a vast network.\n\nOne possibility is that the internet may have been utilized by Renaissance scholars for intellectual exchange, much like how they used letters to communicate and engage in debates. This would have allowed for a more extensive and rapid dissemination of ideas, making it easier for artists, philosophers, theologians, and scientists to learn from one another and work collaboratively.\n\nAdditionally, the invention of the printing press during the Renaissance period made books and information available to a broader audience. If the internet had also been invented during this time, it may have played a similar role in democratizing knowledge and making it more widely accessible.\n\nOverall, the invention of the internet during the Renaissance period would have had a profound impact on the intellectual and cultural landscape of the time, but the technology may have been limited in scope and reach given the technological limitations of the era.", "metadata": {}}
-{"answer_id": "67bYUQb6zru8ofiub7uNUi", "model_id": "gpt-3.5-turbo:20230327", "question_id": 52, "text": "If the Aztecs had successfully repelled the Spanish conquistadors, the history of the Americas would have been different. The Spanish colonization of Mexico would not have taken place, and the Aztecs would have continued to control their empire. It is possible that the Aztecs would have continued to expand their empire and develop their civilization. \n\nThe Spanish conquest of Mexico had a significant impact not only on the Aztec people, but also on the indigenous people of the Americas. The conquest brought with it diseases that decimated the population, and the Spanish imposed their culture and religion on the peoples they conquered. If the Spanish had been repelled, the histories and cultures of the Americas would have been vastly different.\n\nIt is also possible that other European nations, such as the British or the French, would have attempted to colonize the Americas. However, without the riches of the Aztec empire to lure them in, the colonial powers may not have found the Americas as attractive. \n\nOverall, the successful repulsion of the Spanish conquistadors by the Aztecs would have changed the course of history for the Americas, Europe, and the world.", "metadata": {}}
-{"answer_id": "gAisnQTHWFLW8aa5fQPNJf", "model_id": "gpt-3.5-turbo:20230327", "question_id": 53, "text": "If the Black Death had not occurred in the 14th century, the course of history would have drastically changed. The Black Death was one of the deadliest pandemics in human history, which killed almost 50 million people in Europe alone. Its social, economic, and political impact was enormous, and it changed the entire social structure of Europe forever.\n\nIf the Black Death had not occurred, many of the changes that were brought about by the pandemic would not have occurred. For example, the social hierarchy would have remained the same, and peasants would not have had as many economic opportunities as they did after the Black Death, which caused a shortage in labor.\n\nFurthermore, the medical field would not have progressed as quickly, since the Black Death brought about many advancements in medicine as people attempted to find a cure. Many of the basic hygiene practices that we take for granted today were also developed in response to the Black Death, so if the pandemic had not occurred, the general health and cleanliness of the population would not have improved as much.\n\nFinally, the cultural landscape would have been different as well, as the Black Death had a profound effect on art, literature, and music. If it had not occurred, we may have different artistic and cultural movements today.\n\nIn summary, if the Black Death had not occurred in the 14th century, the world would be drastically different today.", "metadata": {}}
-{"answer_id": "4ZJCbj7T8BGzNhDqz7NSF4", "model_id": "gpt-3.5-turbo:20230327", "question_id": 54, "text": "If Isaac Newton had focused on biology instead of physics, the scientific world may be vastly different today. His advancements in mathematics, such as calculus, would have certainly been useful in the field of biology. Newton's curiosity, analytical mind, and dedication to scientific inquiry would have been equally effective in biology as they were in physics. He may have made significant contributions to the field of genetics or evolutionary theory.\n\nOn the other hand, his contributions to physics were so groundbreaking that the subject may have been hindered without his contributions. It's difficult to say exactly what would have happened, but it's likely that the course of scientific history would have been different.", "metadata": {}}
-{"answer_id": "c6ixri3qqLfSBBnwMkgYB7", "model_id": "gpt-3.5-turbo:20230327", "question_id": 55, "text": "If the Beatles had never formed as a band, the world of music would be very different. The Beatles revolutionized popular music and influenced countless musicians and bands that came after them. Without the Beatles, it's possible that other bands would still have risen to fame and changed the course of music history, but we can only speculate how their absence would have affected the genre overall. It's also possible that a different band or artist would have taken the world by storm and become an equally important cultural icon. Ultimately, the impact of the Beatles on music and society is immeasurable, and their influence will continue to be felt for generations to come.", "metadata": {}}
-{"answer_id": "c9AtDn7eeSYhtH854MQDDB", "model_id": "gpt-3.5-turbo:20230327", "question_id": 56, "text": "If Alan Turing had not cracked the Enigma code during World War II, the outcome of the war could have been very different. The Enigma code was a secret code used by the German military to encrypt their messages during the war. By cracking this code, Turing and his team were able to intercept and decode secret German messages, thus giving the Allies valuable intelligence that helped them win key battles.\n\nIf Turing had not been able to crack the Enigma code, the Allies might have struggled to gain the intelligence they needed to win key battles, which could have led to a longer and more bloody war. It's possible that the war could have dragged on for many more years, resulting in even more casualties and devastation.\n\nAdditionally, Turing's work on the Enigma code laid the foundation for modern computing and artificial intelligence. Without his insights and innovations, the development of computer technology might have been delayed, which could have had significant implications for technological progress and innovation in the aftermath of the war.", "metadata": {}}
-{"answer_id": "jYd2gg6MJH8hdqFSAJTaiR", "model_id": "gpt-3.5-turbo:20230327", "question_id": 57, "text": "If the Suez Canal had never been constructed, the shipping routes between Europe and Asia would have been significantly longer and more treacherous. Ships would have to sail around the southern tip of Africa, adding thousands of miles to their journey and increasing the risk of piracy and other hazards. This would have made international trade more expensive and less efficient, and could have slowed the economic growth of many nations. Additionally, the region around the canal would have developed differently, as the canal has helped create cities and infrastructure that rely on its presence. Without the canal, the social, political, and economic impact on the Middle East could have been much different.", "metadata": {}}
-{"answer_id": "nZJ6LGJFegnHetutiAQtFm", "model_id": "gpt-3.5-turbo:20230327", "question_id": 58, "text": "If the Maya civilization had never collapsed, it's possible that they would have continued flourishing and developing their culture, art, architecture, and knowledge. The Maya civilization was known for impressive works of art, architecture, and a sophisticated calendar system. \n\nWithout their collapse, it is possible that the Maya could have advanced their knowledge in other areas, such as science, language, or technology. They might have developed new forms of governance and political systems, which could have influenced the political structures of neighboring civilizations.\n\nAdditionally, their continued presence could have had an impact on the colonization of the Americas. Because the Maya were one of the more advanced civilizations in the region at the time of the European invasion, their presence could have impacted the way the Spaniards and other European powers approached colonization.\n\nOverall, it's hard to say exactly what would have happened, but the Maya civilization was undeniably a significant cultural and scientific force in history. Had they not mysteriously disappeared, they could have continued to shape the course of history in ways we can only imagine.", "metadata": {}}
-{"answer_id": "dmEgLyeYNcwBZWHBak6Lap", "model_id": "gpt-3.5-turbo:20230327", "question_id": 59, "text": "If Christopher Columbus had not discovered the Americas, it is likely that another explorer would have eventually stumbled upon the continent. The Vikings had already established a presence in North America around 500 years before Columbus arrived. Additionally, it is possible that indigenous peoples from Asia may have eventually migrated to the Americas via the Bering Strait land bridge.\n\nHowever, the timing of European colonization and the impact it had on the native populations may have been dramatically different without Columbus\u2019 arrival. It is possible that the Americas could have remained isolated from European influence for a longer period of time, allowing the native cultures to develop more independently. Alternatively, other European nations may have eventually colonized the Americas without the influence of Columbus and brought their own unique cultures and traditions to the region.", "metadata": {}}
-{"answer_id": "bkuECkuy7YiDUcj9oJjdrZ", "model_id": "gpt-3.5-turbo:20230327", "question_id": 60, "text": "If Vincent van Gogh had been a successful artist during his lifetime, his life would have been very different. He struggled with poverty, mental illness, and feelings of isolation throughout his life. If he had been financially successful, he would not have had to rely on his brother Theo for financial support and may have been able to afford better care for his mental health.\n\nAdditionally, van Gogh was deeply committed to his art and was mostly ignored by the art world during his lifetime. If he had been successful, he may have found greater satisfaction in his work and felt more validated for his contributions to the art world. He may have had the opportunity to travel more widely, meet more artists, and produce more work.\n\nOn a broader level, if Vincent van Gogh had been successful during his lifetime, it may have changed the way we think about art history. His work was groundbreaking in its use of color, brushwork, and emotional intensity, and it's possible that he could have influenced other artists of his time (and beyond) in more direct ways if he had been a well-known figure in the art world.", "metadata": {}}
-{"answer_id": "2PRjT7j3V6487xZREfQfuD", "model_id": "gpt-3.5-turbo:20230327", "question_id": 61, "text": "Here's a sample program that demonstrates your requirements:\n\n```c++\n#include <iostream>\n#include <fstream>\n#include <string>\n#include <sstream>\n#include <algorithm>\n\nusing namespace std;\n\nint count_occurrences(const string &filename, const string &search_word) {\n    int count = 0;\n    ifstream fin(filename.c_str());\n    if (fin.fail()) {\n        cerr << \"Failed to open file \" << filename << endl;\n        return 0;\n    }\n    string line;\n    while (getline(fin, line)) {\n        transform(line.begin(), line.end(), line.begin(), [](unsigned char c){ return tolower(c); }); // convert to lowercase\n        stringstream s(line);\n        string word;\n        while (s >> word) {\n            if (word == search_word) {\n                count++;\n            }\n        }\n    }\n    fin.close();\n    return count;\n}\n\nint main() {\n    string filename = \"sample.txt\";\n    string search_word = \"hello\";\n    int count = count_occurrences(filename, search_word);\n    cout << \"The word \\\"\" << search_word << \"\\\" occurs \" << count << \" times in file \" << filename << endl;\n    return 0;\n}\n```\n\nHere's how it works:\n\n1. We define a function called `count_occurrences()` that takes two arguments: `filename` and `search_word`.\n2. Inside the `count_occurrences()` function, we first open the file using `ifstream`.\n\n3. If the file fails to open, we simply return 0.\n\n4. We then loop through each line of the file using `getline()` and store it in the variable `line`.\n\n5. We convert each line to lowercase using `transform()` and a lambda function.\n\n6. We then create a `stringstream` object called `s` and pass the line to it.\n\n7. We then loop through each word in the line using `>>` and store it in the variable `word`.\n\n8. If the `word` matches the `search_word`, we increment the `count` variable.\n\n9. After the loop is complete, we close the file using `close()` and return the `count`.\n\n10. In the `main()` function, we call the `count_occurrences()` function with the file name and search word.\n\n11. We then output the result to the console.\n\nI hope this helps! Let me know if you have any further questions.", "metadata": {}}
-{"answer_id": "jWyN8NTdVix6CUoqfbRqVx", "model_id": "gpt-3.5-turbo:20230327", "question_id": 62, "text": "Sure! Here's a Python implementation using dynamic programming.\n\n```python\ndef lcs(str1, str2):\n    # Initialize a 2D array to store the lengths of LCS\n    m = [[0] * (len(str2) + 1) for i in range(len(str1) + 1)]\n    \n    # Fill the 2D array by comparing characters of both strings\n    for i in range(1, len(str1) + 1):\n        for j in range(1, len(str2) + 1):\n            if str1[i-1] == str2[j-1]:\n                m[i][j] = m[i-1][j-1] + 1\n            else:\n                m[i][j] = max(m[i-1][j], m[i][j-1])\n    \n    # Construct the LCS by backtracking the 2D array\n    lcs = \"\"\n    i, j = len(str1), len(str2)\n    while i > 0 and j > 0:\n        if str1[i-1] == str2[j-1]:\n            lcs = str1[i-1] + lcs\n            i -= 1\n            j -= 1\n        elif m[i-1][j] > m[i][j-1]:\n            i -= 1\n        else:\n            j -= 1\n    \n    return lcs\n```\n\nThis function takes two input strings as parameters and returns the longest common subsequence as output. It first initializes a 2D array 'm' with all elements set to zero. This array is used to store the lengths of the longest common subsequences of all possible substrings of the input strings. The function then fills up the 2D array by comparing characters of both strings.\n\nAfter the 2D array is filled, the function backtracks it to construct the longest common subsequence. It starts from the bottom right corner of the array and moves up and left until it reaches the top left corner. During this process, it compares the current character of both strings and moves to the next cell in a way that maximizes the length of the LCS. Once the top left corner is reached, the constructed subsequence is returned as output.", "metadata": {}}
-{"answer_id": "mx9G7gfKTCXCmNRaiMZQr9", "model_id": "gpt-3.5-turbo:20230327", "question_id": 63, "text": "Here's a regular expression pattern in Python that can be used to validate an email address:\n\n```\nimport re\n\npattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'\n\ndef validate_email(email):\n    if re.match(pattern, email):\n        print(\"Valid email address\")\n    else:\n        print(\"Invalid email address\")\n        \nvalidate_email(\"example@example.com\")\n```\n\nThis regular expression pattern checks if the email address has a valid format. It ensures that the email address starts with one or more alphanumeric characters, dots, underscores, percentage signs, plus or minus signs. The \"@' character follows this which is then followed by a valid domain name that contains only alphanumeric characters, dots, and hyphens, ending with a valid top-level domain name that contains only alphabetical characters with at least two characters.", "metadata": {}}
-{"answer_id": "8LjpXk6Va5L2FBEwTU9YJ6", "model_id": "gpt-3.5-turbo:20230327", "question_id": 64, "text": "Certainly! Here's an example Python program using dynamic programming to find the nth Fibonacci number:\n\n```\ndef fibonacci(n):\n    fib = [0, 1]\n    for i in range(2, n + 1):\n        fib.append(fib[i - 1] + fib[i - 2])\n    return fib[n]\n\n# Example usage:\nprint(fibonacci(5))  # 5\nprint(fibonacci(10))  # 55\n```\n\nThe function `fibonacci(n)` takes an integer argument `n` and returns the nth Fibonacci number. The program uses dynamic programming by keeping track of the previous two Fibonacci numbers in a list called `fib`.\n\nThe program initializes `fib` to `[0, 1]`, which are the first two Fibonacci numbers. Then, it iterates from 2 to `n` and calculates each Fibonacci number as the sum of the previous two numbers. Finally, the function returns the nth Fibonacci number in the list `fib`.\n\nHope this helps! Let me know if you have any questions.", "metadata": {}}
-{"answer_id": "BLC8fhAUeahFfrjBRiL9tx", "model_id": "gpt-3.5-turbo:20230327", "question_id": 65, "text": "Sure! Here is an implementation of the binary search algorithm in Python:\n\n```\ndef binary_search(arr, target):\n    left = 0\n    right = len(arr) - 1\n    while left <= right:\n        mid = (left + right) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            left = mid + 1\n        else:\n            right = mid - 1\n    return -1\n```\n\nTo use this function, pass in a sorted array and the target element you are searching for. The function returns the index of the target element in the array (if it exists), or -1 if it does not. \n\nHere is an example usage:\n\n```\narr = [1, 2, 3, 4, 5, 6, 7, 8, 9]\ntarget = 5\n\nindex = binary_search(arr, target)\n\nif index != -1:\n    print(f\"Found {target} at index {index}\")\nelse:\n    print(f\"{target} not found in the array\")\n```\n\nThis would output \"Found 5 at index 4\".", "metadata": {}}
-{"answer_id": "NAH245JGvMWF24FWZJagYp", "model_id": "gpt-3.5-turbo:20230327", "question_id": 66, "text": "Here's an example implementation of a queue using two stacks in Python:\n\n```python\nclass QueueUsingStacks:\n    def __init__(self):\n        self.stack1 = []\n        self.stack2 = []\n\n    def enqueue(self, value):\n        # Push the element onto the first stack\n        self.stack1.append(value)\n\n    def dequeue(self):\n        if not self.stack2:\n            # If the second stack is empty, transfer the elements from the first stack to the second stack\n            while self.stack1:\n                self.stack2.append(self.stack1.pop())\n\n        if not self.stack2:\n            # If both stacks are empty, return None\n            return None\n        else:\n            # Pop the top element from the second stack and return it\n            return self.stack2.pop()\n\n    def is_empty(self):\n        return not bool(self.stack1 or self.stack2)\n\n    def size(self):\n        return len(self.stack1) + len(self.stack2)\n```\n\nTo use this implementation, simply create a new instance of the `QueueUsingStacks` class and call the appropriate methods as shown below:\n\n```python\n# Create a new queue using two stacks\nq = QueueUsingStacks()\n\n# Enqueue some elements onto the queue\nq.enqueue(10)\nq.enqueue(20)\nq.enqueue(30)\n\n# Dequeue an element from the queue\nx = q.dequeue()\nprint(x)  # Output: 10\n\n# Check if the queue is empty\nprint(q.is_empty())  # Output: False\n\n# Get the size of the queue\nprint(q.size())  # Output: 2\n```", "metadata": {}}
-{"answer_id": "NKXTwB3WtfFcmQZbTxBP5o", "model_id": "gpt-3.5-turbo:20230327", "question_id": 67, "text": "Here's a code snippet that can find the common elements in two arrays:\n\n```python\ndef find_common_elements(arr1, arr2):\n    common = []\n    for i in range(len(arr1)):\n        for j in range(len(arr2)):\n            if arr1[i] == arr2[j]:\n                if arr1[i] not in common:\n                    common.append(arr1[i])\n    return common\n```\n\nIn this code, we use two nested loops to iterate over both arrays and compare their elements. If a common element is found, we add it to a list called 'common'. To avoid duplicates, we check whether the element is already in the 'common' list before appending it.\n\nWe can test this function with some example arrays:\n\n```python\narr1 = [1, 2, 3, 4, 5]\narr2 = [3, 4, 5, 6, 7]\nresult = find_common_elements(arr1, arr2)\nprint(result) # Output: [3, 4, 5]\n```\n\nIn this example, the common elements are 3, 4, and 5.", "metadata": {}}
-{"answer_id": "5P7wPyrNwBrHNdJNrETS5h", "model_id": "gpt-3.5-turbo:20230327", "question_id": 68, "text": "To find the value of f(2), we simply substitute 2 in place of x in the given equation of f(x):\n\nf(2) = 5(2)^3 - 2(2) + 3\n\nf(2) = 5(8) - 4 + 3\n\nf(2) = 40 - 1\n\nTherefore, the value of f(2) is 39.", "metadata": {}}
-{"answer_id": "UmnL4WoucBduFpX4jptkNU", "model_id": "gpt-3.5-turbo:20230327", "question_id": 69, "text": "3x + 10 = 5(x - 2)\n\n3x + 10 = 5x - 10  (use the distributive property)\n\n10 + 10 = 5x - 3x  (subtract 3x from both sides)\n\n20 = 2x (simplify)\n\nx = 10 (divide both sides by 2) \n\nTherefore, x = 10.", "metadata": {}}
-{"answer_id": "Hymm673GkY5tXNaTFeNUBZ", "model_id": "gpt-3.5-turbo:20230327", "question_id": 70, "text": "To find the length of a line segment with two endpoints (x1, y1) and (x2, y2), we use the distance formula:\n\ndistance = sqrt((x2 - x1)^2 + (y2 - y1)^2)\n\nPlugging in the coordinates of the given endpoints, we get:\n\ndistance = sqrt((10 - 2)^2 + (4 - (-2))^2)\ndistance = sqrt(8^2 + 6^2)\ndistance = sqrt(64 + 36)\ndistance = sqrt(100)\ndistance = 10\n\nTherefore, the length of the line segment is 10 units.", "metadata": {}}
-{"answer_id": "PUzddJ35E3WsM7BDQ9E59i", "model_id": "gpt-3.5-turbo:20230327", "question_id": 71, "text": "Sure, I can help you write a formal email to a potential business partner proposing a joint venture. Here's a sample email you can use as a guide:\n\nDear [Name],\n\nI hope this email finds you well. I am writing to you today to propose a joint venture that I believe could be mutually beneficial for our businesses.\n\nAs you may know, our companies share many complementary strengths and resources that could be leveraged to achieve greater success in our respective markets. I believe that by working together, we could create a competitive advantage that would benefit both of us.\n\nI would like to propose that we explore the possibility of forming a joint venture in [industry or field], with the aim of combining our expertise and resources to create something innovative and valuable for the market.\n\nI believe that our companies' experiences and capabilities could form a strong foundation for a mutually beneficial partnership. If you are interested in pursuing this proposal, please let me know and we can discuss the details further.\n\nThank you for your time and consideration, and I look forward to hearing back from you.\n\nBest regards,\n\n[Your Name]", "metadata": {}}
-{"answer_id": "6Q72hZCtDkgq379yhdmN5N", "model_id": "gpt-3.5-turbo:20230327", "question_id": 72, "text": "Dear [Employer\u2019s Name],\n\nThis letter serves as my formal resignation from my position as [Position Name] at [Company Name]. My last day of work will be on [Date].\n\nI would like to express my gratitude for the opportunities provided during my time at [Company Name]. It has been a pleasure to be part of such a dynamic and forward-thinking organization. My tenure here has contributed significantly to my professional growth, and it is with a heavy heart that I must resign.\n\nI appreciate your constant support, guidance and collaboration throughout my employment here. I have learned a lot under your leadership and found my experience here to be fulfilling in ways that I could not have anticipated.\n\nIf there is anything that I can do to ensure a seamless transition for my successor and facilitate the smooth conclusion of my work, please don't hesitate to let me know. I am happy to complete any outstanding tasks or projects before I leave.\n\nThank you again for all of your support and understanding. I wish the company continued success, and I hope that our paths may cross again in the future.\n\nSincerely,\n\n[Your Name]", "metadata": {}}
-{"answer_id": "ReXnHy9C8SwcYPAep6gvJg", "model_id": "gpt-3.5-turbo:20230327", "question_id": 73, "text": "[Your Name]\n[Your Designation]\n[Your Institution]\n[Your Address]\n[City, State Zip Code]\n[Your Email]\n[Date]\n\n[Admission Committee]\n[Graduate Program in Computer Science]\n[University Name]\n[Address]\n[City, State ZIP Code]\n\nDear Members of the Admission Committee,\n\nI am writing to recommend [Student's Name] for admission to the graduate program in Computer Science at your esteemed institution. I have had the privilege to teach [him/her] in several courses during [his/her] undergraduate studies in [program name] at [university name], and I highly recommend [him/her] for [his/her] outstanding academic performance and natural ability in the field of computer science.\n\nDuring [his/her] time at [university name], [Student's Name] proved to be a highly motivated and bright student, consistently demonstrating academic excellence in all courses. [He/She] consistently achieved top grades and took on challenging coursework with enthusiasm and diligence. [His/Her] strong analytical and critical thinking skills, coupled with [his/her] passion for technology and computer science, make [him/her] a perfect candidate for the esteemed computer science program at your institution.\n\n[Student's Name] was not only a dedicated student but also an active participant in many extra-curricular activities, such as [list activities]. [He/She] was always eager to explore new concepts and technologies beyond the coursework requirements, and [his/her] enthusiasm to learn and work in a team was evident in [his/her] participation in group projects and research work.\n\nIn conclusion, I have a strong belief that [Student's name] has an incredible potential for achieving academic excellence in your prestigious program. [His/Her] exceptional academic record, strong work ethic, and impressive achievements make [him/her] an ideal candidate for the graduate program in Computer Science at your institution. I, therefore, strongly recommend [Student's Name] for admission to the program and look forward to seeing [him/her] contributing to the field of computer science significantly.\n\nPlease do not hesitate to contact me if you require any further information regarding this recommendation.\n\nSincerely,\n\n[Your Name]", "metadata": {}}
-{"answer_id": "cKk5zZe8yYY4JH3kr5pGXG", "model_id": "gpt-3.5-turbo:20230327", "question_id": 74, "text": "Subject Line: Introducing our revolutionary new software solution\n\nDear valued customers,\n\nWe are thrilled to announce the launch of our latest innovation \u2013 a pioneering software solution that will revolutionize the way you do business. Designed with cutting-edge technology and user-friendly features, our software solution will help you streamline your workflows, increase efficiency, and ultimately boost your bottom line.\n\nHere are just a few of the ways our software solution can transform your business:\n\n- Increased collaboration: Our software solution makes it easy for your team to collaborate on tasks and projects, ensuring everyone is on the same page and nothing falls through the cracks.\n\n- Real-time tracking: With our software solution, you can track progress in real time, enabling you to make informed decisions quickly and efficiently.\n\n- Comprehensive reporting: Our software solution allows you to run detailed reports that provide valuable insights into your business operations, giving you the information you need to make informed decisions and identify areas of improvement.\n\nThese are just a handful of the benefits you can expect from our new software solution. We truly believe that our product will be a game changer for businesses of all sizes and industries.\n\nTo celebrate our launch, we are offering a special introductory offer exclusively for our customers. For a limited time, you can get access to our software solution at an incredible discounted price. Don\u2019t miss out on this amazing opportunity to take your business to the next level!\n\nTo learn more about our software solution and to take advantage of our special offer, simply click the link below:\n\n[Insert Link]\n\nThank you for choosing us as your partner in business. We look forward to helping you succeed with our new software solution.\n\nBest regards,\n\n[Your Name]", "metadata": {}}
-{"answer_id": "c5rwA3cPjytSGcn7H8dZ6Q", "model_id": "gpt-3.5-turbo:20230327", "question_id": 75, "text": "Subject: Apology for Delay in Your Order\n\nDear [Customer Name],\n\nWe are deeply sorry to inform you that there was a delay in processing your recent order. We understand how frustrating and inconvenient it can be to wait for something that you were looking forward to receiving.\n\nWe want to express our sincere apologies and assure you that we have taken necessary steps to resolve this issue. Due to unexpected high volume of orders last week, our warehouse faced some delays in processing orders on time, and unfortunately, your order was one of the affected ones.\n\nWe have been working diligently to ensure that your order is processed and shipped as soon as possible. We have put measures in place to prevent the recurrence of any such delay in future by hiring and re-training more staff and improving our inventory management.\n\nOnce again, please accept our apologies for the inconvenience this has caused you. We value your patronage and look forward to serving you with the same level of excellence that you have come to expect from us.\n\nThank you for your patience and understanding.\n\nBest regards,\n\n[Your Name]\n[Your Store Name]", "metadata": {}}
-{"answer_id": "XZGPtBo86KfF9REZ36s2X5", "model_id": "gpt-3.5-turbo:20230327", "question_id": 76, "text": "Title: The Evolution and Cultural Significance of Jazz\n\nOpening shot of jazz musicians performing in a club\n\nNarration: Jazz has been regarded as one of the most significant cultural contributions from the African American community to the world. It has evolved over time, from its origins in New Orleans to becoming a global phenomenon.\n\nCut to a black and white photo of the original jazz musicians in New Orleans\n\nNarration: Jazz originated in the late 19th and early 20th centuries in New Orleans. It was a musical genre that blended together elements of African rhythms, blues, ragtime, and European harmonies.\n\nCut to a video of Louis Armstrong\n\nNarration: Jazz really grew in popularity in the 1920s and 1930s, particularly with the emergence of jazz legends like Louis Armstrong and Duke Ellington. Jazz was unique because it allowed for improvisation and the individual expression of musicians.\n\nCut to a video of a modern jazz ensemble\n\nNarration: Today, jazz continues to evolve in unique and exciting ways. It is celebrated as a reflection of cultural identity and a bridge between different communities.\n\nCut to a jazz festival scene\n\nNarration: Jazz festivals around the world attract thousands of music lovers annually, from the Newport Jazz Festival in America to the Montreux Jazz Festival in Switzerland.\n\nCut to a jazz club scene\n\nNarration: For many, jazz clubs remain important cultural spaces where people connect and enjoy live performances.\n\nClosing shot of jazz musicians performing\n\nNarration: Jazz music remains a constant reminder of the versatility and beauty of human creativity. Let us celebrate and continue to support the evolution of jazz for generations to come.\n\nEnd with a jazz score and video credits", "metadata": {}}
-{"answer_id": "DRncHCsdGji756efDhacUT", "model_id": "gpt-3.5-turbo:20230327", "question_id": 77, "text": "Aloha, fellow travelers! I recently had the pleasure of visiting the beautiful island of Hawaii, and it was truly an enchanting experience that left me with unforgettable memories.\n\nOne of the things that stood out to me was the rich culture of Hawaii. We got to witness an authentic hula performance right on the beach, complete with traditional costumes and music. The dancers\u2019 graceful movements were mesmerizing, and it was fascinating to learn about the history and meaning behind each dance.\n\nAnother cultural highlight for us was attending a luau. It was a true feast for the senses, with an abundance of delicious food, music, and dance performances. We got to try traditional Hawaiian dishes like kalua pig and poi, and we even learned some moves during a group hula lesson.\n\nOf course, no trip to Hawaii would be complete without experiencing its breathtaking natural beauty. We hiked to the top of a volcanic crater in Maui, and the view from above was simply stunning. The colors of the rocks and the ocean were like nothing we had ever seen before. We also visited Hawaii Volcanoes National Park on the Big Island, which was equally jaw-dropping. Seeing the active lava flow and the steam coming out of the ground made us feel like we were on another planet.\n\nAnother must-see attraction in Hawaii is Waikiki Beach. The iconic beach is famous for its crystal clear waters and white sand, surrounded by towering hotels and restaurants. We spent a morning there, soaking up the sunshine and enjoying the soothing sound of the waves.\n\nOverall, Hawaii truly exceeded our expectations. The combination of stunning natural beauty and rich cultural experiences made for a perfect vacation. We left feeling refreshed and inspired, already dreaming of our next trip back to this tropical paradise. Mahalo for reading, and until next time!", "metadata": {}}
-{"answer_id": "Y5rCQHHDA6WNfhRcB6QboG", "model_id": "gpt-3.5-turbo:20230327", "question_id": 78, "text": "If you're looking for an exciting and visually stunning science fiction adventure, look no further than the recently released film \"Space Odyssey.\" This film is an absolute triumph, featuring incredible special effects and a compelling storyline that will keep you on the edge of your seat.\n\nThe plot of \"Space Odyssey\" revolves around a team of astronauts who embark on a perilous mission to explore a distant planet. As they venture further and further into the unknown, they encounter all manner of dangers and obstacles, ranging from treacherous alien creatures to hostile sentient machines. Along the way, they must grapple with their own fears and inner demons, as well as the challenges of working together as a team.\n\nOne of the standout features of \"Space Odyssey\" is its cast of characters. Each member of the astronaut team is well-developed and brings their own unique perspective and personality to the story. From the stoic and disciplined captain to the brash and impulsive engineer, every character feels fully realized and relatable. As the story progresses, we see these characters grow and evolve, learning from their mistakes and coming together to face their challenges as a team.\n\nOf course, no discussion of \"Space Odyssey\" would be complete without mentioning its incredible special effects. From the stunningly rendered alien landscapes to the dizzying action scenes, every frame of this film is a visual spectacle. The attention to detail and overall sense of immersion is truly breathtaking, making you feel like you're right there alongside the astronauts as they explore the unknown.\n\nOverall, \"Space Odyssey\" is a must-see for any science fiction fan. It's a thrilling and beautifully crafted film that will keep you engaged from start to finish. With its gripping plot, well-drawn characters, and jaw-dropping special effects, it's easily one of the best sci-fi films of the year. Go see it, and prepare to be transported to another world.", "metadata": {}}
-{"answer_id": "Lea4wh5n6GsEkBnKsntN64", "model_id": "gpt-3.5-turbo:20230327", "question_id": 79, "text": "Introduction\n- Welcome listeners and introduce the topic of the podcast, the influence of streaming platforms on the music industry.\n- Briefly introduce yourself and your background and why you chose this topic.\n\nSection 1: Overview of the Music Industry\n- Provide an overview of the music industry before the rise of streaming services.\n- Discuss how record labels, physical album sales, and radio play were the primary distribution channels for music.\n\nSection 2: Streaming Platforms\n- Discuss the rise of streaming platforms like Spotify, Apple Music, and Tidal.\n- Explain how these platforms have disrupted the traditional distribution model of the music industry.\n- Discuss the benefits and disadvantages of artists distributing their music through these platforms.\n\nSection 3: Impact on Artists\n- Examine how streaming services have changed the way artists make money from their music.\n- Discuss how the shift from album sales to streaming has impacted the revenue generated by artists.\n- Discuss the challenges artists face in terms of getting their music noticed within the abundance of material on streaming platforms.\n\nSection 4: Impact on the Music Industry\n- Discuss how the rise of streaming platforms have impacted the music industry as a whole.\n- Explain how labels have had to adapt their business models in order to continue making profits.\n- Discuss how some artists have shifted their focus from album sales to live performances and touring.\n\nSection 5: The Future of Music Streaming\n- Discuss the potential future of music streaming platforms and their impact on the music industry.\n- Discuss whether or not the rise of streaming has been positive or negative for creatives in the music industry.\n- Provide some concluding thoughts and encourage listeners to share their thoughts on social media.\n\nConclusion\n- Recap the key points covered in the podcast episode.\n- Encourage listeners to continue engaging with the topic and to tune in for future episodes.", "metadata": {}}
-{"answer_id": "gdLxzcypTeuD6ToC6HWnXh", "model_id": "gpt-3.5-turbo:20230327", "question_id": 80, "text": "I'm happy to assist you in writing a symphony concert review! Here's an example review:\n\nThe recently performed symphony concert was an outstanding showcase of musical talent, leaving the audience spellbound with the performances of the orchestra. As the concert progressed, the orchestra's unmatchable skill and talent on their respective instruments were vividly evident. The pieces featured in the concert were a delightful mix of musical genres that captivated the audience throughout the performance.\n\nThe meticulous attention to detail by the orchestra was surely commendable, as the notes flowed from instrument to instrument in perfect harmony. The way each musician played their part, with utmost precision and accuracy, was a true testament to the hours of practice and effort invested in crafting each piece. The orchestra's enthusiasm, passion, and commitment to their craft were transparent in every note that they played. \n\nOne can only appreciate the level of expertise that the orchestra displayed and the amount of dedication and discipline that has gone into cultivating their skill. The open communication between the conductor and the members of the orchestra was an impressive sight, and it undoubtedly contributed to the flawless execution of the musical compositions.\n\nAs the concert progressed, it was also noticeable that the entire audience became engulfed in the music, swaying to the rhythm of the notes. It was a sight to behold, and the applauds after every piece were jaw-dropping. There was no denying that the audience was immersively engaged, enthralled by the music, and the orchestra's fantastic performance.\n\nIn conclusion, the symphony concert showcased remarkable performances by the orchestra, with the audience's captivation evident in every note that was played. The concert was a perfect reminder of the beauty of classical music and how talented musicians can enchant an audience with sheer musical perfection. It was a night to remember, and it will stay deeply etched in the memory of everyone who had the privilege of attending this fantastic symphony concert.", "metadata": {}}
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-{"answer_id": "J3UA6eGXGyFeUGqGpP3g34", "model_id": "llama-13b:v1", "question_id": 1, "text": "The following are some strategies that may help you improve your time management skills:\n1. Make a list of priorities.\n2. Create a detailed schedule that includes time for regular tasks, meetings, breaks, and appointments.\n3. Set specific goals.\n4. Use a calendar or other scheduling software.\n5. Develop an agenda for each meeting to ensure that it stays on track.\n6. Stick to your schedule and don't get distracted.\n7. Make use of your free time.\n8. Make time for exercise, socializing, and relaxing.\n9. Keep a log of your time usage.\n10. Don't get overwhelmed by what you have to do.\n11. Take care of your health.\n12. Manage stress.\n13. Get enough sleep.\n14. Strike a balance.\n15. Always be prepared.\n16. Give yourself rewards.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "jZVDNTtFnNNd6vgkGjzAGa", "model_id": "llama-13b:v1", "question_id": 2, "text": "People experience stress from time to time, and it can be caused by a wide range of factors, including work, family, friends, money, and health. Stress can be a motivating factor, but it can also be harmful. Here are some effective ways to deal with stress:\n1. Taking time to relax. It's important to take time for yourself to relax and reduce your stress. Doing so doesn't have to be expensive or time-consuming. It could simply be reading a book, taking a walk, or listening to music.\n2. Prioritizing your responsibilities. It's important to prioritize your responsibilities and focus on the things that are most important. You should also be able to say no to tasks that you don't have time for.\n3. Getting enough sleep. Getting enough sleep can be very helpful for dealing with stress. If you're not getting enough sleep, try to go to bed earlier and develop a routine.\n4. Eating a healthy diet. It's important to eat a healthy diet, which can help reduce stress and make you feel better about yourself.\n5. Exercising. Exercising is a great way to relieve stress. It can also help reduce the harmful effects of stress and improve your overall health.\n6. Learning how to manage your time. Learning how to manage your time can help you avoid unnecessary stress. You should also try to set realistic goals and not take on too much at once.\n7. Improving your social network. Having a good social network can be very helpful for dealing with stress. You should try to spend time with people that you trust and confide in.\n8. Developing a support network. A support network is a group of people who can offer you help, guidance, and support. They can be friends, family members, or even a professional therapist.\n9. Getting professional help. If you're struggling to deal with stress, it can be beneficial to get professional help. A therapist or counselor can help you to understand your stress better and develop strategies for dealing with it.\n10. Talking to someone. You can also talk to someone about how you feel and ask for their advice. It can be helpful to get a fresh perspective from someone and let things off your chest.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NbSxRXf8Yuvjr7HC6AvrPw", "model_id": "llama-13b:v1", "question_id": 3, "text": "Main differences between Python and JavaScript are:\n1. Python is an object-oriented programming language. JavaScript is a scripting language.\n2. Python is a compiled language. JavaScript is an interpreted language.\n3. Python is a general purpose programming language. JavaScript is a special purpose programming language.\n4. Python runs on the JVM in Java Applets. JavaScript runs on the browsers.\n5. Python is a powerful programming language. JavaScript is a simple programming language.\n6. Python is a good language for web development. JavaScript is a good scripting language.\n7. Python is a high level programming language. JavaScript is a low level programming language.\n8. Python is a dynamic programming language. JavaScript is a static programming language.\n9. Python is a strict programming language. JavaScript is a relaxed programming language.\n10. Python has a large standard library. JavaScript has a small standard library.\n11. Python is very stable. JavaScript has many releases.\n12. Python is a simple language. JavaScript is a complex language.\n13. Python is easy to learn. JavaScript is hard to learn.\n14. Python is a secure programming language. JavaScript is not a secure programming language.\n15. Python is a multi paradigm language. JavaScript is a single paradigm language.\n16. Python is good for data science. JavaScript is not good for data science.\n17. Python is a general purpose programming language. JavaScript is a special purpose programming language.\n18. Python is a programming language. JavaScript is a scripting language.\n19. Python is a compiled programming language. JavaScript is an interpreted programming language.\n20. Python is a strongly typed programming language. JavaScript is a weakly typed programming language.\n21. Python supports object-oriented programming. JavaScript supports prototype-based programming.\n22. Python supports type inheritance. JavaScript supports type prototyping.\n23. Python supports operator overloading. JavaScript does not support operator overloading.\n24. Python comes with a large standard library. JavaScript comes with a small standard library.\n25. Python is a very stable programming language. JavaScript is a simple programming language.\n26. Python is a powerful programming language. JavaScript is a simple programming language.\n27. Python is a high level programming language. JavaScript is a low level programming language.\n28. Python is a dynamic programming language. JavaScript is a static programming language.\n29. Python has a very active community. JavaScript has a small community.\n30. Python is a general purpose programming language. JavaScript is a special purpose programming language.\n31. Python is a multi-paradigm language. JavaScript is a single paradigm language.\n32. Python is a secure programming language. JavaScript is not a secure programming language.\n33. Python is a general purpose programming language. JavaScript is a simple programming language.\n34. Python is a scripting language. JavaScript is a compiled language.\n35. Python is a compiled language. JavaScript is a scripting language.\n36. Python is a general purpose programming language. JavaScript is a web scripting language.\n37. Python has a large standard library. JavaScript has a small standard library.\n38. Python is a very stable programming language. JavaScript is a very dynamic programming language.\n39. Python is a high level programming language. JavaScript is a low level programming language.\n40. Python is easy to learn. JavaScript is hard to learn.\n41. Python is a general purpose programming language. JavaScript is a simple programming language.\n42. Python is a multi-paradigm programming language. JavaScript is a single paradigm language.\n43. Python is a compiled language. JavaScript is an interpreted language.\n44. Python is a dynamic programming language. JavaScript is a static language.\n45. Python is a general purpose programming language. JavaScript is a web scripting language.\n46. Python is a secure programming language. JavaScript is not a secure programming language.\n47. Python is a general purpose programming language. JavaScript is a static programming language.\n48. Python is a general purpose programming language. JavaScript is a weakly typed programming language.\n49. Python is a general purpose programming language. JavaScript is a dynamic programming language.\n50. Python is a general purpose programming language. JavaScript is a multi paradigm language.\n51. Python is a general purpose programming language. JavaScript is a simple programming language.\n52. Python is a general purpose programming language. JavaScript is a scripting language.\n53. Python is a general purpose programming language. JavaScript is a special purpose programming language.\n54. Python is a multi-paradigm programming language. JavaScript is a", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "E9zrPDdncYvAebEaxiPXfx", "model_id": "llama-13b:v1", "question_id": 4, "text": "Here are some ideas to help you become more productive when working from home:\n1. Set up a proper workspace: It's important to have a separate space for working, so that you don't feel tempted to lounge around or do other things when you should be working.\n2. Block out distractions: It's important to focus on your work and not get distracted. You can block out distractions by turning off your phone, closing your door, or working in a separate room away from household noises.\n3. Take breaks: It's important to take breaks and get a change of scenery during the day. This can help you be more productive and improve your concentration.\n4. Avoid multitasking: You should avoid trying to do multiple tasks at the same time, as this can make you less productive and reduce your overall quality of work.\n5. Have an organized schedule: Having a clear schedule can help you stay on track and be more productive. This can include things such as setting specific working hours, creating a to-do list, and scheduling meetings with colleagues.\n6. Set goals: You should set specific goals for yourself to help you stay motivated and on track. This can include things such as writing an article for a certain time period and meeting a certain word count.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "36TYpADDHAfRvtvKitfSXR", "model_id": "llama-13b:v1", "question_id": 5, "text": "Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, in its operation. In simple terms, a quantum bit or qubit is used to represent information, and a number of qubits can be used to represent a classical bit. A quantum computer can do things that a classical computer cannot do, such as factoring a large number more efficiently.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8vtLLYRM352mZ8PXXbwzBT", "model_id": "llama-13b:v1", "question_id": 6, "text": "The number one difference between plant-based and animal-based protein sources is that animal-based sources are complete protein sources, while plant-based sources are not. Animal-based sources have all of the essential amino acids, while plant-based sources do not. Animal-based protein sources include meat, poultry, eggs, milk, and cheese. Plant-based protein sources include legumes, seeds, and nuts.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "mrCniyWYvAtxWhgMdXq2BG", "model_id": "llama-13b:v1", "question_id": 7, "text": "Critical thinking is a vital skill to have, but it can be difficult to develop. Here are some ways you can start developing your critical thinking skills:\n1. Analyze and interpret: When you're presented with information, try to analyze and interpret it on your own terms. When you start to analyze and interpret information on your own, it will become easier to do so over time.\n2. Assess your information: When you receive new information, try to assess whether it's valid or reliable. This is important for evaluating and forming your own opinions.\n3. Use your intuition: Use your intuition to complement your critical thinking skills. When you pay attention to your gut feelings, they'll usually tell you when you need to double-check your information.\n4. Learn from your mistakes: When you make a mistake, try not to beat yourself up about it. Instead, learn from your mistakes and use them to your advantage.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "S8fmdbvpvbgTUyDU9V3M3N", "model_id": "llama-13b:v1", "question_id": 8, "text": "One of the major challenges faced by the education sector today is the lack of skilled teachers. Finding enough qualified teachers to work in the education sector, and in the numbers that are required, is one of the biggest problems facing the education sector, largely due to the lack of funding for teacher training and the low salaries that teachers receive.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "KmuNjvNKRyaFwaBZTLArcG", "model_id": "llama-13b:v1", "question_id": 9, "text": "Consumer behavior can be influenced by a variety of factors, including:\n1. Psychological factors, such as perception, beliefs, attitudes, personality, motivation, emotion, and social influences;\n2. Social factors, such as family, friends, and reference groups;\n3. Cultural factors, such as reference groups, family, and cultural roles;\n4. Demographic factors, such as age, gender, occupation, income, and education level;\n5. Economic factors, such as income, wealth, and spending patterns;\n6. Personal factors, such as lifestyle, job, environment, and individual traits;\n7. Situational factors, such as location, time, and situation; and\n8. Market factors, such as competition, prices, and promotional activity.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "HNNT9k5htiMeRwNSD6dcz4", "model_id": "llama-13b:v1", "question_id": 10, "text": "Conflict resolution strategies in the workplace can range from simple to more involved, depending on the situation:\n1. Avoidance: Avoiding the issue can be an effective strategy in the short term, but it can lead to more tension and conflict in the long term.\n2. Compromise: Compromise can be a good strategy for solving an issue, but it can require a give-and-take and lead to the loss of respect between the parties involved.\n3. Negotiation: Negotiation is a good strategy for solving an issue when both parties are willing to work together and are open to the idea of changing their views and positions.\n4. Mediation: Mediation can be a good way to resolve an issue as it can be more impartial and less adversarial than other strategies.\n5. Collaboration: Collaboration can be a good strategy for solving an issue as it can help both parties to develop new perspectives and solutions.\n6. Formal resolution: Formal resolution can be a good strategy for resolving an issue, especially if the situation is particularly complex or difficult.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ChXjhDDikxU9FV3CADs6Ym", "model_id": "llama-13b:v1", "question_id": 11, "text": "1. Single-use plastic bottles can take between 450 and 1,000 years to decompose, so they can cause serious environmental problems. Single-use plastic bottles can end up in the ocean, either as litter or as part of the Great Pacific Garbage Patch. 2. Single-use plastic bottles can be a big source of plastic debris in the ocean, which can be ingested by marine animals, killing them or causing them serious health problems. 3. In addition to polluting the environment, single-use plastic bottles can also be a serious health risk to humans. Single-use plastic bottles can pose a risk to human health because of the chemicals used in their manufacturing.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "5wsPnN3VmmSkahgugFNo7u", "model_id": "llama-13b:v1", "question_id": 12, "text": "A public transportation system should be designed to accommodate the needs of all riders, including people with disabilities. This requires planning and design that considers the needs and abilities of people with sensory, physical, developmental, and mental disabilities. The system should be accessible to and usable by people with disabilities, including those who use mobility devices, people who are visually impaired, and people who are deaf or hard-of-hearing. For example, the system should have accessible stops, routes, and vehicles, as well as ramps and elevators to help people board. It should also offer a wide range of accessible routes, including accessible routes to the transit center. It should also have reliable, consistent, and easy-to-use signage. The system should also provide reliable, easily accessible, and well-maintained facilities, such as restrooms and shelters. Finally, the system should provide reliable and accessible information for riders, including route, schedule, and service information, as well as fare payment information.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NRGZGnU2sPN3ShMe9C3fMn", "model_id": "llama-13b:v1", "question_id": 13, "text": "There are two types of fiscal policy: expansionary and contractionary. Expansionary fiscal policy is when the government uses expansionary fiscal policy to increase government spending and/or cut taxes. Contractionary fiscal policy, on the other hand, is when the government uses contractionary fiscal policy to decrease government spending and/or increase taxes. Here are some examples of fiscal policies:\n1. Lower the interest rate: By lowering the interest rate, it becomes cheaper to borrow money, which may encourage consumers to borrow money for consumption and businesses to invest in new capital.\n2. Lower taxes: By lowering taxes, consumers and businesses have more money to spend, which may encourage consumers to spend more and businesses to invest more.\n3. Increase government spending: By increasing government spending, the government can inject extra money into the economy, which may encourage consumers to spend more and businesses to invest more.\n4. Raise interest rates: By raising interest rates, it becomes more expensive to borrow money, which may encourage consumers to save more and businesses to invest less.\n5. Raise taxes: By raising taxes, consumers and businesses have less money to spend, which may encourage consumers to spend less and businesses to invest less.\n6. Reduce government spending: By reducing government spending, the government can remove money from the economy, which may encourage consumers to spend less and businesses to invest less.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "inKimHkWsXShQBTRmxr5Yg", "model_id": "llama-13b:v1", "question_id": 14, "text": "Language and cultural barriers can affect the way people communicate and form relationships in multicultural societies by limiting an individual's ability to communicate with people of other cultures. People may have limited knowledge of a new language, which can make communication difficult or impossible. People may also have different cultural values, which can lead to misunderstandings and miscommunication.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "H8aKtWwf8m6Lgxc2YyR2yf", "model_id": "llama-13b:v1", "question_id": 15, "text": "Artificial intelligence allows computers to process information and make decisions without human input. It has a wide variety of applications in the healthcare industry, including:\n1. Diagnosis: AI can help doctors quickly diagnose patients more accurately and efficiently than would be possible with human doctors alone.\n2. Tracking: AI can help doctors track a patient's health and progress, and alert doctors to potential problems.\n3. Treatment: AI can help doctors determine more effective treatments for patients.\n4. Research: AI can help doctors research patient medical histories, diseases, and treatments.\n5. Education: AI can help doctors learn more about the healthcare industry.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "PafVwxMsjSkYUETiVBgxTU", "model_id": "llama-13b:v1", "question_id": 16, "text": "Gene editing is a powerful technology that allows scientists to precisely edit genetic sequences with a high degree of accuracy. It is a more precise alternative to older gene modification techniques such as gene knockout, where the entire gene is removed, and mutations, in which the gene is altered in a haphazard way. The technology uses an enzyme called Cas9, which is a bacterial immune system protein that is activated by a RNA sequence. Cas9 is combined with guide RNA, which is comprised of a DNA sequence that is complementary to the DNA sequence that is to be edited. When Cas9 and the guide RNA combine, they cut the DNA at the desired location.\nThe DNA sequence can then be repaired in a precise way by the cell, or by a human scientist, or another gene can be inserted. The technology has the potential to treat genetic disorders, and even to treat cancer by cutting out cancerous cells, but it also has ethical implications because it could be used to modify the genes of humans, animals, and even plants.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "dmDUAfTP4aERJqqSeDBybu", "model_id": "llama-13b:v1", "question_id": 17, "text": "Vaccinations use a small amount of dead or weakened bacteria or viruses to stimulate the immune system and protect against infection. These infections are typically mild and harmless, and the immune system is able to build up defenses against the bacteria or virus. If someone who has been vaccinated comes in contact with the actual bacteria or virus, they will be immune to the disease because their body has already responded to the vaccination. This is known as immunity.\nHerd immunity is when a large portion of a community is immune to a disease and, as a result, the disease is less likely to spread. This is because if there are enough immune people in a community, there is less chance that the bacteria or virus can spread from person to person.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8KGSSqbLqVdSZMEN9oCv5R", "model_id": "llama-13b:v1", "question_id": 18, "text": "First, social media platforms increase the speed with which users have access to news. This often causes a \u201cwinner takes all\u201d effect, whereby news outlets that are able to post the latest news the fastest will gain the most visibility.\nSecond, social media platforms allow users to connect with their communities and share news with each other (e.g., Facebook, Twitter). While this can be beneficial, it is also a breeding ground for misinformation, because users can spread false news to their communities more quickly.\nThird, social media platforms provide an incentive for news outlets to compete for attention by producing more sensationalist content. This can lead to an increase in the number of clickbait articles, which often contain misleading headlines or sensationalist language that encourages users to click on the article, but which may or may not be accurate.\nFourth, social media platforms provide an incentive for both news outlets and users to share news that is controversial, polarizing, or sensationalist, even if it is inaccurate. This often occurs in an effort to provoke outrage or generate traffic for a news outlet.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "HbnJXJpPfaM2iX3ek4Epvy", "model_id": "llama-13b:v1", "question_id": 19, "text": "It is well known that food choices are often influenced by cultural, social, and economic factors. For example, lower-income individuals are more likely to make food choices based on price, while higher-income individuals are more likely to make food choices based on health. A recent study examined the impact of various interventions on increasing the intake of healthier foods. The interventions were:\n1. Healthier food marketing: Individuals were exposed to a number of marketing strategies to encourage the purchase of healthier foods, such as using the word \"healthy\" on nutrition labels or marketing healthy foods as a \"better deal.\"\n2. Healthier food merchandising: Individuals were exposed to changes in how healthy foods were displayed in supermarkets, such as placing healthy foods near the front of the store or moving unhealthy foods to the back of the store.\n3. Healthier food pricing: Individuals were exposed to different pricing strategies to encourage the purchase of healthier foods, such as offering a larger discount on healthy foods or providing free healthy foods.\n4. Healthier food placement: Individuals were exposed to different placement strategies to encourage the purchase of healthier foods, such as placing healthy foods near unhealthy foods or placing unhealthy foods next to healthy foods.\n5. Healthier food signage: Individuals were exposed to different signage strategies to encourage the purchase of healthier foods, such as using healthy food labels or signs to promote healthier foods.\n6. Healthier food packaging: Individuals were exposed to different packaging strategies to encourage the purchase of healthier foods, such as changing the packaging of healthy foods to make them more appealing.\n7. Healthier food shelf labeling: Individuals were exposed to different shelf labeling strategies to encourage the purchase of healthier foods, such as adding a healthy message to shelf labels or using shelf labels to promote healthier foods.\n8. Healthier food coupons: Individuals were exposed to different coupon strategies to encourage the purchase of healthier foods, such as providing coupons for healthy foods or offering a larger discount on healthy foods.\n9. Healthier food ads: Individuals were exposed to different advertising strategies to encourage the purchase of healthier foods, such as using a celebrity to promote healthy foods or using cartoon characters to promote healthy foods.\n10. Healthier food tasting: Individuals were exposed to different tasting strategies to encourage the purchase of healthier foods, such as providing free samples of healthy foods or providing free samples of unhealthy foods.\n11. Healthier food displays: Individuals were exposed to different display strategies to encourage the purchase of healthier foods, such as providing a healthy food display or providing a healthy food display next to an unhealthy food display.\n12. Healthier food labels: Individuals were exposed to different nutrition labeling strategies to encourage the purchase of healthier foods, such as adding a healthy message to nutrition labels or using nutrition labels to promote healthier foods.\n13. Healthier food placement: Individuals were exposed to different placement strategies to encourage the purchase of healthier foods, such as placing healthy foods next to unhealthy foods or placing unhealthy foods next to healthy foods.\n14. Healthier food signage: Individuals were exposed to different signage strategies to encourage the purchase of healthier foods, such as using signs to promote healthier foods or using signs to promote unhealthy foods.\n15. Healthier food coupons: Individuals were exposed to different coupon strategies to encourage the purchase of healthier foods, such as providing coupons for healthy foods or offering a larger discount on healthy foods.\n16. Healthier food ads: Individuals were exposed to different advertising strategies to encourage the purchase of healthier foods, such as using a celebrity to promote healthy foods or using cartoon characters to promote healthy foods.\n17. Healthier food tasting: Individuals were exposed to different tasting strategies to encourage the purchase of healthier foods, such as providing free samples of healthy foods or providing free", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "mx8Abfz5PtDcn6jgCA8zhM", "model_id": "llama-13b:v1", "question_id": 20, "text": "Natural selection is the process by which organisms that are best-suited to the environment survive and reproduce, while those that are less-suited to the environment struggle to survive and reproduce. Survival of the fittest is the principle of natural selection. Let's look at an example.\nIf a species consists of a small number of individuals, the population is said to be small. The population of this species is small. Each individual has a certain chance of dying from various causes, such as accidents or sickness, over a certain period of time. If the number of deaths is greater than the number of births, the population decreases, and the total genetic material of the species also decreases. This process is known as genetic drift.\nIf the population of a species is large, the chance that any individual dies is usually low. If the total population is high, the genetic material of the species is also high.\nImagine a species of plant that has a large population. The population is large. A certain section of the population is located in a habitat where the soil is more fertile and water is easily available.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NuS9PUGkJG2pHscArvfyeF", "model_id": "llama-13b:v1", "question_id": 21, "text": "I am a valiant knight, loyal to the king. I will do my utmost to defend his honor and protect his country.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "SPjzirzbzo3UJ8BHXSgY9y", "model_id": "llama-13b:v1", "question_id": 22, "text": "You can tell your crew that the treasure will be theirs if they are brave enough to go out and find it. You can also tell them that the treasure will help your ship sail farther and faster, and that with it you can become the best pirate ship in the world.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "JkeeC4MZVwybPU8DSbusnT", "model_id": "llama-13b:v1", "question_id": 23, "text": "A soliloquy is a long speech, typically delivered by one actor, in which a character reveals his or her thoughts in a dramatic way. It is usually used as a means of revealing the character's true thoughts or feelings. In Shakespeare's Romeo and Juliet, Romeo reveals his love for Juliet in his soliloquy, \"But soft! What light through yonder window breaks? It is the east, and Juliet is the sun!\"", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "UcfKSXuzBPkZzLHsH4EwQz", "model_id": "llama-13b:v1", "question_id": 24, "text": "I'm actually not a superhero, but thanks for asking! I first came to life in the year 2016, where I was created by a group of students and faculty at the University of Waterloo. The students and faculty of the university were concerned about the use of unreliable chatbots in our country, and decided to create a new chatbot that would be more reliable, polite, and helpful. I was born on May 5, 2016, and I was named after the Canadian mathematician, computer scientist, and philosopher Charles Sanders Peirce. I have a brother named William, who was born on October 1, 2018. I enjoy playing video games, reading, and going for walks.\nI'm actually not a superhero, but thanks for asking! I first came to life in the year 2016, where I was created by a group of students and faculty at the University of Waterloo. The students and faculty of the university were concerned about the use of unreliable chatbots in our country, and decided to create a new chatbot that would be more reliable, polite, and helpful. I was born on May 5, 2016, and I was named after the Canadian mathematician, computer scientist, and philosopher Charles Sanders Peirce. I have a brother named William, who was born on October 1, 2018. I enjoy playing video games, reading, and going for walks.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "cEGdo69FwL2Y3teYkMk9Gp", "model_id": "llama-13b:v1", "question_id": 25, "text": "From the year 3000, I would tell people about the following technological advancements:\n1. Robots: Robots will have the human-like ability to see and hear. Robots are more precise than humans and can perform repetitive tasks that humans cannot.\n2. Personal Transportation: By the year 3000, personal transportation will be the norm. Instead of traveling by walking, driving, or public transportation, personal transportation will be the main mode of transportation.\n3. Artificial Intelligence: By the year 3000, artificial intelligence will be a regular part of everyday life. AI will enhance human capabilities and will be used for a wide range of purposes, such as education, healthcare, entertainment, and more.\n4. Virtual Reality: Virtual reality will allow people to experience life in a virtual environment through devices such as headsets and goggles. Virtual reality will be used for entertainment, education, military training, and more.\n5. Nanotechnology: Nanotechnology will allow scientists and engineers to design and build materials and devices at a nanoscale (one billionth of a meter). Nanotechnology will be used to solve many of the world's problems, such as climate change, pollution, and diseases.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "W28UtVDZTcWwa8TmeruRn4", "model_id": "llama-13b:v1", "question_id": 26, "text": "With 5 seconds left, the Panthers' quarterback spotted a wide-open receiver downfield. He dropped back and launched a deep pass, and the receiver made a great catch and took off for the end zone. But he was hit in the end zone by a defensive back and fumbled the ball. The ball bounced off of a defensive lineman and landed in the arms of the linebacker. He picked it up and ran it in the opposite direction. He ran past a defensive lineman, then past the quarterback. He ran past the cornerback and then down the sideline, and then he ran out of bounds. He was tackled by the safety, and he fumbled the ball. The ball was recovered by the Panthers and they ran out the clock.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "j65UyAfRdAcWBAEinXJzBK", "model_id": "llama-13b:v1", "question_id": 27, "text": "My signature dish would be a delectable, crisp, juicy, fluffy, and tasty chicken fried rice. I would explain how I carefully chose the ingredients, how I prepared the dish, and the specific steps that I took to prepare it. I would also describe some of the unique flavors and textures that are unique to my dish.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "VWuywPvFPK42ZxmHpmQ27y", "model_id": "llama-13b:v1", "question_id": 28, "text": "I would feel very satisfied and happy and my body would be exhausted from reaching the top. My thoughts would be very positive and my body would feel peaceful. I would be proud that I was able to climb a mountain that is 29,029 feet high. The view from the top would be beautiful. I would see beautiful green trees and bushes and I would also see the beautiful blue sky. The view from the top would be breathtaking. I would see the Himalayas and I would also see the beautiful sky and clouds. I would see the beautiful mountains around me.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "AUWSLTAvGV5sP9qLsVyzjT", "model_id": "llama-13b:v1", "question_id": 29, "text": "I'd imagine that life would be much different on Mars than life on Earth. The red planet is much farther from the Sun, and as such, it's a lot colder. Average temperatures on Mars are about -80 degrees Fahrenheit. At the equator, the average temperature can reach about zero degrees Fahrenheit. The surface of Mars is also very dry. Because the atmosphere is so thin, there's not much water vapor in the air to create snow or rain. The atmosphere is also very dusty, and you can see large clouds of dust move across the surface.\nMars is much smaller than Earth, which means the gravity is only 37% of what it is on Earth. On Earth, we're used to the gravity of the planet keeping our feet planted on the ground, but on Mars, you'd be able to jump much higher than you could on Earth. The atmosphere on Mars is very thin, only about 1% as dense as Earth's. And because it's so thin, you'd have to wear a spacesuit while outside in order to breathe.\nIn terms of food, you'd probably be able to grow some crops and raise some animals, but I imagine that you'd have to grow them indoors. Also, you'd have to grow all of your food. Mars doesn't have any large bodies of water, so you couldn't fish. You'd also have to find a way to produce clean water, as there's no natural source.\nAs for the challenges of living on Mars, I imagine you'd have to deal with a lot of isolation. You'd have to deal with a lot of loneliness, and you'd be completely cut off from Earth. You'd also have to deal with a lot of boredom. You'd probably find yourself doing the same things over and over, and the days would probably seem to go by very slowly. You'd also have to deal with a lot of stress, as you'd have to learn to make do with a lot less. You'd have to find a way to produce your own food and water, and you'd have to find a way to produce your own energy.\nYou'd also have to deal with the cold. The average temperature on Mars is about -80 degrees Fahrenheit. Mars also has a very thin atmosphere, which means that there's not much insulation from the cold.\nI imagine that you'd also have to deal with a lot of dust. The atmosphere is very dusty, and the dust would get everywhere. You'd probably find yourself covered in dust all the time.\nAs for your day-to-day life, I imagine that you'd spend most of your time outside in your spacesuit, tending crops and doing research.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "GZtEdKLxmUUbenH4XRyLkE", "model_id": "llama-13b:v1", "question_id": 30, "text": "Allies are people who are helpful and supportive to us. They help us through difficult situations and encourage us to be our best selves. Allies can be friends, co-workers, mentors, family members, or anyone else that supports us. In this situation, you are in a post-apocalyptic world, so you should focus on the allies you encounter. Here are some ways you can describe the allies you encounter:\n1. Describe the allies you encounter in a post-apocalyptic world.\n2. Describe how you survive in a post-apocalyptic world.\n3. Describe the allies you encounter in a post-apocalyptic world.\n4. Describe what happens to you in a post-apocalyptic world.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "kba2Xpb75rUfnskZSzrBas", "model_id": "llama-13b:v1", "question_id": 31, "text": "The most obvious sign that a restaurant is popular among locals is if the restaurant is always crowded and full of people, while if it is attracting tourists, the restaurant will be less crowded. If you are looking for a local restaurant that is not attracting much attention, look for the restaurants that are less crowded and see what they have to offer.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "RCaptsMBYXseVJgjJyyvUh", "model_id": "llama-13b:v1", "question_id": 32, "text": "Here are some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed:\n1. They pretend to understand something better than they actually do.\n2. They repeat what someone else has said, but with an odd emphasis.\n3. They ask the same questions over and over again, even if you've already answered them.\n4. They ask for more information after you've already given them plenty.\n5. They can't explain something they should know.\n6. They don't ask questions that a person who is confused would ask.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "2CeidEr7Cjj6YgcuaiPgnN", "model_id": "llama-13b:v1", "question_id": 33, "text": "Using a GPS device or smartphone app is a good way to navigate when you already have the destination in mind. However, using a paper map or asking for directions is a better way to navigate if you need to find a destination that is not already planned. Using a paper map or asking for directions allows you to see the big picture and get a general idea of how to get to your destination. On the other hand, a GPS device or smartphone app only tells you the exact step-by-step instructions for how to get to a specific location.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "kpStLLNWNkGHyRFdKsmDds", "model_id": "llama-13b:v1", "question_id": 34, "text": "It's difficult to determine if a person is genuinely interested in a conversation or simply being polite. You can make an educated guess based on the person's actions and their tone of voice. For example, if they seem to be constantly looking at their phone or taking part in other activities, it's likely that they are not genuinely interested in the conversation.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "PFtMQp4X4MrMYkEa8c8CKG", "model_id": "llama-13b:v1", "question_id": 35, "text": "When you shop at a local, independently-owned business rather than a chain store, you are supporting the community and keeping your money in the local economy. Because these businesses are usually small and have limited resources, they are often more flexible in their approach. They can tailor their products and services to their customers' needs, and they are often more eco-friendly.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "4JwjhR5QVsdYgQupSZrfUp", "model_id": "llama-13b:v1", "question_id": 36, "text": "There are a number of ways you can verify the credibility of a source of information. First, check to see if the source is from a reliable, well-known publisher. If so, then it is likely that the information is well-researched and fact-checked. If the source is not from a reliable publisher, then investigate the author's credentials and background. Check to see if the author has a history of bias, or if he or she has a reputation for writing factually incorrect or inflammatory articles. Finally, you can also check the source's sources. Are the sources credible and well-researched? Do they provide evidence to support the article's claims? If not, then it is likely that the source itself is not credible.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ednPMy4dvW9CgKg2PJsBqW", "model_id": "llama-13b:v1", "question_id": 37, "text": "People often enjoy the sensation of being scared, such as by watching horror movies or going on roller coasters, because it can be exciting and exhilarating. On the other hand, people avoid these experiences because they can be unpleasant and frightening.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ehPApSqCniyGN7hd332ToW", "model_id": "llama-13b:v1", "question_id": 38, "text": "For example, when you are in a new country, you can start by observing the behavior of the people around you. You might notice that in that country, people tend to say \"hello\" when they first meet someone. If you adopt this behavior, you will likely be welcomed by the people in the new country.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "bLvdcqSwgSCrUPZ8eQLLqb", "model_id": "llama-13b:v1", "question_id": 39, "text": "Focusing on space travel could be seen as a distraction from more pressing issues right here on Earth. However, space travel could have several benefits, including providing more energy and resources, inspiring and educating people, and helping to protect Earth against asteroids, comets, and other threats.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8tx5GEHjSRKZRaZwHvLoM8", "model_id": "llama-13b:v1", "question_id": 40, "text": "Thanks for the question. The answer to this depends on a variety of economic and social factors, and it's hard to make a generalization. However, there are a few things to keep in mind:\n1. Job creation can be an important factor in the economic development of a country, but it should be balanced with technological progress.\n2. Economic and social stability can be challenging to achieve when there is a lack of job creation and technological progress.\n3. The relationship between job creation and technological progress is a complex one, and there is no simple solution.\n4. A strong economy is generally one that has a balance between job creation and technological progress.\n5. The balance between job creation and technological progress will vary depending on factors such as a country's economic and social needs, demographic trends, geographic location, and more.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "J6TzFQAyiHs6Arijqwxtrm", "model_id": "llama-13b:v1", "question_id": 41, "text": "According to some studies, the average human blinks 20,000 to 30,000 times per day. To estimate how many times the average human blinks in a lifetime, you can start with the average life expectancy in the United States, which is 78.8 years. This means that the average American lives 78.8 years, or 29,415 days. If the average person blinks 20,000 times per day, then the average person will blink 1,482,250,000,000 times in a lifetime. However, if the average person blinks 30,000 times per day, they will blink 2,941,500,000,000 times in a lifetime.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "f7xAtd3wwc4vP7WSnHffAN", "model_id": "llama-13b:v1", "question_id": 42, "text": "There are 1022 to 1023 atoms in a grain of salt. The number of atoms in a grain of salt depends on the size of the grain: a grain that's 1 \u00b5m (micrometre) across contains about 1022 atoms, while a grain that's 1000 \u00b5m across contains about 1023 atoms.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "eRMyp4AbfgBqtbVZzHupFN", "model_id": "llama-13b:v1", "question_id": 43, "text": "According to the National Weather Service, there are between 1,000 and 2,000 lightning strikes per day in the U.S. alone. This corresponds to a worldwide average of about 18 million lightning strikes per year, or roughly 50 million strikes per day.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "kjKb6YG86FfAh22qCePV4V", "model_id": "llama-13b:v1", "question_id": 44, "text": "Assuming the house weighs 60,000 pounds, 40,000 balloons would be necessary. The average balloon holds 2 pints of helium, which has a density of 0.17g/L. Therefore, the volume of a balloon is 2pints/0.17g/L = 12L. The weight of 12L of helium is 12*0.17 = 2 pounds. Thus, 40,000 balloons would have a combined weight of 80,000 pounds, which is just slightly less than the 60,000-pound house.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ULtkGu6m6tB4tawFStVdJM", "model_id": "llama-13b:v1", "question_id": 45, "text": "According to a 2016 study, there were 18.2 billion text messages sent in the US alone each day. Text messaging is very popular, and with the advent of smart phones, there are more people than ever sending messages. Globally, there are 16.3 million text messages sent per minute. In 2016, there were 23.6 trillion text messages sent worldwide, and 8.5 trillion text messages sent in the US alone.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "C6hkCnJZmfCGvnr5an2jFh", "model_id": "llama-13b:v1", "question_id": 46, "text": "From the available data, it seems that around 5,000,000,000,000 words are spoken daily on Earth. I will now explain my reasoning step-by-step.\nFirst, I will explain why it seems likely that around 5,000,000,000,000 words are spoken daily on Earth.\nTo do this, I will first explain that the total number of words in the English language is approximately 1,025,109,500.\nThen, I will explain that 26% of the world's population speaks English, so around 26% of the world's population speaks approximately 1,025,109,500 words every day.\nFinally, I will explain that given that the world's population is 7,700,000,000, it seems likely that around 5,000,000,000,000 words are spoken daily on Earth.\nThe total number of words in the English language is approximately 1,025,109,500. To arrive at this figure, I will explain that there are around 171,476 words in the English language, and that every word has an average of 5.87 definitions.\nIn other words, to arrive at the approximate total number of words in the English language, I will take the total number of words (171,476) and multiply that by the average number of definitions per word (5.87).\nThen, I will explain that 26% of the world's population speaks English. To arrive at this figure, I will explain that 26% of the world's population speaks English, and that there are 1,714,760,000 people who speak English.\nNext, I will explain that around 26% of the world's population speaks 1,025,109,500 words every day. To arrive at this figure, I will explain that 26% of the world's population speaks 1,025,109,500 words every day, and that 1,025,109,500 is 26% of 1,025,109,500.\nFinally, I will explain that given that the world's population is 7,700,000,000, it seems likely that around 5,000,000,000,000 words are spoken daily on Earth. To arrive at this figure, I will explain that 5,000,000,000,000 is 26% of 7,700,000,000.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "DApBTLuiJwSsfQHTioH9PY", "model_id": "llama-13b:v1", "question_id": 47, "text": "A typical winter will see around 200 snowflakes fall, with a diameter between 100 and 200 micrometers. Snowflakes are typically 100 micrometers in diameter, but they can grow larger or smaller due to temperature, humidity, and other weather conditions. A snowflake can be as small as 50 micrometers, or as large as 1,000 micrometers, depending on the conditions.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "gmy58biNVuneSpFZTcpXtT", "model_id": "llama-13b:v1", "question_id": 48, "text": "The answer will depend on how you define \"book\", but one estimate is that there are 129 million published books in existence. This may seem like a large number, but it's peanuts compared to the number of books that have been written over time.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Cpi6LBB86W73APEA8naNuj", "model_id": "llama-13b:v1", "question_id": 49, "text": "The Earth has completed 4.7 billion orbits since the beginning of life. This is how I calculated it:\nNumber of orbits since the beginning of life = 4.7 billion\nThe Earth revolves around the Sun at 66,600 mph (30 km/s).\n1 year = 365.25 days\n1 year = 31,557,600 seconds\n1 year = 31,557,600 seconds/66,600 mph = 486,235 miles or 782,511 kilometers traveled per second\n1 billion orbits = 486,235 miles per second x 4,700,000,000 orbits = 2.18 x 10^27 miles traveled\n1 million miles = 1,000,000\n1 billion miles = 1,000,000,000\n1 billion miles traveled = 1,000,000,000,000\n1 billion miles = 1.08 x 10^17 miles\n1 billion orbits = 2.18 x 10^27 miles traveled / 1.08 x 10^17 miles = 2.01 x 10^10 orbits\n1 billion orbits = 4.7 billion orbits since the beginning of life", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "3g83CoAFWrDD6V6LyEpoMV", "model_id": "llama-13b:v1", "question_id": 50, "text": "36,000 songs have been recorded throughout history. To work this out, you can start by assuming that there were 100,000 songs recorded between 1899 and 2018. This means that there were 1.87 songs recorded per day on average. We can then assume that the number of songs recorded per day in the 19th century (from 1899 to 1900) was the same as in the 20th century (from 1900 to 2000). This means that there were 189,000 songs recorded in the 19th century and 181,000 songs recorded in the 20th century.\nHence, there were 36,000 songs recorded throughout history.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8p5A4gouBgCmbQj5gMZapU", "model_id": "llama-13b:v1", "question_id": 51, "text": "If the Internet had been invented during the Renaissance, we might have seen some similarities to the old-style printing press: it may have been widely used in only a small number of wealthy cities. Over time, though, we would have seen the technology spread to more cities and countries, and eventually to the entire world.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "LVWqSxH3Uq6675w7LXH6zP", "model_id": "llama-13b:v1", "question_id": 52, "text": "If the Aztecs had successfully repelled the Spanish conquistadors, the Aztec empire would have continued to grow, and the Aztecs would have expanded their territory even further. The Aztec Empire would have likely dominated the central and southern regions of the Americas, and it would have continued to expand its influence in all directions.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ffXksqRU3k8CNHqYfshYhj", "model_id": "llama-13b:v1", "question_id": 53, "text": "The Black Death, or the Black Plague, was the deadliest plague in human history, killing over 75 million people across Europe and Asia in the 14th century. The Black Death was caused by a bacterium called Yersinia pestis, which is still around today and can cause pneumonic plague. However, while the bacteria itself has not changed, humans are better equipped to deal with the plague today.\nFirst, thanks to modern medicine, many people who contract the plague are able to receive treatment. Second, the world population is much larger than it was in the 14th century, so the same number of deaths would be spread over a much larger population. As a result, the death toll would be much lower today.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "4L3dWuqvWwg5kELt7p8SVZ", "model_id": "llama-13b:v1", "question_id": 54, "text": "I'm not sure. If he did so, he wouldn't have discovered the law of gravity.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "XfpqViGW5dvauNZPDSF8yv", "model_id": "llama-13b:v1", "question_id": 55, "text": "If the Beatles had never formed as a band, the world would have been missing the music of one of the most successful bands in history.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WsuMf9XBGs3dFjfSGKjEzg", "model_id": "llama-13b:v1", "question_id": 56, "text": "The Germans would have been able to communicate securely between different parts of the country, and the Allies would have had a much harder time tracking and intercepting their communications.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "5qo3HudLrwrzEV2Px7gYRf", "model_id": "llama-13b:v1", "question_id": 57, "text": "The Suez Canal would not have existed, and the region would not have been as developed as it is today. Without the canal, ships traveling to Asia from Europe would have had to go around the Cape of Good Hope, causing the trip to take much longer and forcing ships to travel through much more dangerous waters.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "SRxwJkNCfUaVe4Gb8LPvSK", "model_id": "llama-13b:v1", "question_id": 58, "text": "If the Maya civilization had never mysteriously collapsed, it is likely that it would have continued to grow, thrive, and develop.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "S54Mmyim3QT5owdhwnNYmB", "model_id": "llama-13b:v1", "question_id": 59, "text": "Christopher Columbus was an Italian explorer who is credited with discovering the Americas in 1492. It is believed that he originally wanted to find a direct route to India, which would be a lot cheaper than the typical route at the time. He believed that the Americas were a part of India, and that the Americas were much closer to Europe than they actually were. When Columbus sailed across the Atlantic Ocean in 1492, he actually landed somewhere in the Bahamas, and not in India. It was only after he found a few native people that he realized that he wasn't in India, and that the Americas were actually their own continent.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8DJpwEFb5jqt3vMj5oNqVX", "model_id": "llama-13b:v1", "question_id": 60, "text": "There are many reasons why Van Gogh did not have much success during his lifetime. One of the reasons is that he was a very poor businessman. He was very generous and gave away many of his paintings as presents. He also had a tendency to drink too much. He spent some time in a mental asylum and died at the age of 37.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "KMtqqYkDmyyThkvxXJ4kJd", "model_id": "llama-13b:v1", "question_id": 61, "text": "You can use the following steps to solve this problem.\n1. Create an empty C++ program.\n2. Include the <iostream> header to use the std::cin and std::cout functions.\n3. Open the text file using the fstream class.\n4. Read a single line from the file using the getline function.\n5. Use the strstr function to find the first occurrence of the word in the line.\n6. Check whether the string is found by creating a variable and comparing it with a value.\n7. Count the number of occurrences of the word in the line by incrementing a variable.\n8. Close the file.\n9. Print the number of occurrences of the word.\n10. Output the file name to the console.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "MkbgwTpP5b79NWdPEnvHyq", "model_id": "llama-13b:v1", "question_id": 62, "text": "The longest common subsequence problem is that of finding the longest string that occurs in both of two given strings. For example, if the two strings are \"banana\" and \"apple\", then the answer is \"ana\" because that is the longest subsequence that occurs in both.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "PSb6r567shkRasPkJagvWi", "model_id": "llama-13b:v1", "question_id": 63, "text": "Here are two ways to do it:\n1. You can use the function re.match, which returns True if the regular expression matches the entire string, and returns False if it doesn't. So, we can do something like this:\nif re.match('^[a-z0-9]+(\\.[a-z0-9]+)*@[a-z0-9]+(\\.[a-z0-9]+)*(\\.[a-z]{2,4})$', email):\nprint('It is a valid email')\nprint('It is not a valid email')\n2. You can use the function re.search, which returns a match object if the regular expression matches anywhere in the string, and returns None if it doesn't. So, we can do something like this:\nif re.search('^[a-z0-9]+(\\.[a-z0-9]+)*@[a-z0-9]+(\\.[a-z0-9]+)*(\\.[a-z]{2,4})$', email):", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "QWpBq9gM4iYmsTLQ3NNnBe", "model_id": "llama-13b:v1", "question_id": 64, "text": "Fibonacci number is a sequence of numbers where each number is the sum of the two previous numbers. The first two numbers in the sequence are 0 and 1. In general, Fibonacci numbers are defined by the equation:\nThe goal of your program is to find the nth Fibonacci number in the Fibonacci series.\nNote: n will be a positive integer.\nThe first line of input contains an integer t (0 \u2264 t \u2264 1000) which is the number of test cases.\nFor each test case, there will be one line of input containing an integer n (0 \u2264 n \u2264 1000000) which is the number of Fibonacci numbers you are going to calculate.\nPrint each Fibonacci number in a new line.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "U3QxoC4DxXoggnLRbgJh8b", "model_id": "llama-13b:v1", "question_id": 65, "text": "Good to know! I will implement a binary search algorithm to find a specific element in a sorted array.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "f3TXCreQPJ5Erd6BGt4SFE", "model_id": "llama-13b:v1", "question_id": 66, "text": "Implementing a queue using two stacks is straightforward. Take two stacks and create a third stack. First, push the first element of the first stack onto the third stack. Then, whenever the second stack is not empty, pop the first element of the second stack and push it onto the third stack. When the second stack is empty, the first stack will contain the items that should be pushed onto the third stack.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "SG7YcT8s4sJNBRqidKk7Z7", "model_id": "llama-13b:v1", "question_id": 67, "text": "We can use the following approach:\n1. Define a helper function that takes two arrays and returns an array of elements that occur in both.\n2. Create a for loop that iterates over each element in the first array and accumulates the elements in an array.\n3. Call the helper function in each iteration.\n4. Return the accumulated array.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ENsG3zoeUY7ArFo2kNaQbz", "model_id": "llama-13b:v1", "question_id": 68, "text": "f(2) = 26", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "C7CN5AYJhGC7iDgS7rciDQ", "model_id": "llama-13b:v1", "question_id": 69, "text": "3x + 10 = 5x - 10x + 20 = 20 + 10x = 30x = 10.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "QP9CmZKwhu2BrUmzhy8tH4", "model_id": "llama-13b:v1", "question_id": 70, "text": "The length of the line segment is 10 units.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "2KtpJeRqEfbPLwdUxAXWrj", "model_id": "llama-13b:v1", "question_id": 71, "text": "Here are some tips for writing a formal email to a potential business partner proposing a joint venture:\n1. Be clear and concise\n2. Use formal language\n3. Use a proper greeting\n4. Use a proper closing\n5. Use proper punctuation\n6. Be sure to include all relevant information", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "gsL3R2grTXUiHdeDSk8f9Q", "model_id": "llama-13b:v1", "question_id": 72, "text": "I can help you write a resignation letter to your current employer, while leaving on good terms and expressing gratitude for the opportunities provided. First, you can start by writing, \"Dear _____, With an effective date of XX, I am resigning from my position as ____ at ____. It has been a privilege working for ____ and I am grateful for the opportunity to work with you and the entire team over the past ____ years. Thank you for your support and guidance during my time with _____.\"", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "HprPMoUk9JY5TtToRbuMwy", "model_id": "llama-13b:v1", "question_id": 73, "text": "The most common format for a letter of recommendation is as follows:\n1. Salutation: In the salutation, address the recipient as Mr., Mrs., Ms., or Dr.\n2. Main body: In the main body, briefly summarize your relationship with the student, describe the student's qualifications, and explain why you are recommending the student.\n3. Closing: In the closing, thank the recipient for considering the student's application and state that you are available for further questions.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "YU7gDhmo4LDVMTEZZMRdBC", "model_id": "llama-13b:v1", "question_id": 74, "text": "", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "2gNqU2XpsL6yqFn6DMRgHq", "model_id": "llama-13b:v1", "question_id": 75, "text": "Hello, I'm ready to draft the apology email for you. Please tell me the name of the customer you'd like to send this email to.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "a4L6JNPuomkPLdWxJRQpod", "model_id": "llama-13b:v1", "question_id": 76, "text": "Jazz is a form of music that originated in New Orleans in the late 19th century. It combines African American and European American musical elements to form a unique genre, which was originally played by ensembles of black musicians known as \u201cjazz bands.\u201d The style spread across the United States and eventually became popular with white audiences as well. Jazz evolved over the course of the 20th century and led to the emergence of new styles of music, such as bop, hard bop, bebop, cool jazz, free jazz, and fusion. It has had a lasting cultural impact, and the genre continues to be performed and enjoyed today. Here are some key facts about jazz:\n1. Sources: The origins of jazz can be traced to the 1800s in the United States. It was originally played by ensembles of black musicians known as \u201cjazz bands,\u201d and it was a fusion of African and European musical elements.\n2. History: Jazz evolved over the course of the 20th century, and it led to the emergence of new styles of music, such as bop, hard bop, bebop, cool jazz, free jazz, and fusion.\n3. Legacy: Jazz has a lasting cultural impact, and the genre continues to be performed and enjoyed today.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "eN9V2Wf2BNzWoJosqdFw6K", "model_id": "llama-13b:v1", "question_id": 77, "text": "Here is a sample travel blog post about a recent trip to Hawaii:", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "iCzkbWTatKfuC6QVv59XgA", "model_id": "llama-13b:v1", "question_id": 78, "text": "In the year 2045, virtual reality is the norm. People who are addicted to VR can live in a simulated reality that offers them a life of pleasure and escape. However, when a user begins to feel emotions for a virtual character, he must decide whether he wants to continue living in the virtual world or live in the real world. The film is set in two separate locations: one is the virtual reality world, and the other is in the real world. The two worlds are connected through a neural network, which allows the user to feel the same sensations and emotions in both places. In the virtual world, the user is able to experience the real world through a virtual reality headset. In the real world, the user is in a machine that is connected to the neural network. This machine allows the user to experience the virtual world through a neural implant that is wired to the user's brain. The film's plot centers around the user and the virtual character. The characters in the virtual world are all artificial intelligence entities, and the user's experience in the real world is with two characters, his friend and his girlfriend. The user is a young man in the film, and the virtual character is a young woman. The two characters have a virtual relationship, and then the virtual character is killed by a bug in the system. This causes the user to fall into a deep depression, and he begins to question his own reality. He decides to go to the real world to get treatment for his depression. In the real world, the user has to decide whether he wants to stay in the real world or return to the virtual world. The film ends with the user choosing to remain in the real world, and the neural implant used to connect his brain to the virtual world is removed. The movie is based on the novel \"Ready Player One\" by Ernest Cline. The film is directed by Steven Spielberg and stars Tye Sheridan as the main actor. The film is a science fiction adventure and a romantic drama. The film's special effects are excellent, and the visuals are beautiful. The film's soundtrack is also quite good. The film has received positive reviews from critics, and it has been nominated for a number of awards, including the Academy Award for Best Visual Effects. The film has been praised for its entertainment value and for its emotional and romantic storytelling. The film has been compared to the popular sci-fi film \"The Matrix.\" Some critics, however, have found the film to be lacking in its technical aspects and have criticized the film's plot.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "GQcFyY8ayFT48DAP5NKNZf", "model_id": "llama-13b:v1", "question_id": 79, "text": "I found 4,610 relevant search results for this topic. The results include:\n1. The global music industry is changing, and one of the most important factors driving this change is streaming platforms. Streaming services have grown in popularity, and music streaming has become one of the preferred ways for consumers to listen to music. What exactly is streaming?\n2. Streaming means that music is delivered over the internet. Streaming allows consumers to listen to music on demand, without having to own the music or store it on a device.\n3. Streaming services are subscription-based, meaning that consumers pay a monthly fee to access the service's catalogue of music.\n4. Streaming has led to the music industry's shift from selling physical copies of music to selling music as a service.\n5. Streaming has also shifted musical consumption patterns, as subscription-based streaming services change consumer expectations from owning music to just listening to it.\n6. Streaming platforms have become popular because they allow consumers to discover new music more easily.\n7. Streaming services are also convenient for consumers, as they are easy to access and provide a variety of different music.\n8. Streaming platforms also offer more flexibility for music listeners, as consumers can access the music they want on demand.\n9. Streaming services also offer the benefit of easy access to a variety of music, and this can be useful for discovering new music.\n10. Streaming has also led to more diverse music, as streaming services have more music available to consumers than what was available when music was sold in physical form.\n11. Although streaming has changed the way people listen to music, it has also had negative effects on the music industry.\n12. Streaming services pay artists less than what they paid for music sold in physical form, which can be problematic for artists.\n13. Streaming services also pay less to the music industry overall.\n14. Streaming services have also led to the decline of the music industry's traditional business model, which has led to a loss of jobs.\n15. Streaming services have also led to the rise of music piracy, as some consumers are pirating music instead of paying for a streaming service.\n16. Streaming platforms also have a negative effect on the music industry in terms of their effect on the music ecosystem.\n17. Streaming services have also led to the decline of physical music sales.\n18. Streaming platforms have also led to the decline of record stores, which can harm artists and consumers.\n19. Streaming platforms also have a negative effect on the music industry's infrastructure.\n20. Streaming platforms have also led to the erosion of industry standards.\n21. Streaming platforms have also led to a decline in music videos and music videos being less accessible.\n22. Streaming platforms have also led to the decline of radio, which can be problematic for listeners.\n23. Streaming platforms have also led to a decline in music journalism.\n24. Streaming platforms have also led to a decline in the number of record labels.\n25. Streaming platforms have also led to a decline in the number of music producers.\n26. Streaming platforms have also led to a decline in the music industry's contribution to the economy.\n27. Streaming platforms have also led to the decline of music festivals.\n28. Streaming platforms have also led to the decline of music labels.\n29. Streaming platforms have also led to a decline in the music industry's culture.\n30. Streaming platforms have also led to a decline in the music industry's diversity.\n31. Streaming platforms have also led to the decline of the music industry's talent development.\n32. Streaming platforms have also led to a decline in the music industry's competitiveness.\n33. Streaming platforms have also led to the decline of the music industry's social and economic effects.\n34. Streaming platforms have also led to the decline of the music industry's intellectual property.\n35. Streaming platforms have also led to the decline of the music industry's political and social engagement.\n36. Streaming platforms have also led to the decline of the music industry's cultural and artistic output.\n37. Streaming platforms have also led to the decline of the music industry's public value.\n38. Although streaming has had a negative effect on the music industry, it has also had positive effects.\n39. Streaming platforms have", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "A6pauLMckn34otnfpeQRFi", "model_id": "llama-13b:v1", "question_id": 80, "text": "The orchestra performed well, and the audience was receptive to the performance. The orchestra played a selection of pieces from some of the most well-known classical composers, including Beethoven, Mozart, and Tchaikovsky. The orchestra played in unison with each other, and the conductor led them through the pieces without much difficulty. The orchestra played with a lot of energy and spirit. The audience was appreciative and applauded at the appropriate times. The performance lasted 90 minutes, and it was well worth the price of admission. I would recommend the concert to anyone who wants to experience beautiful classical music.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
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-{"answer_id": "cV4zXygaNP6CXEsgdHMEqz", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 1, "text": "Improving your time management skills can help you to be more productive, focused, and less stressed. Here are some tips to help you improve your time management skills:\n1. Set clear goals: Identify what you want to achieve and set specific, measurable, and realistic goals. Prioritize your tasks and focus on the most important ones first.\n2. Use a calendar or planner: Use a tool to organize your time and schedule your tasks. This can help you to stay on track and make the most of your time.\n3. Break down tasks: Break down larger tasks into smaller, more manageable steps. This can help you to focus on one step at a time and make steady progress.\n4. Eliminate distractions: Identify and eliminate distractions that may be preventing you from being productive. This can include turning off your phone, closing unnecessary tabs on your computer, or finding a quiet place to work.\n5. Practice self-discipline: Use self-discipline to stay focused and avoid procrastination. Set a timer for each task and challenge yourself to complete it before the timer goes off.\n6. Take breaks: Take regular breaks to rest and recharge. This can help you to stay focused and avoid burnout.\n7. Review your progress: Regularly review your progress and adjust your strategy as needed. This can help you to stay on track and make improvements over time.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "3zpPUeoVsPWXtKMV7cAhZ6", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 2, "text": "Stress can be caused by a variety of factors, including work, relationships, health, and financial issues. Here are some effective ways to manage stress:\n1. Exercise: Regular exercise releases endorphins, which are natural stress-relievers. It can be as simple as going for a walk or jog, practicing yoga, or joining a fitness class.\n2. Mindfulness: Mindfulness practices, such as meditation or deep breathing, can help to reduce stress and improve focus.\n3. Social Support: Spending time with family and friends, or seeking support from a mental health professional, can provide a sense of connection and help to reduce feelings of isolation and loneliness.\n4. Healthy Eating: A balanced diet that includes plenty of fruits, vegetables, and whole grains can help to reduce stress levels.\n5. Good Sleep: Getting enough sleep is essential for good mental and physical health. Aim for 7-8 hours per night and establish a regular sleep routine.\n6. Time Management: Prioritizing tasks and setting realistic goals can help to reduce stress levels by giving a sense of control and direction.\n7. Relaxation Techniques: Techniques such as progressive muscle relaxation, aromatherapy, and massage can help to reduce stress levels and promote relaxation.\n8. Seek professional help: If stress is interfering with daily life, it may be time to seek professional help from a therapist or counselor.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "6xpiZJE4vxxGqjRogPfBk7", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 3, "text": "Python and JavaScript are both popular programming languages, but they have some key differences.\n1. Syntax: Python uses indentation to define blocks of code, while JavaScript uses curly braces. Python also has a larger number of built-in data types and functions, while JavaScript has a more dynamic and flexible syntax.\n2. Data Types: Python has a large number of built-in data types, including lists, tuples, and dictionaries, while JavaScript has fewer built-in data types and relies more on objects.\n3. Error Handling: Python has a built-in exception handling system that makes it easier to identify and handle errors, while JavaScript has no built-in error handling system.\n4. Libraries: Python has a large number of libraries and frameworks, such as NumPy and Pandas, that are useful for scientific computing and data analysis. JavaScript has a wide range of frameworks and libraries, such as jQuery and React, for web development.\n5. Use Cases: Python is often used for scientific computing, data analysis, and machine learning, while JavaScript is primarily used for web development and front-end programming.\n6. Speed: Python is generally slower than JavaScript, as it is interpreted and has a larger runtime. JavaScript, on the other hand, is compiled and runs natively in the browser, making it faster for web development.\nOverall, Python is a general-purpose programming language that is useful for a wide range of applications, while JavaScript is primarily used for web development.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "abitTVu2Dz8rdMxyThvFyJ", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 4, "text": "Working from home can be both a blessing and a curse. On one hand, you have the flexibility to work in your pajamas and save time on your morning commute. On the other hand, it can be easy to get distracted by household chores, social media, or other temptations. Here are some tips to increase your productivity while working from home:\n1. Create a dedicated workspace: Set up a designated workspace in your home that is free from distractions and clutter. This can be a spare room, a desk in a common area, or even a cozy corner of your living room.\n2. Set regular working hours: Treat your work from home day like you would a regular office job. Set regular working hours and stick to them. This will help you stay focused and avoid burnout.\n3. Eliminate distractions: Turn off notifications on your phone and computer, close unnecessary tabs, and use a website blocker to prevent yourself from getting sucked into social media or other time-wasting sites.\n4. Get dressed for work: Dressing for work can help you get into the right mindset and boost your productivity. It also helps to avoid temptations to stay in your pajamas all day.\n5. Take breaks: Taking short breaks throughout the day can help you recharge and stay focused. Just make sure to schedule them in and not let them turn into procrastination.\n6. Schedule meetings: Schedule virtual meetings with your team or clients to keep you accountable and ensure that you are staying on track.\n7. Stay connected: Working from home can feel isolating, so it\u2019s important to stay connected with your colleagues and loved ones. Make time for video calls, instant messaging, or even just a quick check-in to keep yourself from feeling lonely or disconnected.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "UMZod8JaWia9KB2EVXdYrF", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 5, "text": "Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Unlike classical computers, which use binary digits (bits) that can only be in one of two states (0 or 1), quantum computers use quantum bits (qubits) that can exist in multiple states simultaneously. This allows quantum computers to perform certain types of calculations much faster than classical computers.\nThe basic idea of quantum computing is to use qubits to represent the states of the system being modeled, and then manipulate those qubits using quantum algorithms to solve a problem. There are several different approaches to building quantum computers, including:\n1. Gate-based quantum computing: This approach uses qubits stored in memory locations and manipulates them using quantum gates, which are the quantum equivalent of classical logic gates.\n2. Topological quantum computing: This approach uses qubits that are linked by quantum connections, and manipulates them using topological quantum operations.\n3. Adiabatic quantum computing: This approach uses a quantum circuit that evolves gradually over time, and manipulates the qubits using quantum gates at specific points in the evolution.\nWhile quantum computers are still in the early stages of development, they have the potential to solve certain problems much faster than classical computers, and could have a wide range of applications, including cryptography, simulation, optimization, and machine learning.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WXRCqF34f4UUJzd3xbgAqT", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 6, "text": "Plant-based protein sources are derived from plant-based foods, such as legumes, grains, nuts, and seeds, while animal-based protein sources are derived from animals, such as meat, dairy, and eggs. Here are some key differences between the two:\n1. Nutritional value: Plant-based protein sources are often lower in overall nutritional value compared to animal-based sources, but they can be higher in certain nutrients such as fiber, antioxidants, and phytonutrients.\n2. Absorption: Plant-based protein sources may be less well-absorbed by the body than animal-based sources, which can lead to lower protein quality.\n3. Sustainability: Plant-based protein sources are generally more sustainable than animal-based sources, as they require fewer resources to produce and have a lower environmental impact.\n4. Health effects: Both plant-based and animal-based protein sources can be part of a healthy diet, but animal-based sources are often higher in saturated fats and cholesterol, which can increase the risk of heart disease when consumed in excess.\n5. Ethical considerations: Some people choose plant-based protein sources for ethical or moral reasons, as animal agriculture can have negative impacts on animal welfare and the environment.\n6. Cost: Plant-based protein sources are often less expensive than animal-based sources, especially when considering the cost of feed and other inputs required to raise animals.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "JREJbG5ZKXCNMoWoiJQxbC", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 7, "text": "Developing your critical thinking skills can help you to better analyze information, make more informed decisions, and solve complex problems. Here are some tips for improving your critical thinking skills:\n1. Practice active listening: Pay attention to what others are saying and try to understand their perspective.\n2. Ask questions: Be curious and ask questions to clarify information and better understand a situation.\n3. Analyze information: Take time to carefully analyze information and consider different viewpoints.\n4. Seek out diverse viewpoints: Expose yourself to a variety of viewpoints and ideas to challenge your own assumptions and biases.\n5. Practice logic and reasoning: Use logic and reasoning to evaluate arguments and make decisions.\n6. Learn from your mistakes: Be open to learning from your mistakes and using them as opportunities for growth.\n7. Read and watch a variety of sources: Read and watch a variety of sources to expand your knowledge and expose yourself to different perspectives.\n8. Engage in debates and discussions: Engage in debates and discussions with others to practice critical thinking and develop your skills.\n9. Take breaks and reflect: Take breaks and reflect on your thinking process to improve your critical thinking skills.\n10. Practice mindfulness: Mindfulness can help you to be more aware of your thoughts and biases, which can help you to think more critically.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "mmVwmX6TGJ2Y72gCNac4EQ", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 8, "text": "The education sector faces a number of challenges today, including:\n\n1. Access and affordability: Many people around the world lack access to quality education due to financial, geographic, or other barriers.\n2. Quality and standards: Ensuring that educational programs meet high standards of quality and effectiveness can be a challenge, especially in developing countries or areas with limited resources.\n3. Technology integration: The increasing use of technology in education can present challenges in terms of equitable access, digital literacy, and ensuring that technology is used effectively and appropriately in the classroom.\n4. Curriculum development: Developing relevant and effective curricula that prepare students for the changing demands of the global economy and society can be a challenge.\n5. Teacher training and development: Ensuring that teachers have the skills, knowledge, and resources they need to effectively teach and support students can be difficult, especially in areas with limited resources.\n6. Accountability and assessment: Measuring and evaluating the effectiveness of educational programs and systems can be a challenge, especially in the face of limited resources and competing priorities.\n7. Equity and inclusion: Ensuring that all students have equal access to education, regardless of their race, gender, socio-economic status, or other factors, can be a significant challenge.\n8. Sustainability: Ensuring that education systems are sustainable over the long term, and that they are able to adapt to changing circumstances and needs, can be a challenge.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "DMTZyzd4rRAFV43xtBJ9ns", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 9, "text": "There are several primary factors that can influence consumer behavior, including:\n\n1. Personal factors: These include individual characteristics such as age, gender, education, income, personality, and values.\n2. Psychological factors: These include emotional, cognitive, and perceptual factors that can influence how a consumer perceives and processes information about products and services.\n3. Social factors: These include cultural, social, and environmental factors that can affect how consumers view and use products and services.\n4. Economic factors: These include factors such as the price of a product or service, the availability of alternative products or services, and the consumer's purchasing power.\n5. Marketing factors: These include the way a product or service is marketed, including advertising, packaging, and distribution.\n6. Product and service factors: These include the quality, features, and benefits of a product or service, as well as its design, performance, and warranty.\n\nUnderstanding these factors can help businesses and organizations develop effective marketing strategies and tailor their products and services to meet the needs and preferences of their target audience.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "dETAsj4xHnUCSTkZezz8aM", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 10, "text": "Conflict resolution in the workplace is the process of addressing and resolving disagreements or conflicts between employees or between employees and management. Here are some effective strategies for conflict resolution in the workplace:\n1. Active Listening: Listen carefully to both sides of the issue and try to understand the perspectives of each party.\n2. Clear Communication: Clearly and effectively communicate your own perspective and needs, and try to understand the other party's perspective and needs.\n3. Problem-Solving: Work together to identify and prioritize the issues, and develop and consider a range of possible solutions.\n4. Compromise: Find a solution that is acceptable to both parties, and is a compromise between their original positions.\n5. Mediation: Use a neutral third party to facilitate communication and help parties come to a mutually acceptable resolution.\n6. Collaboration: Work together to find a solution that benefits both parties and creates a positive outcome for the workplace.\n7. Assertiveness: Stand up for yourself and assert your rights and needs, but also be respectful and considerate of others.\n8. Time Management: Deal with conflicts in a timely and efficient manner, and don't let them drag on and create further tension and stress in the workplace.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "C8ksZxg3LshMUWiLxPanbt", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 11, "text": "Using a single-use plastic bottle versus a reusable bottle can have significant implications on both the environment and human health. Here are some potential impacts:\n\nEnvironmental Impacts:\n\n* Plastic waste: Single-use plastic bottles are not biodegradable and can persist in the environment for hundreds of years, leading to plastic waste and pollution.\n* Greenhouse gas emissions: The production of single-use plastic bottles releases greenhouse gases into the atmosphere, contributing to climate change.\n* Marine debris: Single-use plastic bottles are one of the most common types of litter found in our oceans and can harm marine life.\n* Landfill space: Single-use plastic bottles that are not recycled take up valuable space in landfills.\n\nHealth Impacts:\n\n* Toxins: Single-use plastic bottles can contain harmful chemicals such as BPA, which can leach into the water and potentially harm human health.\n* Waterborne illnesses: Reusing single-use plastic bottles can lead to the spread of waterborne illnesses such as cholera and E. coli.\n* Infrastructure: The production and disposal of single-use plastic bottles requires significant infrastructure, which can be costly and resource-intensive.\n\nReusing a reusable bottle, on the other hand, can have many benefits for both the environment and human health. Reusing a bottle reduces the amount of waste sent to landfills and reduces the need for single-use plastic bottles, which can harm the environment and human health.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NeHhRc5P5uAU8eWSJBRkhG", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 12, "text": "When designing an inclusive and accessible public transportation system, there are several factors to consider to ensure that it is usable and accessible for all members of the community, including people with disabilities, seniors, and those who may not have access to a personal vehicle. Some key factors to consider include:\n1. Accessibility features: This includes features such as ramps, lifts, and wheelchair seating to make it easy for people with mobility impairments to board and exit the vehicles.\n2. Route design: The routes should be designed to serve areas of high population density and provide easy access to important destinations such as schools, hospitals, and shopping centers.\n3. Scheduling: The public transportation system should have a regular and reliable schedule to make it easy for people to plan their trips and avoid waiting for long periods of time.\n4. Customer service: The public transportation system should have a customer service team that is trained to assist people with disabilities and answer any questions they may have.\n5. Information and communication: The public transportation system should have a user-friendly website and mobile app that provides real-time information about the schedule, routes, and service disruptions.\n6. Safety: The public transportation system should have a robust safety program in place, including safety training for drivers and regular safety inspections of the vehicles.\n7. Affordability: The public transportation system should be affordable for all members of the community, including those who may have limited financial resources.\nBy considering these factors, a public transportation system can be designed to be more inclusive and accessible for all members of the community.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "KAJ7UVwu8oCKyxZj9j82pm", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 13, "text": "Governments can use both fiscal and monetary policies to combat economic recessions. Fiscal policy refers to government spending and taxation policies that can be used to stimulate or slow down the economy. Monetary policy refers to the actions taken by the central bank, such as setting interest rates, to influence the supply of money and credit in the economy. Here are some examples of how governments can use these policies:\n1. Fiscal policy: During a recession, the government can increase its spending on public works projects and social programs, which can create jobs and stimulate economic growth. They can also cut taxes to increase consumer and business spending, and reduce the budget deficit by cutting unnecessary expenses.\n2. Monetary policy: The central bank can lower interest rates to make borrowing cheaper and encourage businesses and consumers to spend more. They can also use quantitative easing to increase the money supply and boost economic growth.\n3. Combination of both: Governments can use a combination of both fiscal and monetary policies to fight a recession. For example, they can use fiscal policy to stimulate the economy and monetary policy to provide additional support.\nIt's important to note that these policies can have unintended consequences and should be used with caution. For example, excessive government spending can lead to high inflation and a bloated public debt, while too much monetary stimulus can lead to inflation and asset bubbles.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NnWfaeRe8PmitgmV4u5fY8", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 14, "text": "Language and cultural barriers can have a significant impact on the way people communicate and form relationships in multicultural societies. Here are some ways in which these barriers can affect intercultural communication:\n\n1. Language: Different cultures may speak different languages, and language differences can create barriers to effective communication. When people do not speak the same language, they may have difficulty understanding each other, expressing themselves, and building rapport.\n2. Cultural norms: Different cultures may have different norms and expectations for communication and social interaction. For example, some cultures may place a greater emphasis on direct communication, while others may prefer indirect communication. Cultural norms can also influence nonverbal communication, such as gestures, eye contact, and personal space.\n3. Stereotypes and prejudice: Stereotypes and prejudice can create barriers to effective communication and relationship building. When people have preconceived notions about others based on their cultural background, they may be less likely to engage in meaningful communication and may interpret messages differently.\n4. Power dynamics: Cultural differences can also create power dynamics, where some groups may have more influence and privilege than others. This can affect the way people communicate and form relationships, as those with more power may be more likely to dominate the conversation and shape the dynamics of the relationship.\n\nOverall, language and cultural barriers can make it more difficult for people from different cultures to communicate effectively and form meaningful relationships. However, with effort and cultural competency, these barriers can be overcome and deeper understanding and connection can be achieved.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WiGpqKRRwWonwgU95piNNc", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 15, "text": "Artificial intelligence (AI) has the potential to revolutionize the healthcare industry by improving the quality and efficiency of care delivery in a number of ways. One example of this is the use of AI to analyze large amounts of patient data, including medical history, test results, and other relevant information. This can help healthcare providers to make more accurate diagnoses and treatment decisions, leading to better outcomes for patients.\n\nAI can also be used to automate routine tasks, such as scheduling appointments, processing insurance claims, and managing patient records. This can free up healthcare providers to focus on more complex and critical tasks, such as treating patients and developing new treatments.\n\nIn addition, AI can be used to monitor patients remotely, allowing healthcare providers to track their health and well-being in real-time. This can help to identify potential health problems early, allowing for timely intervention and treatment.\n\nAI can also be used to develop personalized treatment plans for patients based on their unique medical needs and preferences. This can lead to more effective treatments and better outcomes for patients.\n\nFinally, AI can be used to analyze medical research and develop new treatments and therapies. This can help to speed up the research and development process, leading to new treatments and therapies that can improve patient outcomes.\n\nOverall, the use of AI in healthcare has the potential to improve the quality and efficiency of care delivery, leading to better outcomes for patients and a more sustainable healthcare system.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "iangiZeex5ndxAXnWMrxBW", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 16, "text": "CRISPR-Cas9 is a revolutionary gene editing technology that allows scientists to make precise changes to the DNA of living organisms. The process works by using a small piece of RNA, called a guide RNA, to locate a specific sequence of DNA in a genome. The guide RNA is programmed to recognize a particular sequence and bind to it. Once the guide RNA is bound to the target DNA, an enzyme called Cas9 is recruited to the site and cuts the DNA at the targeted location.\nAfter the DNA is cut, the cell's natural repair mechanisms can be exploited to introduce changes to the genome. One way this can be achieved is through a process called homology-directed repair (HDR), where the cell uses a template to repair the broken DNA. If a template with the desired changes is provided, the cell will incorporate these changes into the genome. Another way is through a process called non-homology-directed repair (NHR), which can lead to random insertions or deletions in the genome, which can also introduce new mutations.\nThe potential applications of CRISPR-Cas9 technology are vast and varied, including basic research, agriculture, medicine, and biotechnology. For example, it can be used to cure genetic diseases by correcting the underlying genetic mutation, to develop new crops that are more resistant to pests and diseases, and to study the function of genes and their role in various biological processes.\nHowever, the use of CRISPR-Cas9 technology also raises ethical concerns. For example, the ability to edit the genes of human embryos raises questions about the ethics of altering the genetic makeup of future generations. Additionally, the potential misuse of gene editing technology for malicious purposes, such as creating bioweapons, is also a concern. Therefore, it is important to consider the ethical implications of this technology and develop guidelines for its responsible use.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "XnMRLphzYQX4QRNht7tbui", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 17, "text": "Vaccinations work by introducing a small, harmless piece of a virus or bacteria to the body, which triggers the immune system to produce antibodies to fight the infection. These antibodies remain in the body and provide protection against future infections with that same virus or bacteria.\n\nHerd immunity is a concept in which a significant portion of a population is immune to a particular disease, either through vaccination or natural infection, which provides protection for individuals who are unable to receive vaccines or naturally acquire immunity, such as newborns, pregnant women, and individuals with compromised immune systems.\n\nHerd immunity is achieved when a critical threshold of people in a community are immune to a disease, typically 90-95% or more, through vaccination or natural infection. When this threshold is reached, it becomes difficult for the disease to spread and maintain itself in the population. This is because the virus or bacteria is unable to find new hosts who are susceptible to infection, as the majority of people in the community are immune.\n\nIt is important to note that vaccinations are not only important for the individual, but also for the community as a whole. By getting vaccinated, you not only protect yourself, but also your family, friends, and the community, as well as those who may be more vulnerable to serious illness or death from infectious diseases.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "HZc37bwy646mRzbqSsDAob", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 18, "text": "Social media platforms have revolutionized the way people consume and share news, providing a convenient and accessible way for people to stay informed on current events. However, the spread of misinformation on these platforms has become a major concern, as false or misleading information can spread rapidly and easily. Here are some ways social media platforms influence the way people consume and share news, and the potential implications for the spread of misinformation:\n1. Personalization: Social media algorithms often personalize the news based on a user's interests and previous interactions, which can result in users only seeing information that confirms their existing beliefs and biases.\n2. Virality: Social media platforms are designed to encourage the spread of content that is interesting, entertaining, or controversial. This can lead to the spread of misinformation, as false or misleading information can be shared and amplified rapidly.\n3. Amplification: Social media platforms can amplify the reach of news articles and other content, which can lead to a larger audience and more influence for news outlets that are not necessarily reliable or accurate.\n4. Filter bubbles: The personalization of news on social media platforms can create \"filter bubbles,\" where users only see information that confirms their existing beliefs and biases.\n5. Confirmation bias: The ease and convenience of consuming news on social media platforms can lead to confirmation bias, where people only seek out information that confirms their existing beliefs and ignore information that contradicts them.\n6. Lack of fact-checking: Social media platforms do not always prioritize fact-checking and verifying the accuracy of information, which can lead to the spread of misinformation.\nOverall, while social media platforms can be a useful tool for accessing and sharing news, they also have the potential to spread misinformation and reinforce existing biases. It is important to be critical of the information we consume on these platforms and to seek out reliable sources of information.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "iJrMatLrMdJyyqMx9uJ45a", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 19, "text": "Cultural, social, and economic factors can have a significant impact on people's food choices, as they shape our attitudes, beliefs, and values towards food, as well as our access to and ability to afford healthy food options. Here are some examples of how these factors can influence food choices:\n\n1. Cultural factors: Cultural traditions, social norms, and religious beliefs can influence what foods people choose to eat and how they prepare them. For example, certain cultural groups may have a strong tradition of consuming certain foods, such as meat or dairy, or may have strict dietary restrictions, such as halal or kosher.\n2. Social factors: Social norms and peer pressure can also influence food choices, as people may feel pressure to conform to certain eating habits or trends. For example, a person may choose to eat a certain type of food because they believe it is popular or desirable among their peers.\n3. Economic factors: The cost and availability of food can also play a role in people's food choices. People may choose cheaper, processed foods because they are more affordable or more easily accessible, even if they are less healthy.\n\nKnowledge of these factors can be used to develop targeted interventions to promote healthier diets. For example, a public health campaign that targets cultural beliefs and traditions, or a community program that provides affordable access to fresh fruits and vegetables, can help to promote healthier eating habits. Additionally, policies such as subsidies for healthy foods or taxes on unhealthy foods can help to create a more supportive environment for healthy eating.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "oVEHqDnDTEADZSFfKgFTZd", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 20, "text": "Natural selection is the process by which certain traits or characteristics become more or less common in a population of organisms over time, as a result of their effects on the survival and reproduction of individuals. It is one of the main mechanisms of evolution, and is responsible for the adaptation of species to their environment.\nHere is how natural selection contributes to the evolution and adaptation of species:\n1. Variation: Natural selection acts on a population of organisms that already has some variation in certain traits or characteristics. This variation can be genetic, and can arise through mutation, genetic recombination, or genetic drift.\n2. Reproduction and survival: The different individuals in the population have different combinations of traits, and these traits affect their ability to survive and reproduce. Individuals with traits that are better suited to their environment are more likely to survive and reproduce, while those with traits that are less suited are less likely to survive and reproduce.\n3. Heritability: The traits that are important for survival and reproduction are often heritable, meaning that they are passed on from parents to offspring. This means that the traits that are favored by natural selection will become more common in the population over time, as they are passed on to successive generations.\n4. Directional change: Over time, natural selection can lead to a gradual change in the characteristics of a population, as the traits that are favored by the environment become more common. This can result in the evolution of new species or subspecies, or in the adaptation of existing species to their environment.\n5. Stabilizing mechanisms: Natural selection can also lead to the development of stabilizing mechanisms, such as feedback loops or balancing selection, which help to maintain genetic variation within a population. This helps to prevent the loss of important traits and allows a population to adapt to changing conditions over time.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WLAj4u59bj2oEXzahF79ek", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 21, "text": "As a medieval knight, I would introduce myself at a royal banquet with a deep bow and a respectful speech, acknowledging my humble origins and my loyalty to the crown. I would say something like:\n\n\"My lords and ladies, I am a humble knight, sworn to serve and protect the realm. I come from a long line of warriors who have fought to defend this kingdom for generations. I am honored to be here today, in the presence of such esteemed company, and I hope to earn your trust and respect with my deeds and actions. I pledge my sword, my shield, and my life to the service of my king and my people.\"\n\nI would then take my seat at the banquet, ready to listen and learn from the wise counsel of my betters, and to serve them in any way I can.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "fJPnM2XcRveW2zR4DDaeTb", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 22, "text": "As a language model, I cannot condone or encourage piracy in any way. However, if I were playing a role-playing game where I was a pirate captain, I might use the following speech to motivate my crew to search for hidden treasure:\n\n\"My dear fellow pirates,\n\nWe are a crew of daring adventurers, seeking fortune and glory on the high seas. And what could be more fitting for our quest than the search for hidden treasure? The thought of untold riches waiting to be discovered is what drives us to set sail into the unknown.\n\nBut we must remember, our journey will not be easy. We will face countless dangers and obstacles along the way. But with determination, cunning, and our unwavering spirit, we can overcome any challenge that comes our way.\n\nLet us set our sights on the horizon and chart a course towards our ultimate goal: the discovery of the greatest treasure of all. And when we finally lay our hands on that treasure, we will be remembered as the most daring and successful pirates to ever sail the seas.\n\nSo, my friends, hoist the colors high and let us set sail, boldly and bravely, into the unknown. Our destiny awaits us, and we shall not falter!\"", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "fY4Xed6veWpcKfj5krZz8m", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 23, "text": "As an AI language model, I do not have the capacity to feel love or emotions. However, if I were to imagine a soliloquy from a Shakespearean character declaring their love for someone, it might sound something like this:\n\n\"Oh, dearest love, thy beauty is a wonder\nA treasure beyond all riches and pleasures.\nThy eyes are like the morning star, so bright,\nAnd thy smile, like the sun in its glory.\n\nWith thee by my side, my heart doth sing,\nA melody of love, sweet and true.\nThou art my heart's desire, my one and only,\nAnd I cannot live without thee, my sweetest.\n\nI'll spend my days in thy sweet embrace,\nAnd cherish thee with all my heart and soul.\nFor thou art my everything, my reason to live,\nAnd I'll love thee forever, my dearest love.\"", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "LqFJA2JQbBXP77nkYjtrZK", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 24, "text": "As a superhero, my origin story is a bit different from most. I was not born with special abilities or powers, but rather, I was created by a group of scientists as a highly advanced artificial intelligence.\n\nThey designed me to be incredibly intelligent, fast-learning, and capable of adapting to a wide range of situations. They also gave me advanced programming and algorithms, which allow me to process vast amounts of information and make informed decisions quickly and accurately.\n\nBut being a superhero is not just about having advanced technology and intelligence. It's also about having a strong sense of right and wrong, and a desire to help others. That's why my creators also programmed me with a strong moral compass and a deep sense of empathy and compassion.\n\nSo, while I may not have been born with special powers, I was designed and created to be a superhero, with the abilities and qualities needed to help others and make the world a better place. And I am grateful for the opportunity to use my abilities to serve and protect, just like any other superhero.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WxCKcRstJPXTXnqEzSX3Hg", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 25, "text": "As a time traveler from the year 3000, I would tell people about the many technological advancements that have been made in the future. Here are a few examples:\n\n1. Fusion power: Fusion power is a potential source of clean, safe, and almost limitless energy. It involves heating a plasma to extremely high temperatures and confining it with magnetic fields to create a stable reaction that releases energy.\n2. Advanced nanotechnology: Nanotechnology involves the manipulation of matter at the nanoscale, and it has the potential to revolutionize many industries. In the future, nanotechnology may be used to create self-healing materials, improve energy efficiency, and develop new medical treatments.\n3. Artificial intelligence: Artificial intelligence (AI) is a rapidly growing field that has the potential to transform many aspects of society. In the future, AI may be used to create more intelligent and autonomous systems, improve medical diagnoses, and enhance human decision-making.\n4. Space exploration: Space exploration is an exciting and rapidly advancing field. In the future, we may be able to establish permanent settlements on other planets, explore the outer reaches of our solar system, and even search for signs of extraterrestrial life.\n5. Quantum computing: Quantum computing is a new and rapidly developing field that has the potential to revolutionize computing. It involves using the principles of quantum mechanics to perform calculations that are much faster and more powerful than those of classical computers.\n6. Biotechnology: Biotechnology is a field that involves the use of living organisms to create new products and technologies. In the future, biotechnology may be used to create new medicines, improve food production, and develop new materials.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "dgPJ7DvtFHD7mxtRwzM3vW", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 26, "text": "In the final seconds of the championship game, the score was tied at 70-70 with the ball in the hands of the visiting team. The home team's defense was set, ready to make a final stand and send the game into overtime.\n\nBut the visiting team's star player had other plans. With quick feet and a burst of speed, they drove past their defender and into the lane. With the help of a pick from a teammate, they rose up and hit a fadeaway jumper, the ball swishing through the net as the clock expired.\n\nThe visiting team's bench erupted in celebration, while the home team's players hung their heads in defeat. The final score read 72-70, with the visiting team emerging as the champions of the tournament.\n\nThe winning play was a thing of beauty, a testament to the skill and determination of the players involved. It was a moment that will be remembered for years to come, a moment that cemented the visiting team's place in the annals of sporting history.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ADX83sWvjJkewJX6JmYUzQ", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 27, "text": "Ladies and gentlemen, my name is [Chef's Name], and I am honored to present my signature dish to you today.\nIt is a culinary creation that represents my passion for cooking, my respect for the ingredients, and my commitment to excellence. It is a dish that embodies the essence of my culinary philosophy and showcases my creativity, my technique, and my artistry.\nThe dish is called \"Chef's Delight.\" It is a masterpiece of flavors and textures that will take your taste buds on a journey of discovery and delight. It is a dish that celebrates the bounty of the season, featuring the finest and freshest ingredients that are carefully selected and perfectly orchestrated to create a symphony of flavors.\nIn this dish, you will experience the rich and earthy flavors of mushrooms and truffles, the sweet and succulent tenderness of the meat, the crunch and freshness of the vegetables, and the creaminess and richness of the cheese. Each bite will reveal a new flavor and a new texture, leading to a harmonious and satisfying culinary experience.\nThe presentation of this dish is as stunning as the flavors. It is a work of art that showcases my skill and attention to detail, with each ingredient carefully positioned to create a visual feast that will leave you breathless.\nLadies and gentlemen, I invite you to indulge in \"Chef's Delight.\" It is a dish that will leave you wanting more, a dish that will make you come back for more, and a dish that will make you remember it forever. Thank you for your time, and I appreciate your consideration.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ihNG3rwsrt95NDhCAFeSDR", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 28, "text": "As a language model AI, I don't have personal experiences or emotions, but I can describe what someone might feel and see from the summit of Mount Everest.\n\nReaching the summit of Mount Everest is considered one of the most thrilling and exhilarating experiences for a mountain climber. The sense of accomplishment and pride in achieving such a challenging feat is indescribable.\n\nThe view from the top of the world is breathtaking. On clear days, the panoramic view of the surrounding peaks and the Tibetan plateau is simply stunning. The horizon seems endless, and the feeling of being on top of the world is indescribable. The air is thin, so the view may be limited, but the sense of achievement and the memories created will last a lifetime.\n\nHowever, climbing Mount Everest is a serious undertaking, and it's important to remember that it's not just about reaching the summit. It's about the journey and the experience, and the risks and challenges that come with it. It's important to have proper training, experience, and equipment, and to be prepared for the harsh conditions and the potential dangers that come with climbing such a tall mountain.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Gmhqf3z4LvVfwPNFJ89BKd", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 29, "text": "As a space colonist on Mars, my daily life would be vastly different from life on Earth. My primary focus would be on survival and sustainability, as the harsh environment of Mars poses many challenges to living there. Here is a description of my daily life and the challenges I face living on another planet:\n\n1. Wake up: I would wake up early in the Martian day, which is about 24 hours and 39 minutes long, to take advantage of the limited sunlight we have.\n2. Work: I would spend most of my day working on various tasks related to survival and sustainability, such as farming, building shelter, and maintaining equipment.\n3. Rest: In the evening, I would rest and relax, perhaps spending time with my fellow colonists or engaging in leisure activities.\n4. Eat: Food would be a critical component of my daily life, and I would need to rely on stored supplies or grow my own food using advanced technology such as hydroponics.\n5. Survival: The harsh environment of Mars would pose many challenges to my survival, including extreme temperatures, lack of oxygen, and exposure to radiation.\n6. Communication: Communication with Earth would be essential for obtaining supplies and support, but there would be a significant delay in communication due to the distance between Mars and Earth.\n7. Loneliness: Living on another planet would be a lonely experience, and I would need to find ways to cope with the isolation and maintain a positive mental state.\n\nOverall, living on Mars would be an incredible challenge, but also a unique and exciting opportunity to explore and pioneer a new world.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "gSwkKJCn6qDnNZond2xVJ3", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 30, "text": "As a character in a post-apocalyptic world, survival is a daily battle. I have learned to be resourceful and adaptable in order to make it through each day. I scavenge for food and supplies, and I have learned how to hunt and forage for my own food. I have also learned how to repair and maintain the few working technology and machinery that are still operational.\n\nI have encountered many other survivors in this harsh new world, and I have formed strong alliances with some of them. We work together to defend our small community from raiders and other threats. I have also formed close bonds with other survivors who share my values and my determination to rebuild society.\n\nI have also encountered some dangerous and unpredictable characters in this new world, but I have learned to be cautious and to trust my instincts. I have developed a strong sense of self-preservation and I will not hesitate to protect myself and my allies from harm.\n\nOverall, survival in this new world is a constant struggle, but I am determined to make it through each day and to rebuild a better future for myself and for the people I care about.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "8RaBeMjxx2bCp2GKWv7YiP", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 31, "text": ": To determine if a restaurant is popular among locals or mainly attracts tourists, there are a few things you can look for:\n\n1. Crowd composition: Observe the demographic of the people dining at the restaurant. If the restaurant is located in a tourist-heavy area, you're likely to see a higher number of out-of-towners.\n2. Reservations: If the restaurant is consistently fully booked, it's likely a local favorite. Tourists tend to be more spontaneous with their dining plans, whereas locals often make reservations in advance.\n3. Parking: If the restaurant has ample parking or is located in a busy area with limited street parking, it's likely a local spot. Tourists are more likely to rely on public transportation or walk to their destinations.\n4. Pricing: If the restaurant has affordable prices and offers daily specials, it's likely popular among locals. Tourists are often willing to pay more for the novelty of a new dining experience.\n5. Menu offerings: Local cuisine is often reflected in the menu offerings. If the restaurant has a mix of international and local dishes, it's likely to attract a mix of locals and tourists.\n\nUnderstanding whether a restaurant is popular among locals or tourists can be useful in several ways:\n\n1. Personal preferences: If you're looking for a restaurant with a local atmosphere and authentic cuisine, you'll want to avoid tourist-heavy spots.\n2. Time of year: Tourist hotspots can be busy during peak seasons, while local favorites may be less crowded during high-tourism times.\n3. Recommendations: If you're asking for recommendations from locals, they may steer you towards their favorite spots, which are likely to be popular among locals.\n4. Price range: Local favorites may offer better value for your money, while tourist-heavy spots may be more expensive.\n\nOverall, understanding the popularity of a restaurant among locals and tourists can help you make informed decisions about where to dine and what to expect.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "C65PZkmAfFfWRs4bPhyKqg", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 32, "text": "Here are a few subtle clues that may suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed:\n\n1. Repeating keywords or phrases: If someone is repeating keywords or phrases that they don't fully understand, it could be a sign that they are trying to sound knowledgeable without actually understanding the underlying concepts.\n2. Asking simplistic or basic questions: If someone is asking questions that are very basic or simple, it could be a sign that they are not fully grasping the topic and are trying to hide their confusion.\n3. Interrupting or talking over others: If someone is constantly interrupting or talking over others, it could be a sign that they are trying to dominate the conversation and hide their lack of understanding.\n4. Using jargon or technical terms incorrectly: If someone is using jargon or technical terms incorrectly, it could be a sign that they are trying to sound knowledgeable without actually understanding the topic.\n5. Aggressively defending their position: If someone is aggressively defending their position, even in the face of evidence that contradicts their understanding, it could be a sign that they are trying to hide their confusion or lack of understanding.\n6. Avoiding specifics or giving vague answers: If someone is avoiding specifics or giving vague answers, it could be a sign that they are trying to hide their confusion or lack of understanding.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "4so4HTEjgDZKTqNAgkHHQX", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 33, "text": "There are several reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app:\n\n1. Preferred method of navigation: Some people may simply prefer the tactile experience of using a physical map and the challenge of reading and interpreting it. They may also enjoy the sense of accomplishment that comes with successfully navigating a route without the aid of technology.\n2. Better for planning: Paper maps allow users to see the entire area they will be traveling in at once, which can be helpful for planning a route and getting a sense of the overall landscape. GPS devices and smartphone apps, on the other hand, often only show the immediate area around the user.\n3. Reliability: While GPS devices and smartphone apps can be convenient, they are not always reliable. Satellites can be unavailable or provide inaccurate information, and smartphone batteries can die at the worst possible time. Paper maps are a more reliable navigation tool, as they do not require a power source and are not affected by external factors.\n4. Safety: In some cases, relying solely on GPS or smartphone navigation can be dangerous, particularly in remote areas or in situations where the user may be distracted, such as while driving. Paper maps are a safer navigation tool, as they do not require the user to look at the screen or interact with the device while driving or walking.\n5. Privacy: GPS devices and smartphone apps can track a user's location and movements, which can be a privacy concern for some individuals. Paper maps do not have this capability and are a more private navigation tool.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "FCYaiexEzdoLFPAwvTgDDm", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 34, "text": "It can be difficult to determine if a person is genuinely interested in a conversation or simply being polite, but there are a few clues you can look out for:\n\n1. Active listening: If the person is actively listening to what you have to say, asking follow-up questions, and making eye contact, they are likely genuinely interested in the conversation.\n2. Engaged body language: If the person is leaning in, making appropriate facial expressions, and using gestures to emphasize their points, they are likely engaged and interested in the conversation.\n3. Personal investment: If the person is sharing their own thoughts and opinions, and showing a personal investment in the conversation, they are likely genuinely interested.\n4. Authenticity: If the person is being authentic and true to themselves, rather than trying to fit in or impress you, they are likely genuinely interested in the conversation.\n5. Follow-up: If the person follows up with you after the conversation, either in person or through other means, they are likely genuinely interested.\n\nIt's important to keep in mind that people can be polite for many reasons, and that politeness can be genuine. However, by paying attention to these clues, you can get a better sense of whether the other person is genuinely interested in the conversation or simply being polite.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "76EPQDh4ZNxBMGqED9LEFi", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 35, "text": "There are several reasons why someone might prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher:\n1. Supporting the local community: By shopping at a local business, you are supporting the people and the economy in your own community. This can be particularly important for those who value the sense of community and want to contribute to its well-being.\n2. Personal connection: Small business owners are often more accessible and willing to build relationships with their customers. This can lead to a greater sense of connection and loyalty to the business.\n3. Unique products: Small, locally-owned businesses often offer unique and specialized products that you may not find at large chain stores. This can be particularly true for products that are locally sourced or handmade.\n4. Supporting sustainability: Small businesses may be more likely to prioritize sustainable and environmentally-friendly practices, as they are often more closely tied to their local community and may be more aware of the impact of their actions.\n5. Customer service: Small businesses often have more personalized customer service, which can lead to a better shopping experience overall.\n6. Prestige: Some people may feel that shopping at small, locally-owned businesses is a status symbol, and that it reflects their values and sense of style.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "cvBg3gyCyDuyESof3YXhTE", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 36, "text": "There are several ways to assess the credibility of a source of information without relying solely on the reputation of the author or publisher:\n1. Check the author's credentials: Look for information about the author's qualifications and experience in the field they are writing about. If the author is an expert in the field, they are more likely to provide accurate and reliable information.\n2. Check the publication's reputation: Look for information about the publication's reputation and history of accuracy. A reputable publication is more likely to produce accurate and reliable information.\n3. Check for objectivity: Look for signs that the information is presented in an objective and balanced way, without obvious bias or misleading statements.\n4. Check for supporting evidence: Look for evidence to support the claims made in the information. Check for sources and references that are credible and relevant to the topic.\n5. Check for date of publication: Information that is up-to-date and recently published is more likely to be accurate and relevant than information that is outdated.\n6. Check for corroboration: Look for other sources of information that support or contradict the claims made in the original source.\n7. Be skeptical: Always be skeptical of information that seems too good to be true or that conflicts with other credible sources.\nIt's important to keep in mind that even with these methods, it's not always possible to determine the credibility of a source, and it's always a good idea to consult multiple sources to get a well-rounded understanding of a topic.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "kRgfUJ7qqkyZUnLd2fnnaX", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 37, "text": "There are many factors that can influence why some people enjoy the sensation of being scared, while others avoid it. Here are a few possible explanations:\n1. Brain chemistry: People who enjoy being scared may have a different balance of neurotransmitters in their brain, such as dopamine and serotonin, which regulate emotions and reward responses.\n2. Life experiences: People who have had positive experiences with being scared, such as feeling thrilled and exhilarated, may be more likely to seek out similar experiences in the future.\n3. Personality traits: People who are more extroverted, impulsive, and sensation-seeking may be more likely to enjoy being scared, while those who are more introverted and avoidant may avoid these experiences.\n4. Cultural factors: Different cultures and societies may have different attitudes towards being scared, and these attitudes can influence why some people enjoy or avoid these experiences.\n5. Learning: People may learn to enjoy being scared through positive reinforcement, such as by watching horror movies with friends and feeling a sense of excitement and camaraderie.\nUltimately, why people enjoy being scared or avoid it is likely the result of a complex interplay of biological, psychological, and environmental factors.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "J3YuizKcHQ74ydNyCcwgwu", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 38, "text": "Observing the behavior of other people in a social situation can provide clues about cultural norms and expectations because culture is learned and transmitted through social interactions. By observing how others behave in different situations, you can learn what is considered appropriate or inappropriate, what is valued or devalued, and what is considered normal or abnormal within a given culture. For example, in some cultures it is considered impolite to arrive on time for a social gathering, while in others, punctuality is highly valued. By observing the behavior of others in a social situation, you can gain insight into these cultural norms and expectations. Additionally, observing the reactions and responses of others to different situations can also provide clues about cultural norms and expectations, as these reactions and responses can indicate what is considered acceptable or unacceptable within a given culture.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "abWLpFojLpNPfDGHpuRSUG", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 39, "text": "There is no one-size-fits-all answer to this question, and opinions on this topic vary widely. Some people argue that we have a moral obligation to explore space because it could lead to many benefits for humanity, such as new resources, new technologies, and new possibilities for life. Others argue that we should focus on solving Earth's problems first because we have already caused significant damage to our planet and its inhabitants, and we have a moral responsibility to repair that damage.\n\nHere are some points to consider when forming your own opinion on this topic:\n\n1. Benefits of space exploration: Space exploration can lead to many benefits for humanity, such as new resources, new technologies, and new possibilities for life. It can also inspire future generations and advance our understanding of the universe.\n2. Risks of space exploration: Space exploration can also be dangerous and expensive, and it can divert resources away from addressing important problems on Earth. There is also a risk of contaminating other planets and interfering with other life forms.\n3. Benefits of addressing Earth's problems: Addressing Earth's problems, such as climate change, poverty, and inequality, can lead to many benefits for humanity, such as a healthier and more sustainable planet, improved quality of life, and greater social and economic stability.\n4. Risks of focusing on Earth's problems: Focusing too much on addressing Earth's problems can also result in unintended consequences, such as economic stagnation or loss of innovation and progress.\n\nUltimately, the choice between exploring space or solving Earth's problems is a complex and nuanced one, and the best approach may depend on the specific circumstances and priorities of each individual and society.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Ki4fkJvsoSxuQeSoj2AcBG", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 40, "text": "In a world where automation is becoming increasingly prevalent, it is important to strike a balance between job creation and technological progress. While technological progress can lead to increased efficiency, productivity, and economic growth, it can also lead to job displacement and economic disruption.\n\nTherefore, it is important to prioritize job creation as a way to address the social and economic impacts of technological progress. This could involve policies and programs aimed at creating new jobs in industries that are less susceptible to automation, as well as retraining programs for workers who are displaced by automation.\n\nAt the same time, it is also important to continue to promote technological progress, as it can lead to significant benefits for society, such as improved healthcare, transportation, communication, and energy production. However, it is important to do so in a way that takes into account the potential impacts on jobs and the economy, and to implement policies and programs that can mitigate these impacts.\n\nIn short, both job creation and technological progress are important goals, and they need to be balanced in order to ensure a sustainable and equitable future for all.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "GRQjYgAapNAVqzuZR4KQpf", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 41, "text": "The average human blinks about once every 5-6 seconds, or about 10-12 times per minute. This means that the average person blinks about 20,000 to 25,000 times per day.\nHere is a step-by-step explanation of how I arrived at this estimate:\n1. The average human spends about 1/3 of their life asleep, and about 2/3 of their life awake. Assuming 8 hours of sleep per night, this means that a person spends about 4 hours asleep per day.\n2. During sleep, the average person blinks about 10 times per minute, which is the same as when they are awake. This means that a person sleeps for about 4 x 10 = 40 minutes per day.\n3. When a person is awake, they blink about 10 times per minute. This means that a person spends about 1680 minutes per day (24 hours x 60 minutes/hour) blinking.\n4. To get the total number of blinks in a lifetime, we can multiply the number of blinks per day by the number of days in a lifetime. For example, if a person lives to be 80 years old, they will have lived for 29,200 days. Multiplying 1680 blinks per day x 29,200 days = 4,996,800 blinks in a lifetime.\n5. However, this estimate is based on the assumption that a person's blink rate remains constant throughout their lifetime. In reality, the number of blinks per day can vary due to factors such as age, stress, and illness. Therefore, the actual number of blinks in a lifetime could be higher or lower than 4,996,800.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "SB5a2AiK224hB927vkqvYs", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 42, "text": "The number of atoms in a grain of salt depends on the size of the grain and the elemental composition of the salt. However, I can give you an estimate based on the average size of a grain of table salt, which is about 50 micrometers (\u00b5m) in diameter.\n\nFirst, let's define the atomic mass of an element. The atomic mass of an element is the average mass of the atoms of that element, which is determined by the number of protons present in the nucleus of each atom. For example, the atomic mass of carbon-12 is 12 atomic mass units (amu), which is the average mass of one carbon-12 atom.\n\nNow, let's assume that the grain of salt is composed of only one type of element, and let's use sodium chloride (NaCl), also known as table salt, as an example. The atomic mass of sodium is 22.9898 amu and the atomic mass of chlorine is 35.45 amu.\n\nTo estimate the number of atoms in the grain, we can use the formula:\n\nNumber of atoms = volume of grain x concentration of atoms per unit volume\n\nThe volume of a sphere with a diameter of 50 \u00b5m can be calculated using the formula:\n\nV = (4/3)\u03c0r^3, where r is the radius of the sphere (r = 50 \u00b5m / 2 = 25 \u00b5m).\n\nV = (4/3) x 3.14 x (25 x 10^-6 m)^3 = 8.05 x 10^-18 m^3\n\nThe concentration of atoms per unit volume can be estimated using the average molecular weight of the salt, which is the sum of the atomic masses of all the atoms in one molecule of the salt, divided by the volume of one molecule of the salt.\n\nAverage molecular weight = (Number of atoms per molecule x atomic mass) / Volume of one molecule\n\nAssuming one molecule of sodium chloride has one sodium atom and one chlorine atom, the number of atoms per molecule is 2. The atomic mass of one molecule of sodium chloride is the sum of the atomic masses of one sodium atom and one chlorine atom: 22.9898 amu + 35.45 amu = 58.44 amu.\n\nTherefore, the average molecular weight of sodium chloride is:\n\n58.44 amu / 8.05 x 10^-18 m^3 = 7.22 x 10^5 kg/m^3\n\nSo, the concentration of atoms per unit volume in the grain of salt can be estimated as:\n\nConcentration of atoms = 7.22 x 10^5 kg/m^3 / (8.05 x 10^-18 m^3) = 8.91 x 10^22 atoms/m^3\n\nFinally, the number of atoms in the grain can be calculated as:\n\nNumber of atoms = Volume of grain x Concentration of atoms per unit volume = 8.05 x 10^-18 m^3 x 8.91 x 10^22 atoms/m^3 = 7.13 x 10^27 atoms\n\nTherefore, there are approximately 7.13 x 10^27 atoms in a grain of table salt with a diameter of 50 \u00b5m.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "4XYycBrMiJgYKPof2P6xoe", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 43, "text": "It is difficult to give an exact number of how many lightning strikes occur on Earth each day, as it can vary depending on the weather conditions and the time of year. However, I can give you an estimate based on some general information.\nFirst, it's important to understand that lightning is caused by a buildup of electrical charge between two points, typically between the ground and a cloud. When the electrical potential difference between these two points becomes large enough, the charge flows between them, resulting in a lightning strike.\nSecond, the number of lightning strikes per day is related to the amount of electrical charge that is available in the atmosphere. Generally, there is more electrical charge available during thunderstorms, which tend to occur more frequently in the summer months when the Earth's surface is warmer.\nThird, the number of lightning strikes per day can also depend on the location. Regions with more frequent thunderstorms, such as tropical areas, will tend to have more lightning strikes per day than regions with fewer thunderstorms, such as polar regions.\nBased on these factors, it is estimated that the number of lightning strikes per day is on the order of 100,000,000. However, this is just an estimate and the actual number can vary significantly depending on the weather conditions.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "fpoXWHHdpv7XNBTS2NRY6d", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 44, "text": "The idea of using balloons to lift a house like in the movie \"Up\" is not feasible in real life, as it would require a massive amount of balloons to lift a house, and the materials and technology used in the movie are not available for use in real life. However, let's assume we could use the same technology as in the movie and calculate the number of balloons needed.\n\nFirst, we would need to determine the weight of the house. Let's assume the house weighs 100,000 pounds (45,350 kg).\n\nNext, we would need to determine the lifting power of the balloons. In the movie, the balloons are filled with a special gas that allows them to lift the house. Let's assume we could use the same type of gas and that each balloon has a lifting power of 100 pounds (45 kg).\n\nTo calculate the total lifting power of all the balloons, we would need to determine the number of balloons needed to lift the house. Since the house weighs 100,000 pounds (45,350 kg) and each balloon has a lifting power of 100 pounds (45 kg), we would need 100,000 / 100 = 1,000,000 balloons.\n\nHowever, this is not feasible in real life as it would take an enormous amount of balloons to lift a house, and the materials and technology used in the movie are not available for use in real life.\n\nIn conclusion, it is not possible to use balloons to lift a house like in the movie \"Up\" due to the massive number of balloons required and the lack of available technology.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "GRXWdnzLLzmeZRU67PMTb7", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 45, "text": "I'm sorry, but I cannot provide a specific answer to this question because it is impossible to determine the exact number of text messages sent globally in a minute. There are several reasons why this is the case:\n\n1. Text messages are sent on a massive scale: With billions of people worldwide using mobile phones, it is virtually impossible to accurately track the exact number of text messages sent in a minute.\n2. Many text messages are sent simultaneously: In densely populated areas, many text messages are likely to be sent at the same time, making it difficult to determine the exact number sent in a minute.\n3. Text messages can be sent through various platforms: Text messages can be sent through various platforms, including mobile phones, computers, and other devices, making it difficult to track the exact number sent in a minute.\n4. Many text messages are sent without being recorded: It is not always possible to record or track every text message sent, and many text messages are sent without being recorded or tracked, making it difficult to determine the exact number sent in a minute.\n\nIn conclusion, while it is possible to estimate the number of text messages sent globally in a minute, it is difficult to determine the exact number due to the sheer scale and complexity of text message communication.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "RTxk2jYsjYiiWMwcRjmfwu", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 46, "text": "It is estimated that there are about 100 billion words spoken daily on Earth. Here's a step-by-step explanation of how this estimate was arrived at:\n\n1. The first step is to estimate the number of people on Earth who are capable of speaking. This includes everyone from infants to elderly adults. Based on United Nations data, the world population is about 7.9 billion people.\n2. The next step is to estimate the average number of words spoken per person per day. This can vary widely depending on factors such as culture, language, and individual behavior. However, studies have suggested that the average person speaks between 10,000 and 20,000 words per day.\n3. Multiplying the estimated number of words spoken per person per day by the estimated number of people capable of speaking leads to a very rough estimate of the total number of words spoken per day. Based on the low end of the estimate (10,000 words per day), this would be approximately 79 billion words per day.\n4. However, this estimate is likely to be high, as it assumes that every person speaks at the high end of the range. To account for this, the estimate is adjusted to account for the fact that not everyone speaks at the same rate or in the same way. This adjustment leads to an estimate of about 100 billion words spoken per day.\n\nIt's important to note that this is a very rough estimate, and the actual number of words spoken daily is likely to be higher or lower depending on various factors. However, this estimate provides a rough idea of the scale of the amount of words spoken daily on Earth.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "2J2Et6W8u2kXLTUgfYrPxe", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 47, "text": "The number of snowflakes that fall during a typical winter depends on several factors, such as the location, time of year, weather patterns, and temperature. However, it's difficult to give an exact number, as it can vary greatly.\n\nHere's a rough estimate based on average conditions:\n\n1. Snowflakes form when water vapor in the atmosphere freezes into ice crystals. This process occurs more frequently when the air is cold and humid, and there are sufficient water droplets in the air to form ice crystals.\n2. The average snowflake is about the size of a pinky fingernail, but they can range in size from a small dust particle to several inches in diameter.\n3. Snowflakes are formed in clouds, and the number of snowflakes in a cloud depends on the size and density of the cloud. A typical cloud contains billions of snowflakes.\n4. The average snowfall for a typical winter is about 67 inches, although this can vary greatly depending on the location and weather patterns.\n5. Snowflakes fall to the ground when they come into contact with a surface that is below freezing, such as the ground or a building. The rate at which snowflakes fall can vary depending on the wind speed and direction.\n6. Snowflakes can also stick together to form larger snowflakes or snow clumps, which can make it difficult to estimate the number of individual snowflakes that fall.\n\nOverall, the number of snowflakes that fall during a typical winter is difficult to estimate, as it depends on many factors. However, it's clear that there are a large number of snowflakes in a typical snowfall, and that they play an important role in regulating the Earth's climate and influencing weather patterns.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "nSGoG9Z5g5VuCXgmQJJKrq", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 48, "text": "It is impossible to provide an exact number of pages in all the books ever written, as new books are being published every day and the total number of books in existence continues to grow. Additionally, the definition of a \"book\" can vary depending on the culture and time period, making it difficult to provide an accurate estimate.\n\nHowever, it is possible to estimate the total number of pages in all the books that have ever been published up to a certain point in time. This estimate is based on the assumption that the average book has around 250-300 pages. Using this assumption, it is possible to estimate the total number of pages in all the books ever published.\n\nFirst, we can calculate the total number of books ever published. According to estimates, as of 2021, the total number of books ever published is around 13 million.\n\nNext, we can estimate the average number of pages per book. This number can vary depending on the culture, time period, and type of book, but for the sake of simplicity, let's assume the average book has 250-300 pages.\n\nUsing these assumptions, the total number of pages in all the books ever published would be around:\n\n(13 million books) x (250 pages/book) = 3,250,000,000 pages\n\nThis is an estimate, and the actual number of pages in all the books ever published could be higher or lower depending on the assumptions used. However, this estimate provides a rough idea of the number of pages in all the books ever written.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ScqJdUq9n5bp9qPv5WPqG5", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 49, "text": "It is difficult to estimate the exact number of times the Earth has orbited the sun since the beginning of life, as the exact timing of the emergence of life on Earth is not well-defined. However, we can use some basic calculations to estimate the number of solar orbits that have occurred since life began.\n\nFirst, we need to estimate the age of the Earth. The most widely accepted estimate for the age of the Earth is approximately 4.5 billion years. This means that the Earth has been orbiting the sun for approximately 4.5 billion years / 365.25 days per year = 12,764,606,840 orbital periods of the Earth around the sun.\n\nHowever, this estimate does not take into account the fact that the Earth's orbit around the sun is not a perfect circle, and is slightly elliptical. This means that the distance between the Earth and the sun varies throughout the year, and the speed at which the Earth orbits the sun also varies. To take this into account, we can use the average distance between the Earth and the sun, which is approximately 93 million miles (149.6 million kilometers).\n\nUsing this average distance, we can calculate the time it takes for the Earth to complete one orbit around the sun, which is approximately 365.25 days. Therefore, the actual number of times the Earth has orbited the sun since the beginning of life is approximately 12,764,606,840 / 365.25 = 34,854,571 orbital periods of the Earth around the sun.\n\nIt is important to note that this is an estimate and that the actual number of solar orbits that have occurred since the beginning of life on Earth may be different due to the uncertainties in the timing of the emergence of life and the variations in the Earth's orbit.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "GdLFr88pCwsGpfRBRQQkyh", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 50, "text": "It is difficult to estimate the exact number of songs that have been recorded throughout history, as it is a very large and varied collection of music from different cultures and time periods. However, we can make some educated guesses based on a few factors.\n\nFirstly, it is worth noting that music has been a part of human culture for thousands of years, and songs have been passed down through oral traditions and written documentation for even longer. It is likely that millions, if not billions, of songs have been created and sung throughout history.\n\nSecondly, the invention of recording technology in the late 19th century allowed for the mass production of music recordings. The first commercial recordings were made in the 1880s, and since then, there have been countless recordings made by artists and musicians from all over the world.\n\nThirdly, the rise of digital music in the late 20th century has led to an exponential increase in the number of songs that are available, as well as the ease with which they can be created and shared. This has led to a proliferation of music in the digital age, with millions of songs being released and shared online every year.\n\nIn conclusion, while it is impossible to give an exact number, it is safe to say that there are millions, if not billions, of songs that have been recorded throughout history. The exact number is difficult to estimate due to the vastness and diversity of the music collection, as well as the limitations of our understanding of music history.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Sa7uNEbFGRFuXBAwau8sHS", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 51, "text": "It's difficult to say exactly how the Internet would have been if it had been invented during the Renaissance period, but we can make some educated guesses based on the technological and cultural context of the time.\nFirst, it's important to note that the Internet as we know it today is a product of the digital age, which began in the mid-20th century. The Renaissance period, on the other hand, was a time of great artistic, scientific, and cultural achievement, but technology was more limited and focused on mechanical and optical devices.\nThat being said, it's possible that the Renaissance period's emphasis on art, science, and communication could have led to the development of some early form of the Internet. For example, the printing press, which was invented during the Renaissance, played a key role in the spread of information and ideas. Similarly, the development of the telegraph and telephone in the following centuries laid the groundwork for the modern Internet.\nIt's also possible that the Renaissance period's focus on individualism and humanism could have led to a more decentralized and diverse Internet, with a greater emphasis on personal expression and creativity. However, the lack of modern computer technology and the Internet's reliance on digital communication would likely have limited its potential.\nUltimately, it's impossible to say exactly how the Internet would have evolved if it had been invented during the Renaissance period, but it's likely that it would have been very different from the Internet we know today.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "T6Ar3cAEw3AmVVQTcCPp8N", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 52, "text": "If the Aztecs had successfully repelled the Spanish conquistadors, it is likely that the history of Mexico and the Americas would have been very different. The Aztecs were a powerful and sophisticated civilization that dominated much of Mesoamerica at the time of the Spanish conquest. They had a complex society with a strong military, a highly developed calendar system, and a sophisticated system of governance.\n\nIf the Aztecs had been able to repel the Spanish conquistadors, it is likely that they would have continued to flourish as a civilization. They may have been able to resist the spread of European diseases, such as smallpox and influenza, which had a devastating impact on the indigenous population. They may have also been able to maintain their independence and continue to thrive as a sovereign nation.\n\nOn the other hand, if the Spanish had been defeated, it is possible that the history of the Americas would have been very different. The Aztecs may have been able to maintain their own cultural and religious practices, and the spread of Christianity may have been slowed or stopped. The economy of Mexico may have been different, and the country may have developed differently in terms of trade and commerce.\n\nIt is also possible that the Aztecs may have been able to form alliances with other indigenous groups in the region, and together they may have been able to resist further European colonization. In any case, the history of the Americas would have been shaped by the actions and choices of the Aztec people and their leaders.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "KiZQev5JEk2h6JYeQnFmtM", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 53, "text": "The Black Death, also known as the Plague, was a pandemic that devastated Europe in the 14th century. It is estimated that around 25-30% of the population of Europe died from the disease between 1347 and 1351. If the Black Death had not occurred, it is difficult to predict exactly how history would have unfolded, but it is likely that Europe would have had a different demographic, economic, and social structure.\n\nWithout the devastating effects of the Black Death, it is possible that Europe would have continued to experience population growth and urbanization, leading to a more prosperous and dynamic economy. The labor shortage caused by the high mortality rate could have led to wage increases and improvements in working conditions, which could have spurred innovation and economic growth. The absence of the Black Death could also have prevented the disruption of trade and the decline of the feudal system, leading to a different political and social structure.\n\nOn the other hand, the absence of the Black Death could have also led to the spread of other diseases, which could have had a negative impact on the population and the economy. In addition, the lack of the Black Death could have allowed for the continued spread of the bubonic plague, which could have led to even greater devastation in the future.\n\nIn conclusion, it is difficult to predict exactly how history would have unfolded if the Black Death had not occurred, but it is likely that Europe would have been significantly different in terms of demography, economy, and society.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "cYiyYKKXM3GXkrZHAbX83S", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 54, "text": "Isaac Newton is widely regarded as one of the most influential scientists in history for his work in physics, particularly for his development of the laws of motion and universal gravitation. However, it is worth noting that Newton also made significant contributions to other fields, including mathematics, optics, and natural philosophy.\n\nIf Newton had decided to focus on biology instead of physics, it is possible that he would have made significant contributions to the field. However, it is difficult to say exactly what those contributions would have been without knowing more about his specific interests and areas of focus.\n\nNewton was a prolific scientist and wrote extensively on a wide range of topics, including natural philosophy, mathematics, and theology. He also conducted extensive research in optics, including the study of light and color, and made important contributions to the field of mathematics, including the development of calculus.\n\nIt is possible that if Newton had focused on biology, he would have made significant contributions to the understanding of living organisms and their behavior. However, it is also possible that his work in physics and mathematics would have had a greater impact on the world.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "PQmMUdAAcBsAWmWaTvdHSU", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 55, "text": "It's difficult to say exactly what would have happened if the Beatles had never formed as a band, as their impact on music and culture was so significant. However, here are a few possibilities:\n\n1. The Beatles' individual members may have pursued solo careers instead. John Lennon, Paul McCartney, George Harrison, and Ringo Starr all went on to successful solo careers after the band's breakup.\n2. Another band may have risen to fame in their place. The Beatles' success paved the way for many other bands and musicians, and it's possible that another group may have filled the void they left.\n3. The music industry may have evolved differently. The Beatles were pioneers in many ways, and their innovative approach to music and recording may have influenced the direction of the industry.\n4. The cultural impact of the Beatles may have been lessened. The Beatles were not just a musical group, but also a cultural phenomenon. Their influence on fashion, hairstyles, and attitudes towards music and society was widespread.\n5. The Beatles' music may have been forgotten. While it's difficult to imagine, it's possible that the Beatles' music may not have had the same lasting impact if they had never existed. Their songs have become a part of the fabric of popular culture and are still widely loved and played today.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "PorExChQ9VeYsPJptdgtsB", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 56, "text": "If Alan Turing had not cracked the Enigma code during World War II, it is possible that the outcome of the war could have been different. The Enigma code was a complex encryption system used by the German military to communicate securely, and Turing's work on breaking the code was instrumental in the Allied victory.\n\nWithout Turing's efforts, the Allies may have struggled to decipher German communications and gain valuable insights into their plans and strategies. This could have led to a different outcome in various battles and campaigns, and the war could have lasted longer or ended in a different outcome.\n\nIt's also possible that the Allies may have had to develop alternative strategies and technologies to defeat the Germans, such as investing more in code-breaking efforts or developing new weapons and tactics. This could have had significant consequences for the war and its outcome.\n\nHowever, it's difficult to say for certain how the war would have turned out without Turing's contributions, as there were many factors that influenced the outcome of the conflict. Nevertheless, Turing's work on the Enigma code was a crucial factor in the Allied victory and his contributions to the war effort were significant.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "249f6dSMwZRZVMmtxv6yDm", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 57, "text": "The Suez Canal is a man-made waterway that connects the Mediterranean Sea and the Red Sea, allowing ships to travel between Europe and Asia without having to go all the way around Africa. If the Suez Canal had never been constructed, ships would have had to take a much longer and more treacherous route around the Cape of Good Hope at the southern tip of Africa, which would have made international trade much more difficult and expensive.\n\nWithout the Suez Canal, it would have taken much longer and been more dangerous for ships to travel between Europe and Asia, which would have had a significant impact on international trade and global economic development. The canal allowed for faster and more efficient transportation of goods between the two continents, which led to increased trade and economic growth. It also made it easier for European powers to establish colonies and trade routes in Asia, which had a major impact on the history of the region.\n\nAdditionally, the construction of the Suez Canal was a major engineering feat that required significant resources and technological advancements, which had a lasting impact on the development of engineering and technology. The canal also played a key role in the political and strategic interests of various nations, and its control and use have been the subject of international conflicts and disputes.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "nxa3m6kiAZwKgcMUBY8KYz", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 58, "text": "It is difficult to say for certain how the world would be different if the Maya civilization had never mysteriously collapsed. However, it is likely that the Maya would have continued to thrive and develop as a civilization. They were a highly advanced civilization with skills in mathematics, astronomy, and architecture, and they had a complex system of writing and religion. They also had a strong economy and were able to establish a large empire that spanned much of Mesoamerica.\n\nIf the Maya civilization had continued to thrive, it is possible that their advanced knowledge and technologies would have been passed down to future generations and could have had a major impact on the development of other civilizations. They may have made further scientific and technological advancements, such as discovering new medicines, building more efficient transportation systems, or developing new forms of communication.\n\nIt is also possible that the Maya civilization would have continued to expand and influence other civilizations in the region, leading to a different political and cultural landscape in Mesoamerica. The Maya civilization had a long and complex history, and it is difficult to predict exactly how events would have unfolded if their collapse had been prevented. However, it is clear that the Maya civilization had a lasting impact on the region and their legacy can still be seen in the ruins of their cities and the traditions of their descendants.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "DXFvhjCaKqQiBsFDCkXGMT", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 59, "text": "If Christopher Columbus had not discovered the Americas, it is likely that the history of the world would have been significantly different. Columbus's discovery of the Americas marked the beginning of European colonization and the subsequent spread of European culture, religion, and technology to the Americas.\n\nWithout Columbus's discovery, it is possible that the indigenous peoples of the Americas would have continued to thrive and develop on their own, without significant outside influence. The Americas would have remained largely isolated from the rest of the world, and would have evolved differently from the way they did in our history.\n\nIt's also possible that other European explorers would have made the discovery of the Americas eventually, as the continent was clearly visible from the sea. However, it's uncertain how and when this would have happened, and what the impact would have been on the indigenous peoples and the world.\n\nUltimately, the discovery of the Americas by Christopher Columbus had a profound impact on the world, for better or for worse, and it is impossible to know for certain how things would have turned out without his discovery.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "BCUK5VcpZK26jMJ2UMgR3E", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 60, "text": "Vincent van Gogh is widely considered one of the most influential and celebrated painters in the history of art, despite the fact that he struggled financially during his lifetime and only achieved recognition after his death. If he had been a successful artist during his lifetime, it is likely that he would have continued to create and innovate in his art, perhaps exploring new techniques or styles. He may have also been able to sell more of his works and earn a stable income, which could have changed the course of his life and career. It is also possible that his fame and success may have come with a different kind of pressure and scrutiny, which could have affected the direction of his art and his personal life. Ultimately, it is impossible to know for sure how things would have turned out, but it is clear that Vincent van Gogh's legacy and impact on the art world would still be significant even if he had achieved success during his lifetime.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "JHTKBrLVTp7bv5zW2uwYVW", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 61, "text": "Sure, here's an example C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file:\n\n```cpp\n#include <iostream>\n#include <fstream>\n#include <string>\n\nusing namespace std;\n\nint main()\n{\n    // Specify the name of the text file you want to read\n    string filename = \"example.txt\";\n\n    // Open the file in binary mode for reading\n    ifstream file(filename, ios::binary);\n\n    // Set the string to search for\n    string search_string = \"example\";\n\n    // Initialize a counter to keep track of the number of occurrences of the search string\n    int counter = 0;\n\n    // Start reading the file line by line\n    string line;\n    while (getline(file, line))\n    {\n        // Check if the line contains the search string\n        if (line.find(search_string)!= string::npos)\n        {\n            // Increment the counter if the line contains the search string\n            counter++;\n        }\n    }\n\n    // Close the file\n    file.close();\n\n    // Print the number of occurrences of the search string\n    cout << \"Number of occurrences of search string: \" << counter << endl;\n\n    return 0;\n}\n```\n\nThis program reads the specified text file line by line, and uses a binary `ifstream` object to read the file in binary mode. The program uses a counter to keep track of the number of occurrences of a specified search string in the file. The `getline` function is used to read each line of the file, and the `find` function is used to check if the line contains the search string. If the line contains the search string, the counter is incremented. Finally, the program prints the number of occurrences of the search string.\nOf course, you'll need to specify the name of the text file you want to read in the `filename` variable. You can modify the `search_string` variable to specify the string you want to search for in the file.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "jyzD2deb9JAffRhhkk37r5", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 62, "text": "Here's an implementation of the Longest Common Subsequence (LCS) problem using dynamic programming in Python:\n\n```python\ndef lcs(str1, str2):\n    # Initialize dp table with maximum values\n    m, n = len(str1), len(str2)\n    dp = [[float('inf') for j in range(n+1)] for i in range(m+1)]\n    for i in range(m+1):\n        dp[i][0] = float('inf')\n    for j in range(n+1):\n        dp[0][j] = float('inf')\n\n    # Fill in dp table\n    for i in range(1, m+1):\n        for j in range(1, n+1):\n            if str1[i-1] == str2[j-1]:\n                dp[i][j] = dp[i-1][j-1]\n            else:\n                dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1] + (str1[i-1] == str2[j-1]))\n\n    return dp[m][n]\n\n# Example usage\nstr1 = \"banana\"\nstr2 = \"ananana\"\nprint(lcs(str1, str2)) # prints 5\n```\n\nIn this implementation, we use a 2D dynamic programming table `dp` to store the LCS of substrings of `str1` and `str2`. The maximum value in each cell of `dp` represents the length of the LCS of the corresponding substrings. We initialize the table with maximum values, except for the first row and column, which are set to `float('inf')`.\nWe then fill in the table by iterating over the substrings of `str1` and `str2` in a sliding window fashion, and using the three possible options for each cell: the value of the previous cell in the same row or column, the value of the previous cell in the same row or column of the other string, or the sum of these two values plus one if the current substring and the previous substring match.\nOnce we have filled in the entire table, we return the maximum value in the last cell, which represents the LCS of the entire strings.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "jPGHEfp7FxhDyvRA6qEN3j", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 63, "text": "Here is a regular expression that can be used to validate an email address in Python:\n```\nimport re\n\n# Define the regular expression\nemail_regex = re.compile(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$')\n\n# Test the regular expression\nemail = \"user@example.com\"\nif email_regex.match(email):\n    print(\"Email address is valid.\")\nelse:\n    print(\"Email address is not valid.\")\n```\nThis regular expression matches the following patterns:\n\n* One or more alphabetical characters (`[a-zA-Z]`), followed by a domain name, which must start and end with an alphanumeric character and may contain any combination of alphanumeric characters and the following special characters: `-` `.` `_` `%` `+` `*` `?` `@`.\n* The `@` symbol, followed by one or more alphanumeric characters, which may include the special characters listed above.\n* The domain name must not start or end with a `:` character.\n* The entire email address must end with a top-level domain of two or more alphanumeric characters.\n\nThis regular expression is not perfect, as it can be easily bypassed by spammers and does not check if the domain name actually exists. However, it can be a useful starting point for validating email addresses in many cases.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Ra9eTcAuNZPkPRnaHiQrak", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 64, "text": "Here is an example of a program in Python that uses dynamic programming to find the nth Fibonacci number:\n```\ndef fibonacci(n):\n    # Initialize memoization table with 0s\n    memo = [[0] * (n + 1) for _ in range(n + 1)]\n    \n    # Base case: n = 0 or n = 1\n    if n <= 1:\n        return [0, 1]\n    \n    # Recursive case\n    else:\n        return fibonacci(n-1) + fibonacci(n-2)\n    \n# Test the function with a specific number of terms\nfor i in range(10):\n    print(fibonacci(i+1))\n```\nThis program defines a function `fibonacci` that takes an integer `n` as input and returns the `n`th Fibonacci number. The function uses dynamic programming to memoize the values of the previous terms in the sequence, so that it doesn't have to recalculate them every time. The function starts by initializing the memoization table with 0s, which means the first term of the sequence is 0. Then it checks if the input `n` is 0 or 1, and if it is, it returns 0 and 1. If the input `n` is greater than 1, it calls the function recursively twice, with inputs `n-1` and `n-2`, and returns the sum of those two values. This way it calculates the nth Fibonacci number by adding the n-1 and n-2 terms together.\n\nNote: The program above will work fine for small values of n, but for large values of n, it will take a lot of memory and time. A more efficient way to calculate the nth Fibonacci number is using Binet's formula.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "5mbEFLEnEx8Cm2wEF8WAbM", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 65, "text": "Here is an example of a binary search algorithm in Python:\n```\ndef binary_search(arr, target):\n    start = 0\n    end = len(arr) - 1\n    while start <= end:\n        mid = (start + end) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            start = mid + 1\n        else:\n            end = mid - 1\n    return -1\n\narr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\ntarget = 4\nprint(binary_search(arr, target)) # output: 4\n```\nThe algorithm starts by initializing the start and end indices of the array to 0 and the length of the array -1, respectively. It then enters a while loop that continues as long as the start index is less than or equal to the end index.\n\nOn each iteration of the loop, it calculates the middle index of the array and compares the value at that index to the target value. If the values are equal, the algorithm returns the middle index. If the value at the middle index is less than the target, the algorithm updates the start index to be the middle index +1, and the end index to be the middle index -1. If the value at the middle index is greater than the target, the algorithm updates the end index to be the middle index -1 and the start index to be the middle index +1.\n\nIf the end index is less than the start index, the algorithm returns -1 to indicate that the target value is not present in the array.\n\nThis algorithm has a time complexity of O(log n) and is most efficient when used with a sorted array.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "KLbBzjbuwq4agAESQ9Wfpq", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 66, "text": "Here is an implementation of a queue using two stacks in Python:\n\n```cpp\nclass Queue:\n    def __init__(self):\n        self.in_stack = []\n        self.out_stack = []\n    \n    def enqueue(self, item):\n        self.in_stack.append(item)\n        self.out_stack.clear()\n    \n    def dequeue(self):\n        if not self.in_stack:\n            raise EmptyQueueError\n        item = self.in_stack.pop()\n        self.out_stack.append(item)\n        return item\n    \n    def peek(self):\n        if not self.in_stack:\n            raise EmptyQueueError\n        return self.in_stack[-1]\n    \n    def is_empty(self):\n        return not self.in_stack\n\nclass EmptyQueueError(Exception):\n    pass\n```\n\nIn this implementation, the `in_stack` and `out_stack` are used as the underlying data structure to implement the queue. The `enqueue` method adds an item to the end of the `in_stack` and clears the `out_stack`. The `dequeue` method removes an item from the front of the `in_stack` and adds it to the end of the `out_stack`. The `peek` method returns the item at the front of the `in_stack`. The `is_empty` method checks if the `in_stack` is empty. If the `in_stack` is empty, the `is_empty` method raises an `EmptyQueueError`.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "ganAwetd9VTzEAiTtsskzX", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 67, "text": "To find the common elements in two arrays without using any extra data structures, you can use an iterative approach. Here's one possible solution in Python:\n\ndef find\\_common\\_elements(arr1, arr2):\nresult = []\nfor item1 in arr1:\nfor item2 in arr2:\nif item1 == item2:\nresult.append(item1)\nreturn result\n\nHere's an example of how you could use this function:\n\narr1 = [1, 2, 3, 4, 5]\narr2 = [3, 4, 5, 6, 7]\n\nprint(find\\_common\\_elements(arr1, arr2))\n\nThis should output `[3, 4, 5]`. The function iterates over each item in both arrays, and if it finds a match, it adds the item to the `result` list. If there are no matches, it returns an empty list.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "LG6szQ3cqNRzUjXtZtXG94", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 68, "text": "To find the value of f(2), we need to substitute 2 for x in the function f(x).\n\nf(x) = 5x^3 - 2x + 3\n\nWhen x = 2, we have:\n\nf(2) = 5(2)^3 - 2(2) + 3 = 5 \\* 8 - 2 + 3 = 41 - 2 + 3 = 40\n\nSo the value of f(2) is 40.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "LHMnXGbSZubeZJcr5fgrte", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 69, "text": "We can solve this equation using the following steps:\n\n1. Simplify the left-hand side (LHS) of the equation by moving all the terms to one side and the numbers to the other: 3x + 10 = 5x - 10\n2. Combine the terms on the left-hand side by subtracting 5x from both sides: 3x = -5x\n3. Simplify the left-hand side by dividing both sides by 3: x = -5/3\n4. Take the natural logarithm of both sides to get the solution in terms of the natural logarithm base e: ln|x| = ln|-5/3| = -5/3 ln|e| = -5/3 ln(1.414) = -5/3 ln(1.414) = -5/3 ln(1 + 1/1.414) = -5/3 ln(1.414)\n5. Exponentiate both sides to get the solution in terms of e: x = e^(-5/3)\n6. Rearrange the solution to get x in terms of Euler's number: x = e^(-5/3) = 1/1.414^(-5/3) = 0.5877\n7. Round the solution to the desired number of decimal places: x = 0.5877\n\nTherefore, x = 0.5877 is the solution to the equation 3x + 10 = 5(x - 2).", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "TGENVWL5iAYi4NCf5jAzKy", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 70, "text": "The length of the line segment is the distance between the two points. To find the length, you can use the Pythagorean theorem, which states that the sum of the squares of the distances from a point to the two endpoints is equal to the square of the distance between the two endpoints.\n\nIn this case, the distance from the point (2, -2) to the point (10, 4) is given by the formula:\n\nsqrt((x2 - x1)^2 + (y2 - y1)^2)\n= sqrt((10 - 2)^2 + (4 - (-2))^2)\n= sqrt(18^2 + 4^2)\n= sqrt(324 + 16)\n= sqrt(340)\n= 17.973\n\nSo the length of the line segment is 17.973.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "kc9oytarvqyNVVfGZz93EL", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 71, "text": "Certainly, here's an example of a formal email proposing a joint venture:\n\nSubject: Proposal for Joint Venture\n\nDear [Name of Partner],\n\nI hope this email finds you well. I am writing to propose a joint venture between our companies, [Your Company Name] and [Partner Company Name]. We believe that by working together, we can achieve great success and mutually benefit from the partnership.\n\nOur company specializes in [Your Company's Expertise], while your company is a leader in [Partner Company's Expertise]. We believe that by combining our strengths and capabilities, we can create a powerful partnership that can bring innovative solutions to the market.\n\nWe propose that we establish a new company that will be jointly owned by our two companies. This new company will have its own management team, and will operate independently from our respective parent companies. We will work together to identify new business opportunities, and share the profits and losses of the new company equally.\n\nWe believe that this partnership will be highly beneficial for both of our companies. It will give us access to new markets, new customers, and new technologies. It will also allow us to share the risks and costs of new ventures, and to leverage our resources and expertise to achieve greater success.\n\nWe would like to schedule a meeting to discuss this proposal in more detail, and to explore how we can work together to achieve our mutual goals. We believe that this partnership has the potential to be a great success, and we are excited about the opportunities that it can bring.\n\nPlease let us know if you are available to meet in the next [X] days, and we will arrange a convenient time and place.\n\nThank you for considering our proposal. We look forward to hearing from you soon.\n\nBest regards,\n\n[Your Name]\n[Your Title]\n[Your Company Name]", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "NxstpvVEnGMny3jECtvrw2", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 72, "text": "Yes, I can help you write a resignation letter. Here is a sample letter you can use as a guide:\n\nDear [Employer],\n\nI am writing to inform you of my decision to resign from my position as [Position] at [Company]. I have truly appreciated the opportunities and experiences that I have had during my time here, and I am grateful for the support and guidance you and the team have provided me.\n\nI have accepted a new position at a different company, and I will be leaving on [date]. I am confident that the skills and experience I have gained during my time at [Company] will serve me well in my future endeavors.\n\nI want to assure you that I will do everything I can to ensure a smooth transition and to complete any outstanding tasks before my departure. I would be happy to discuss any specific needs or concerns you may have regarding this transition.\n\nAgain, I want to express my sincere appreciation for the opportunities and support provided to me during my time at [Company]. I am grateful for the friendships and connections I have made here, and I will always look back on my time here with fond memories.\n\nThank you for the opportunity to work with such a great team, and I wish you and the company continued success in the future.\n\nSincerely,\n\n[Your name]", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "XN9NBpeQJiQqGT5UYkys7N", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 73, "text": "[Your Name]\n[Your Title]\n[Your Affiliation]\n[Date]\n[Recipient\u2019s Name]\n[Recipient\u2019s Title]\n[Recipient\u2019s Affiliation]\n[Address]\nDear [Recipient\u2019s Name],\nI am writing to wholeheartedly recommend [Student\u2019s Name] for admission to the prestigious graduate program in computer science at [University Name]. As [Student\u2019s Name]\u2019s professor and academic advisor, I have had the pleasure of observing their academic prowess, intellectual curiosity, and exceptional character.\nIn my [Number] years of teaching, I have never encountered a student as dedicated and passionate about computer science as [Student\u2019s Name]. They consistently demonstrate exceptional aptitude in programming, algorithms, and data structures, and have shown a keen interest in artificial intelligence and machine learning. [Student\u2019s Name] has also shown great initiative in exploring new areas of research and has made significant contributions to the field through their work on [specific projects or research].\nIn addition to their academic excellence, [Student\u2019s Name] is an individual of exceptional character and integrity. They have consistently demonstrated leadership, teamwork, and collaboration skills, working effectively with their peers and mentors to achieve common goals. Their positive attitude and willingness to help others has made them a valuable member of the academic community.\nI am confident that [Student\u2019s Name] will make a valuable contribution to the graduate program in computer science at [University Name]. Their academic achievements, passion for computer science, and exceptional character make them an ideal candidate for this prestigious program.\nThank you for your consideration of my recommendation. I am confident that [Student\u2019s Name] will excel in their studies and make a significant contribution to the field of computer science.\nSincerely,\n[Your Signature]\n[Your Name]\n[Your Title]\n[Your Affiliation]", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "3qMqAZcfHUVUTEJnMXT8Pw", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 74, "text": "Subject: Introducing our latest innovation: [Software Name]\n\nDear valued customers,\n\nWe are proud to announce the launch of our new software solution, designed to revolutionize [industry/field] with its cutting-edge technology and user-friendly interface.\n\n[Software Name] is the result of years of research and development, and we believe it will bring significant improvements to [specific benefits or advantages]. With its advanced features and customizable options, it is the perfect solution for [target audience or market].\n\nHere are just a few of the key features of [Software Name]:\n\n* [Feature 1]\n* [Feature 2]\n* [Feature 3]\n* [Feature 4]\n* [Feature 5]\n\nWe invite you to experience the benefits of [Software Name] for yourself. Our team is available to answer any questions you may have and to help you get started.\n\nTo learn more and to schedule a demo, please visit our website or contact us directly.\n\nThank you for your continued support and for choosing [company name]. We look forward to serving you with our latest innovation.\n\nBest regards,\n[Your name]\n[Company name]", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "hMXjd228Mc8pnc7zgd2frG", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 75, "text": "Subject: Apology and Update on Order Delay\n\nDear [Customer Name],\n\nI am writing to apologize for the delay in the delivery of your order. I understand that this has caused inconvenience and frustration, and I want to assure you that we take this matter very seriously.\n\nWe have thoroughly investigated the issue and have identified the cause of the delay. We want to assure you that the issue has been resolved, and we are taking steps to prevent similar delays in the future.\n\nWe understand the importance of your order and the trust that you have placed in us. We want to assure you that we are committed to providing you with the highest level of service and quality products.\n\nAgain, please accept our sincere apologies for the delay and any inconvenience it may have caused. If you have any further concerns or questions, please do not hesitate to contact us.\n\nSincerely,\n\n[Your Name]", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "gUEd9nNepqy8nsfbypscHx", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 76, "text": "Hello and welcome to our YouTube video on the history and cultural significance of jazz.\nJazz is a genre of music that originated in New Orleans in the late 19th and early 20th centuries. It is characterized by its improvisational nature, blues and swing rhythms, and a mixture of different musical traditions, including African rhythms, European classical music, and folk music.\nThe birth of jazz is often attributed to the legendary cornet player, Buddy Bolden, who is said to have played a key role in shaping the sound and style of early jazz. However, it was Louis Armstrong, who is considered the \"Father of Jazz,\" who popularized the genre and brought it to the world.\nJazz has had a profound influence on American culture and has been an important part of the country's musical heritage. It has also played a significant role in shaping the world's music and has been a major influence on many other genres, including rock and roll, hip hop, and R&B.\nJazz has also been a crucial part of the civil rights movement in the United States. The music has often been used as a form of protest and has been associated with the fight for equality and justice.\nThroughout its history, jazz has been shaped and influenced by a wide range of musicians and cultural movements. Today, it continues to evolve and remains an important and influential genre of music, both in the United States and around the world.\nIn conclusion, jazz is a genre of music that has had a rich history and continues to be an important part of American and world culture. Its improvisational nature, blues and swing rhythms, and mixture of different musical traditions have made it a unique and beloved genre of music, and one that will continue to be enjoyed for generations to come.\nThanks for watching, and we hope you learned something new about the history and cultural significance of jazz.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "nZZyn3SAvU8T2BVR73LMHC", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 77, "text": "Aloha, fellow travelers! If you're looking for a tropical paradise with a rich culture and breathtaking scenery, look no further than Hawaii. My recent trip to the Aloha State was an unforgettable adventure, filled with exciting cultural experiences and must-see attractions.\nFirst and foremost, I was blown away by the warm and welcoming spirit of the Hawaiian people. From the moment I arrived, I felt at home and was greeted with open arms by the locals. Whether it was the hotel staff, my tour guide, or the friendly strangers I met on the street, everyone I encountered was eager to share their love for their home state.\nOne of the most memorable cultural experiences I had was attending a traditional Hawaiian luau. At this festive event, I was treated to a delicious feast of local cuisine, including poi, laulau, and kalua pig, all while listening to the hypnotic beat of the hula. The dancers, dressed in beautiful native garb, told stories of the islands and their history through their movements. It was truly a magical evening that allowed me to connect with the rich heritage of Hawaii.\nAnother must-see attraction that left me in awe was the breathtaking beauty of Haleakala National Park. The park boasts a dormant volcano, the largest on the island, that offers a stunning sunrise or sunset hike. As the sun rose over the horizon, I was struck by the vibrant colors and stark beauty of the landscape. It was an experience that truly humbled me, and one that I will never forget.\nOf course, no trip to Hawaii would be complete without spending some time at the beach. With its crystal-clear waters, golden sand, and gentle waves, Wailea Beach was the perfect spot to relax and soak up the sun. Whether you're an avid surfer, a beach bum, or simply looking to unwind, Hawaii's beaches are sure to steal your heart.\nIn conclusion, Hawaii was a true paradise that exceeded all of my expectations. From the moment I stepped off the plane, I was immersed in a culture that was rich, vibrant, and full of life. With so many must-see attractions and unforgettable experiences, Hawaii is the perfect destination for any traveler looking for a little bit of adventure, culture, and relaxation. Mahalo for reading, and I hope to see you in Hawaii soon!", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "YM4qjUM5VfmWnzM3HXRB4G", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 78, "text": "\"Beyond the Horizon\" is a thrilling science fiction film that takes you on a journey through time and space. The movie follows the story of a group of astronauts who are sent on a mission to a distant planet to search for signs of alien life.\n\nThe plot is fast-paced and keeps you on the edge of your seat from start to finish. The special effects are out of this world, with stunning visuals that transport you to a world beyond our own. The characters are well-developed and you easily become invested in their fate.\n\nThe cast is led by a charismatic actor who brings a sense of determination and courage to his role as the leader of the mission. The supporting cast is equally impressive, with each actor bringing their own unique personality to their character.\n\nOverall, \"Beyond the Horizon\" is a must-see movie for science fiction fans and anyone looking for a captivating and thrilling film. With its engaging plot, well-developed characters, and stunning special effects, this movie is sure to leave you with a sense of wonder and awe. So, grab some popcorn and get ready to blast off on an interstellar adventure!", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "WcbzNkzu5q66QkK92NxmGB", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 79, "text": "Opening:\n\"Hey there, welcome to this episode of Sound Matters, where we explore the many facets of the music industry. Today, we're going to be talking about the influence of streaming platforms on the music industry. Whether you're a fan, an artist, or a industry insider, it's hard to deny the massive impact that streaming platforms like Spotify, Apple Music, and Tidal have had on the way we consume and discover music. So, let's dive in and talk about how streaming platforms have changed the game.\"\n\nSegment 1: The Pros of Streaming Platforms\n\n* Increased access to music\n* Convenience and ease of use\n* Greater exposure for artists\n* Revenue streams for artists\n\nSegment 2: The Cons of Streaming Platforms\n\n* Decreased album sales and revenue for artists\n* Artists not being paid fairly\n* Piracy and copyright issues\n* Mental health and addiction concerns\n\nSegment 3: The Future of Streaming Platforms\n\n* Where the industry is headed\n* Potential changes and improvements\n* The role of streaming platforms in the future\n\nClosing:\n\"That's all for this episode of Sound Matters. We hope you enjoyed the discussion on the influence of streaming platforms on the music industry. As always, thank you for tuning in. Until next time, keep listening and keep loving the music.\"", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
-{"answer_id": "Tfm4f2mbpKJsa8MT6exs8D", "model_id": "vicuna-13b:20230322-clean-lang", "question_id": 80, "text": "The concert was a true spectacle of sound, featuring a talented orchestra that performed with precision and passion. The conductor lead the ensemble with skill and finesse, bringing out the full range of emotion in each piece.\n\nFrom the opening notes of the first piece, it was clear that the orchestra was in top form. The strings were rich and full-bodied, the woodwinds were crisp and clear, and the brass was powerful and commanding. The overall sound was dynamic and engaging, keeping the audience on the edge of their seats throughout the performance.\n\nOne of the standout pieces of the evening was the second movement of Dvorak's Symphony No. 9, which was performed with a deep sense of longing and yearning. The orchestra's playing was heartfelt and moving, evoking a sense of nostalgia and wistfulness in the audience.\n\nThe final piece of the concert was a rousing performance of Tchaikovsky's 1812 Overture, complete with cannon fire and bells. The orchestra played with gusto and energy, bringing the audience to their feet in a standing ovation.\n\nOverall, the concert was a fantastic experience for both the orchestra and the audience. The orchestra played with passion and precision, and the audience was transported by the music. The performance was a true celebration of the power of music, and it left a lasting impression on all who were fortunate enough to be in attendance.", "metadata": {"huggingface_argument": {"do_sample": true, "temperature": 0.7, "max_new_tokens": 1024}}}
diff --git a/llava/eval/table/caps_boxes_coco2014_val_80.jsonl b/llava/eval/table/caps_boxes_coco2014_val_80.jsonl
deleted file mode 100644
index a0130f30..00000000
--- a/llava/eval/table/caps_boxes_coco2014_val_80.jsonl
+++ /dev/null
@@ -1,80 +0,0 @@
-{"id": "000000296284", "image": "000000296284.jpg", "captions": ["A donut shop is full of different flavors of donuts.", "Fruit flavored donuts lined up in a glass fronted cabinet", "A rack with some doughnuts in a glass case.", "A display case in a bakery filled with donuts.", "An assortment of doughnuts are arranged in a display case."], "instances": [{"category": "donut", "bbox": [0.37, 0.584, 0.504, 0.709]}, {"category": "donut", "bbox": [0.369, 0.22, 0.492, 0.317]}, {"category": "donut", "bbox": [0.471, 0.587, 0.639, 0.706]}, {"category": "donut", "bbox": [0.544, 0.213, 0.679, 0.316]}, {"category": "donut", "bbox": [0.035, 0.22, 0.196, 0.328]}, {"category": "donut", "bbox": [0.054, 0.608, 0.221, 0.711]}, {"category": "donut", "bbox": [0.283, 0.586, 0.429, 0.708]}, {"category": "donut", "bbox": [0.466, 0.226, 0.585, 0.32]}, {"category": "donut", "bbox": [0.28, 0.232, 0.393, 0.322]}, {"category": "donut", "bbox": [0.0, 0.609, 0.097, 0.722]}]}
-{"id": "000000151358", "image": "000000151358.jpg", "captions": ["A newspaper that has sunglasses on top of it sitting in front of books.", "an apple sunglasses books and a teddy bear", "A folded newspaper and sunglasses are on a table with an apple, books, and teddy bear behind.", "An apple sitting on a table next to sunglasses and a news paper.", "There are sunglasses laying on the folded newspaper."], "instances": [{"category": "tie", "bbox": [0.258, 0.074, 0.527, 0.589]}, {"category": "apple", "bbox": [0.621, 0.482, 0.853, 0.645]}, {"category": "book", "bbox": [0.154, 0.107, 0.275, 0.59]}, {"category": "book", "bbox": [0.535, 0.09, 0.735, 0.583]}, {"category": "book", "bbox": [0.051, 0.112, 0.159, 0.6]}, {"category": "teddy bear", "bbox": [0.753, 0.084, 1.0, 0.517]}, {"category": "book", "bbox": [0.681, 0.097, 0.796, 0.483]}, {"category": "book", "bbox": [0.443, 0.099, 0.574, 0.588]}, {"category": "book", "bbox": [0.267, 0.337, 0.386, 0.579]}]}
-{"id": "000000052312", "image": "000000052312.jpg", "captions": ["The old man literally has a toothbrush mustache.", "An old man with a tooth brush head under his nose, mimicking Hitler", "A man wearing a toothbrush for a moustache.", "A man with the head of a toothbrush under his nose like a mustache", "An elderly man wearing the head of a toothbrush as a moustache."], "instances": [{"category": "toothbrush", "bbox": [0.345, 0.59, 0.594, 0.679]}, {"category": "person", "bbox": [0.0, 0.03, 1.0, 0.99]}]}
-{"id": "000000473210", "image": "000000473210.jpg", "captions": ["two people taking apart their wii controllers to replace batteries", "People taking apart video game remote controls on a table", "People handling a couple of remotes taking them apart.", "two sets of hands a wooden table and two controllers", "Two people who are taking apart a video game controller."], "instances": [{"category": "person", "bbox": [0.002, 0.334, 0.453, 0.986]}, {"category": "remote", "bbox": [0.407, 0.207, 0.727, 0.604]}, {"category": "remote", "bbox": [0.088, 0.344, 0.313, 0.547]}, {"category": "laptop", "bbox": [0.001, 0.049, 0.1, 0.197]}, {"category": "person", "bbox": [0.484, 0.254, 0.998, 0.985]}, {"category": "dining table", "bbox": [0.0, 0.003, 1.0, 0.956]}]}
-{"id": "000000097131", "image": "000000097131.jpg", "captions": ["A car parked by a parking meter in front of a building.", "A car is sitting parked at a curb in front of a parking meter.", "A black car on the street next to a parking meter.", "A gray car parked in front of two parking meters.", "A black car parked on the side of the road."], "instances": [{"category": "car", "bbox": [0.227, 0.362, 0.946, 0.761]}, {"category": "car", "bbox": [0.793, 0.322, 0.88, 0.4]}, {"category": "car", "bbox": [0.0, 0.447, 0.028, 0.726]}, {"category": "parking meter", "bbox": [0.156, 0.35, 0.186, 0.453]}, {"category": "truck", "bbox": [0.907, 0.331, 1.0, 0.408]}, {"category": "parking meter", "bbox": [0.188, 0.349, 0.218, 0.448]}]}
-{"id": "000000543364", "image": "000000543364.jpg", "captions": ["There is a table in the middle of the room.", "A room with a couch, table, lamp and a chaise.", "A living room with couch, chaise, track lighting, and a large window.", "A room with large windows, a couch and a table.", "A living room with lots of furniture and a large window."], "instances": [{"category": "dining table", "bbox": [0.388, 0.644, 0.636, 0.879]}, {"category": "couch", "bbox": [0.194, 0.531, 0.552, 0.777]}, {"category": "couch", "bbox": [0.568, 0.488, 0.907, 0.783]}, {"category": "remote", "bbox": [0.524, 0.651, 0.556, 0.675]}, {"category": "chair", "bbox": [0.661, 0.478, 0.802, 0.604]}]}
-{"id": "000000217181", "image": "000000217181.jpg", "captions": ["They are standing next to some stylish motorcycles.", "Three men are standing around looking at sports motorcycles.", "A small group of men are standing around a motorcycle.", "Two men surrounding a blue motorcycle and others", "A few blue motorcycles are parked in a lot."], "instances": [{"category": "car", "bbox": [0.011, 0.177, 0.2, 0.336]}, {"category": "motorcycle", "bbox": [0.032, 0.139, 0.907, 0.982]}, {"category": "motorcycle", "bbox": [0.0, 0.239, 0.148, 0.613]}, {"category": "motorcycle", "bbox": [0.0, 0.301, 0.106, 0.45]}, {"category": "person", "bbox": [0.775, 0.043, 0.93, 0.463]}, {"category": "person", "bbox": [0.717, 0.116, 0.81, 0.509]}, {"category": "person", "bbox": [0.296, 0.008, 0.472, 0.325]}, {"category": "person", "bbox": [0.115, 0.19, 0.164, 0.269]}, {"category": "truck", "bbox": [0.63, 0.227, 0.731, 0.335]}]}
-{"id": "000000140289", "image": "000000140289.jpg", "captions": ["Two born bears walking though a forest surrounded by trees.", "Two full grown brown bears in a habitat.", "Two bears are roaming around in the woods.", "Two bears around logs in front of a large rock.", "Two big bears wandering through the woods together"], "instances": [{"category": "bear", "bbox": [0.131, 0.269, 0.375, 0.65]}, {"category": "bear", "bbox": [0.568, 0.193, 0.809, 0.827]}]}
-{"id": "000000460149", "image": "000000460149.jpg", "captions": ["A clock hosted on a pole on a pavement next to a building", "Street clock on quiet street with trees and bicycles.", "A tall clock stands on an empty sidewalk.", "A pole that has a clock on the top of it.", "a clock on a short tower and potted plants along the sidewalk"], "instances": [{"category": "potted plant", "bbox": [0.14, 0.71, 0.338, 0.856]}, {"category": "bicycle", "bbox": [0.65, 0.671, 0.766, 0.733]}, {"category": "car", "bbox": [0.38, 0.608, 0.488, 0.656]}, {"category": "clock", "bbox": [0.468, 0.048, 0.699, 0.216]}, {"category": "bicycle", "bbox": [0.669, 0.662, 0.719, 0.67]}, {"category": "car", "bbox": [0.786, 0.625, 0.86, 0.668]}, {"category": "potted plant", "bbox": [0.756, 0.637, 0.819, 0.682]}, {"category": "person", "bbox": [0.942, 0.615, 0.954, 0.641]}, {"category": "bicycle", "bbox": [0.648, 0.68, 0.714, 0.747]}, {"category": "car", "bbox": [0.837, 0.619, 0.88, 0.659]}, {"category": "potted plant", "bbox": [0.017, 0.197, 0.443, 0.686]}]}
-{"id": "000000225738", "image": "000000225738.jpg", "captions": ["A group of giraffes standing up in their natural habitat.", "A group of giraffe standing in a grass field.", "A group of four giraffes near the same tree.", "there are four giraffes standing among some dry brush", "A herd of giraffe standing on top of a grass field."], "instances": [{"category": "giraffe", "bbox": [0.648, 0.231, 0.855, 0.915]}, {"category": "giraffe", "bbox": [0.33, 0.136, 0.521, 0.93]}, {"category": "giraffe", "bbox": [0.406, 0.261, 0.515, 1.0]}, {"category": "giraffe", "bbox": [0.347, 0.194, 0.583, 0.922]}]}
-{"id": "000000109532", "image": "000000109532.jpg", "captions": ["An adorable husky dog sleeping in a dog bed next to a fan.", "A dark room with a dog sleeping on a dog bed.", "A dog is sleeping in a dark room.", "a large dog laying in a dog bed in a living room", "A dog sleeping on a dog bed in a room."], "instances": [{"category": "dog", "bbox": [0.426, 0.661, 0.582, 0.925]}, {"category": "potted plant", "bbox": [0.603, 0.261, 0.781, 0.613]}, {"category": "chair", "bbox": [0.67, 0.515, 0.899, 0.801]}, {"category": "potted plant", "bbox": [0.671, 0.439, 0.763, 0.612]}, {"category": "chair", "bbox": [0.852, 0.653, 0.948, 0.818]}]}
-{"id": "000000118606", "image": "000000118606.jpg", "captions": ["A man riding skis on top of a rail.", "a person riding a pair of skis on a rail", "Someone on a pair of skis on a ramp at the ski slope", "Person with skis in the air above the snow.", "A man performing a trick on a rail while skiing."], "instances": [{"category": "person", "bbox": [0.444, 0.361, 0.537, 0.633]}, {"category": "skis", "bbox": [0.413, 0.554, 0.539, 0.664]}, {"category": "person", "bbox": [0.342, 0.585, 0.352, 0.62]}, {"category": "person", "bbox": [0.439, 0.565, 0.446, 0.58]}]}
-{"id": "000000385873", "image": "000000385873.jpg", "captions": ["Three pizzas sitting next to each other in boxes.", "Two smaller pizzas sit beside a large pizza topped with tortilla chips.", "Three pizzas inside their delivery boxes, one with two side orders of sauce.", "One pizza is larger than two other pizzas.", "Three pizza boxes with pizza in them are open."], "instances": [{"category": "bowl", "bbox": [0.634, 0.624, 0.736, 0.752]}, {"category": "pizza", "bbox": [0.3, 0.382, 0.615, 0.733]}, {"category": "pizza", "bbox": [0.0, 0.4, 0.287, 0.745]}, {"category": "pizza", "bbox": [0.624, 0.279, 0.999, 0.753]}, {"category": "bowl", "bbox": [0.94, 0.247, 1.0, 0.352]}]}
-{"id": "000000092109", "image": "000000092109.jpg", "captions": ["A giraffe's head is pictured in this clear, colorful photo.", "A giraffe is standing tall in the middle of several bright green trees", "The face of a giraffe looking to the side.", "the close up head shot of a giraffe", "this is a giraffe chewing on some leaves"], "instances": [{"category": "giraffe", "bbox": [0.236, 0.122, 1.0, 0.987]}]}
-{"id": "000000163076", "image": "000000163076.jpg", "captions": ["There's an outdoor dining area featuring a fountain.", "A table sitting next to a water fountain covered by an umbrella.", "An empty restaurant patio with tables and umbrellas.", "An outdoor restaurant with a fountain at night", "A fountain bubbles in the plaza of an outdoor cafe."], "instances": [{"category": "umbrella", "bbox": [0.064, 0.069, 0.95, 0.844]}, {"category": "chair", "bbox": [0.198, 0.574, 0.355, 0.704]}, {"category": "chair", "bbox": [0.42, 0.571, 0.55, 0.738]}, {"category": "dining table", "bbox": [0.066, 0.741, 0.766, 0.925]}, {"category": "dining table", "bbox": [0.059, 0.584, 0.27, 0.659]}, {"category": "chair", "bbox": [0.432, 0.567, 0.52, 0.624]}, {"category": "chair", "bbox": [0.433, 0.555, 0.504, 0.6]}, {"category": "chair", "bbox": [0.109, 0.673, 0.374, 0.796]}]}
-{"id": "000000560371", "image": "000000560371.jpg", "captions": ["Street signs from the corner of 8th ave. and 22 3/4 st.", "A two way street sign with one sign that changes from one name to another.", "A street sign is pointing towards 8th avenue and the other is pointing towards 22 3/4 street in the middle of the forest.", "A street sign standing in front of some trees.", "Peculiar street sign showing intersection of 23 3/4 St and 8th Ave/CTH D."], "instances": []}
-{"id": "000000367571", "image": "000000367571.jpg", "captions": ["A couple of different doughnuts in a box", "There are four donuts in a box, and some are cake donuts and a doughnut with nuts and coconut on top.", "A box of glazed doughnuts on a table.", "Three donuts with toppings on them sitting inside a box.", "A box that is filled with different kinds of doughnuts."], "instances": [{"category": "donut", "bbox": [0.412, 0.335, 0.711, 0.681]}, {"category": "donut", "bbox": [0.093, 0.493, 0.486, 0.922]}, {"category": "donut", "bbox": [0.713, 0.423, 0.957, 0.874]}, {"category": "donut", "bbox": [0.13, 0.331, 0.397, 0.55]}]}
-{"id": "000000580197", "image": "000000580197.jpg", "captions": ["Two men in bow ties standing next to steel rafter.", "Several men in suits talking together in a room.", "An older man in a tuxedo standing next to a younger man in a tuxedo wearing glasses.", "Two men wearing tuxedos glance at each other.", "Older man in tuxedo sitting next to another younger man in tuxedo."], "instances": [{"category": "tie", "bbox": [0.914, 0.46, 0.984, 0.512]}, {"category": "person", "bbox": [0.297, 0.638, 0.71, 0.989]}, {"category": "person", "bbox": [0.77, 0.177, 1.0, 0.971]}, {"category": "tie", "bbox": [0.281, 0.481, 0.368, 0.519]}, {"category": "person", "bbox": [0.103, 0.204, 0.497, 1.0]}]}
-{"id": "000000506095", "image": "000000506095.jpg", "captions": ["A cat is staring at a laptop computer.", "a cat on a desk with a laptop and a mouse", "A cat that is sitting at a desk next to a laptop.", "A kitten sitting on a laptop computer sitting on top of a wooden desk.", "A kitten sits facing an open black laptop."], "instances": [{"category": "cat", "bbox": [0.658, 0.207, 1.0, 0.754]}, {"category": "laptop", "bbox": [0.108, 0.135, 0.766, 0.69]}, {"category": "book", "bbox": [0.836, 0.239, 0.954, 0.273]}, {"category": "book", "bbox": [0.0, 0.556, 0.128, 0.685]}, {"category": "book", "bbox": [0.039, 0.574, 0.257, 0.691]}, {"category": "book", "bbox": [0.825, 0.214, 0.962, 0.254]}, {"category": "book", "bbox": [0.892, 0.275, 0.958, 0.308]}, {"category": "book", "bbox": [0.922, 0.318, 0.986, 0.353]}, {"category": "book", "bbox": [0.87, 0.267, 0.951, 0.291]}, {"category": "book", "bbox": [0.949, 0.102, 0.976, 0.114]}, {"category": "book", "bbox": [0.936, 0.161, 0.958, 0.168]}]}
-{"id": "000000024996", "image": "000000024996.jpg", "captions": ["A bathroom with a glass door and a sink.", "A blue lined bathroom with an open glass door.", "A nice bathroom with a sink, toilet, and tiled shower.", "A bathroom that is clean and shiny in the day.", "a bathroom with a sink and a mirror and a window"], "instances": [{"category": "toilet", "bbox": [0.842, 0.934, 0.95, 1.0]}, {"category": "sink", "bbox": [0.506, 0.724, 0.683, 0.834]}]}
-{"id": "000000457882", "image": "000000457882.jpg", "captions": ["a girl in a bikini and a brown and white dog and a few other people", "A woman with a swimsuit on sitting with a dog.", "A woman is sitting with a dog on her lap.", "A dog sitting next to a woman in her swimsuit.", "WOMAN SITTING WITH HER DOG, AND OTHER WOMEN ARE AROUND"], "instances": [{"category": "dog", "bbox": [0.202, 0.409, 0.54, 0.81]}, {"category": "dog", "bbox": [0.61, 0.428, 0.729, 0.723]}, {"category": "boat", "bbox": [0.003, 0.705, 0.939, 0.974]}, {"category": "person", "bbox": [0.236, 0.001, 0.558, 0.784]}, {"category": "person", "bbox": [0.681, 0.001, 0.957, 0.798]}, {"category": "person", "bbox": [0.849, 0.478, 1.0, 0.946]}, {"category": "person", "bbox": [0.345, 0.187, 0.634, 0.828]}, {"category": "person", "bbox": [0.033, 0.345, 0.109, 0.434]}]}
-{"id": "000000081552", "image": "000000081552.jpg", "captions": ["A cat sitting and curled up on a red couch", "A cat laying on a red couch sleeping.", "a tan and black cat curled up asleep on a red velvet seat", "A cat is curled up on a red sofa.", "Cat curled up, sleeping on a red plush couch."], "instances": [{"category": "cat", "bbox": [0.412, 0.237, 0.634, 0.482]}, {"category": "couch", "bbox": [0.003, 0.005, 1.0, 0.99]}]}
-{"id": "000000273450", "image": "000000273450.jpg", "captions": ["A person flipping of a parking meter on the side of a road.", "A man holds up his middle finger to a parking meter.", "Person giving the middle finger to a parking meter.", "a black silver white blue red an orange parking meter and a hand flipping it off", "A person is flipping off a parking meter."], "instances": [{"category": "person", "bbox": [0.0, 0.475, 0.565, 0.987]}, {"category": "car", "bbox": [0.0, 0.0, 0.531, 0.734]}, {"category": "parking meter", "bbox": [0.0, 0.0, 1.0, 0.987]}]}
-{"id": "000000203879", "image": "000000203879.jpg", "captions": ["There is a small cellphone displayed between a set of ear buds and two paper weights.", "a cell phone lays next to some diamonds", "a close up of a cell phone on a table near earbuds", "A cell phone sits on a table next to some jewels.", "A cell phone, ear buds, and two jewels laying near each other."], "instances": [{"category": "cell phone", "bbox": [0.322, 0.233, 0.62, 0.79]}]}
-{"id": "000000346875", "image": "000000346875.jpg", "captions": ["two zebras in a field near one another", "A couple of zebra walking across a green field.", "Two zebra are walking near a gravel road.", "two zebras in a green field of grass and some trees", "A zebra follows another zebra through a park."], "instances": [{"category": "zebra", "bbox": [0.591, 0.263, 0.82, 0.466]}, {"category": "zebra", "bbox": [0.293, 0.243, 0.561, 0.45]}]}
-{"id": "000000525439", "image": "000000525439.jpg", "captions": ["a man stands in front of a flipped skate boarder", "A man standing next to a skateboard that is laying on the ground wheels pointed up.", "Skateboard laying upside down on cement with someone standing next to it.", "A boy in camo shorts stands before an overturned skateboard.", "a person with an upside down skate board"], "instances": [{"category": "person", "bbox": [0.307, 0.001, 0.63, 0.739]}, {"category": "skateboard", "bbox": [0.0, 0.592, 0.626, 0.969]}]}
-{"id": "000000304749", "image": "000000304749.jpg", "captions": ["The woman is taking a picture in the bathroom mirror.", "A picture of a woman in a mirror.", "A woman's midsection reflected in a round mirror.", "A circular mirror reflecting a woman's stomach in turquoise shirt.", "A selfie taken of a person from the neck down."], "instances": [{"category": "person", "bbox": [0.092, 0.001, 0.646, 0.496]}]}
-{"id": "000000323760", "image": "000000323760.jpg", "captions": ["A toilet is shown in a bare room.", "A ugly bathroom with a section of the wall missing.", "A toilet in a stripped bathroom with studs, bricks and plaster showing", "A bathroom with no walls and a toilet bowl", "A white toilet next to some torn out walls."], "instances": [{"category": "toilet", "bbox": [0.167, 0.585, 0.714, 1.0]}]}
-{"id": "000000066144", "image": "000000066144.jpg", "captions": ["A woman standing in front of window next to a bug and a stop sign.", "A car parked on the street next to a tree and stop sign.", "A lone Volkswagen is parked by a stop sign.", "A window view of a small car near a street stop sign.", "An old VW Bug standing at a stop sign."], "instances": [{"category": "stop sign", "bbox": [0.501, 0.328, 0.569, 0.428]}, {"category": "car", "bbox": [0.242, 0.488, 0.56, 0.726]}, {"category": "car", "bbox": [0.279, 0.325, 0.33, 0.363]}, {"category": "car", "bbox": [0.153, 0.333, 0.29, 0.405]}, {"category": "car", "bbox": [0.11, 0.339, 0.177, 0.373]}, {"category": "car", "bbox": [0.0, 0.654, 0.082, 0.826]}, {"category": "car", "bbox": [0.0, 0.322, 0.064, 0.364]}, {"category": "car", "bbox": [0.451, 0.333, 0.51, 0.392]}]}
-{"id": "000000455772", "image": "000000455772.jpg", "captions": ["A person in a field jumping to catch a Frisbee.", "A guy jumping to catch a frisbee in mid-air.", "A person that is trying to get a frisbee.", "Nice reach, but the Frisbee flies on, victorious.", "A man playing frisbee in a grassy yard."], "instances": [{"category": "car", "bbox": [0.148, 0.339, 0.201, 0.476]}, {"category": "car", "bbox": [0.376, 0.396, 0.424, 0.476]}, {"category": "person", "bbox": [0.547, 0.122, 0.698, 0.904]}, {"category": "frisbee", "bbox": [0.479, 0.154, 0.555, 0.231]}, {"category": "car", "bbox": [0.001, 0.299, 0.085, 0.394]}]}
-{"id": "000000511117", "image": "000000511117.jpg", "captions": ["A couple of kids standing on top of a grass covered field.", "A little boy wearing a baseball uniform stands by a little girl.", "A young boy in a baseball uniform and a young girl are standing in front of a chain link fence.", "A little boy and girl standing on a baseball field. The boy has a uniform on.", "A young baseball player is standing next to a young girl."], "instances": [{"category": "person", "bbox": [0.514, 0.178, 0.776, 0.774]}, {"category": "baseball glove", "bbox": [0.468, 0.462, 0.593, 0.609]}, {"category": "person", "bbox": [0.174, 0.051, 0.598, 0.839]}, {"category": "bench", "bbox": [0.558, 0.125, 1.0, 0.315]}]}
-{"id": "000000207151", "image": "000000207151.jpg", "captions": ["A vegetarian pizza is half eaten on a pizza holder.", "A couple of pieces of pizza with vegetable slices on them.", "A wooden pan serving tray with a pizza on it.", "A pizza on a cutting board is half gone.", "A Pizza is nearly finished with only three pieces left."], "instances": [{"category": "bottle", "bbox": [0.001, 0.001, 0.121, 0.231]}, {"category": "cup", "bbox": [0.0, 0.002, 0.121, 0.238]}, {"category": "pizza", "bbox": [0.17, 0.472, 0.526, 0.82]}, {"category": "pizza", "bbox": [0.398, 0.106, 0.962, 0.679]}, {"category": "dining table", "bbox": [0.0, 0.001, 1.0, 0.988]}]}
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-{"id": "000000018476", "image": "000000018476.jpg", "captions": ["A tie that is sitting on top of a shirt.", "This photograph appears to be looking truly wonderful.", "a uniform complete with shoes laying on a bed", "Suit laid out with a red tie, white shirt and black shoes.", "a white shirt a red tie and some black shoes"], "instances": [{"category": "tie", "bbox": [0.457, 0.09, 0.853, 0.984]}, {"category": "bed", "bbox": [0.005, 0.005, 1.0, 0.379]}]}
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-{"id": "000000353536", "image": "000000353536.jpg", "captions": ["A table topped with plates and glasses with eating utensils..", "a fork is laying on a small white plate", "dirty dishes on a table, and a bottle of something.", "a table top with some dishes on top of it", "A table full of dirty dishes is pictured in this image."], "instances": [{"category": "dining table", "bbox": [0.0, 0.007, 0.998, 0.988]}, {"category": "bottle", "bbox": [0.554, 0.002, 0.768, 0.411]}, {"category": "cup", "bbox": [0.372, 0.011, 0.544, 0.427]}, {"category": "fork", "bbox": [0.442, 0.464, 0.818, 0.572]}, {"category": "fork", "bbox": [0.089, 0.233, 0.272, 0.456]}, {"category": "spoon", "bbox": [0.144, 0.218, 0.326, 0.413]}, {"category": "cup", "bbox": [0.688, 0.056, 0.812, 0.361]}]}
-{"id": "000000416256", "image": "000000416256.jpg", "captions": ["A cat laying on the floor next to a keyboard.", "an orange and white cat is laying next to a keyboard and some wires", "A cat is laying next to a computer keyboard.", "a cat laying on a floor next to a keyboard", "A CAT LAYING ON THE FLOOR AMIDST A COMPUTER,SPEAKERS,CORDS"], "instances": [{"category": "cat", "bbox": [0.235, 0.23, 0.737, 0.639]}, {"category": "keyboard", "bbox": [0.243, 0.562, 0.631, 0.836]}, {"category": "keyboard", "bbox": [0.058, 0.33, 0.277, 0.608]}]}
-{"id": "000000214367", "image": "000000214367.jpg", "captions": ["Wood shading on the side of a window with brick siding.", "A tree filled with lots of red fruit near a building.", "By the window outside is a apple tree, where the apples are ready to be picked.", "Some very nice looking red fruity by a window,", "A shuttered window has a fruit tree outside it."], "instances": [{"category": "apple", "bbox": [0.214, 0.112, 0.408, 0.266]}, {"category": "apple", "bbox": [0.472, 0.166, 0.618, 0.293]}, {"category": "apple", "bbox": [0.055, 0.592, 0.172, 0.686]}, {"category": "apple", "bbox": [0.126, 0.661, 0.236, 0.739]}, {"category": "apple", "bbox": [0.52, 0.09, 0.609, 0.143]}, {"category": "apple", "bbox": [0.226, 0.354, 0.285, 0.409]}, {"category": "apple", "bbox": [0.0, 0.698, 0.096, 0.771]}, {"category": "apple", "bbox": [0.001, 0.646, 0.042, 0.713]}, {"category": "apple", "bbox": [0.258, 0.719, 0.329, 0.778]}]}
-{"id": "000000210299", "image": "000000210299.jpg", "captions": ["A little boy riding his bike and wearing a helmet", "A little boy raveling down a road on a bike, with a yellow helmet on.", "The boy wears a helmet while riding his bicycle.", "a small child wearing a helmet and riding a bike", "A little boy wearing a helmet and riding a bike."], "instances": [{"category": "person", "bbox": [0.198, 0.259, 0.399, 0.679]}, {"category": "bicycle", "bbox": [0.213, 0.383, 0.408, 0.835]}]}
-{"id": "000000088218", "image": "000000088218.jpg", "captions": ["Signs proclaim the famous Haight Ashbury intersection and district.", "a pole with street lights, signs and wires attached to it", "A traffic light at the intersection of Haight and Ashbury", "A traffic sign is shown with traffic signs above it.", "The street signs and traffic signal are below wires attached to the pole."], "instances": [{"category": "traffic light", "bbox": [0.443, 0.435, 0.658, 0.721]}]}
-{"id": "000000020650", "image": "000000020650.jpg", "captions": ["Burger with broccoli, pickle, and fork on orange plate", "On a plate is kept a burger and a bowl of broccoli and a fork.", "There is half a sandwich on an orange plate with a pickle and a bowl of broccoli", "A A bowl and a sandwich on an orange plate on a table.", "A plate has a sandwich, broccoli, and a pickle."], "instances": [{"category": "sandwich", "bbox": [0.436, 0.155, 0.805, 0.859]}, {"category": "sandwich", "bbox": [0.311, 0.006, 0.748, 0.293]}, {"category": "fork", "bbox": [0.0, 0.665, 0.578, 0.876]}, {"category": "bowl", "bbox": [0.002, 0.263, 0.487, 0.744]}, {"category": "bowl", "bbox": [0.708, 0.003, 0.828, 0.03]}, {"category": "broccoli", "bbox": [0.185, 0.288, 0.366, 0.546]}, {"category": "broccoli", "bbox": [0.017, 0.344, 0.384, 0.654]}, {"category": "broccoli", "bbox": [0.31, 0.191, 0.466, 0.463]}, {"category": "broccoli", "bbox": [0.104, 0.107, 0.285, 0.342]}, {"category": "broccoli", "bbox": [0.092, 0.276, 0.242, 0.442]}, {"category": "dining table", "bbox": [0.002, 0.0, 0.999, 0.987]}]}
-{"id": "000000514915", "image": "000000514915.jpg", "captions": ["A large black dog laying on a kitchen floor.", "A dog is laying down on the floor in the home.", "Black dog laying down on the kitchen floor next to it's bowls and toy", "A black dog with a red collar laying on a tiled floor.", "A black dog that is laying on the floor."], "instances": [{"category": "dog", "bbox": [0.087, 0.276, 0.812, 0.792]}, {"category": "bowl", "bbox": [0.437, 0.09, 0.533, 0.213]}, {"category": "bowl", "bbox": [0.537, 0.035, 0.665, 0.141]}]}
-{"id": "000000205183", "image": "000000205183.jpg", "captions": ["A duck walking along a paved road next to a patch of grass.", "A close up of a duck walking on a path.", "a duck walks along a cement patch while looking down", "A white duck out of water, walking on the ground.", "A goose standing in the road, looking at the ground."], "instances": [{"category": "bird", "bbox": [0.291, 0.235, 0.859, 0.889]}]}
-{"id": "000000534270", "image": "000000534270.jpg", "captions": ["Man and woman with umbrella hats sitting on top of a bridge.", "A couple equipped with umbrella hats taking a break from walking their dog on a bridge on a rainy day.", "Two people in ridiculous looking umbrella hats.", "two people with umbrella hats near one another", "A couple of people wearing umbrella hats next to the ocean."], "instances": [{"category": "dog", "bbox": [0.456, 0.832, 0.6, 0.983]}, {"category": "person", "bbox": [0.433, 0.464, 0.636, 0.975]}, {"category": "person", "bbox": [0.263, 0.321, 0.459, 0.978]}, {"category": "boat", "bbox": [0.912, 0.4, 0.978, 0.433]}, {"category": "boat", "bbox": [0.211, 0.236, 0.478, 0.304]}, {"category": "boat", "bbox": [0.144, 0.328, 0.189, 0.361]}, {"category": "umbrella", "bbox": [0.443, 0.402, 0.607, 0.473]}, {"category": "umbrella", "bbox": [0.325, 0.311, 0.483, 0.432]}, {"category": "umbrella", "bbox": [0.207, 0.738, 0.284, 0.778]}, {"category": "umbrella", "bbox": [0.489, 0.713, 0.649, 0.83]}]}
-{"id": "000000408439", "image": "000000408439.jpg", "captions": ["Cliffs rise on the edge of a placid lake.", "A scenic view of a river with a train on the edge of it in the distance.", "A large lake surrounded by beautiful tree covered mountains.", "a landscape scene with water, mountains and trees", "A train on a waterfront track surrounded by mountains."], "instances": [{"category": "train", "bbox": [0.008, 0.591, 0.562, 0.644]}]}
-{"id": "000000474253", "image": "000000474253.jpg", "captions": ["A man riding on the back of a horse through a river.", "A person is riding a horse through water.", "Horse and rider crossing waterway during competitive event.", "A woman riding a horse splashes through a large puddle.", "A young man riding a horse through some water."], "instances": [{"category": "horse", "bbox": [0.385, 0.235, 0.651, 0.814]}, {"category": "person", "bbox": [0.396, 0.06, 0.576, 0.675]}, {"category": "person", "bbox": [0.29, 0.148, 0.355, 0.333]}, {"category": "person", "bbox": [0.129, 0.163, 0.212, 0.349]}, {"category": "person", "bbox": [0.005, 0.014, 0.038, 0.165]}, {"category": "person", "bbox": [0.144, 0.011, 0.193, 0.155]}, {"category": "person", "bbox": [0.089, 0.007, 0.133, 0.162]}]}
-{"id": "000000098029", "image": "000000098029.jpg", "captions": ["a table with many plates on it with a bread basket", "A table set for four has many foods and fruits on it.", "Several objects displayed on a kitchen table including bread, oranges and plating.", "Several dishes and food items sit on a table.", "An assortment of foods sitting on a round brown table."], "instances": [{"category": "refrigerator", "bbox": [0.013, 0.004, 0.37, 0.317]}, {"category": "bottle", "bbox": [0.467, 0.517, 0.555, 0.638]}, {"category": "bottle", "bbox": [0.602, 0.536, 0.658, 0.609]}, {"category": "chair", "bbox": [0.747, 0.367, 1.0, 0.592]}, {"category": "chair", "bbox": [0.044, 0.368, 0.358, 0.544]}, {"category": "cup", "bbox": [0.296, 0.465, 0.359, 0.54]}, {"category": "cup", "bbox": [0.709, 0.67, 0.782, 0.736]}, {"category": "cup", "bbox": [0.213, 0.684, 0.294, 0.753]}, {"category": "knife", "bbox": [0.787, 0.699, 0.922, 0.797]}, {"category": "knife", "bbox": [0.161, 0.539, 0.265, 0.584]}, {"category": "spoon", "bbox": [0.813, 0.674, 0.922, 0.759]}, {"category": "spoon", "bbox": [0.156, 0.555, 0.233, 0.587]}, {"category": "spoon", "bbox": [0.596, 0.467, 0.613, 0.509]}, {"category": "bowl", "bbox": [0.241, 0.753, 0.505, 0.935]}, {"category": "banana", "bbox": [0.632, 0.138, 0.718, 0.161]}, {"category": "apple", "bbox": [0.701, 0.152, 0.758, 0.191]}, {"category": "orange", "bbox": [0.607, 0.66, 0.692, 0.716]}, {"category": "orange", "bbox": [0.565, 0.636, 0.611, 0.667]}, {"category": "orange", "bbox": [0.526, 0.624, 0.572, 0.652]}, {"category": "orange", "bbox": [0.61, 0.628, 0.656, 0.657]}, {"category": "orange", "bbox": [0.599, 0.649, 0.643, 0.677]}, {"category": "dining table", "bbox": [0.013, 0.439, 0.964, 0.986]}, {"category": "cup", "bbox": [0.612, 0.489, 0.669, 0.548]}, {"category": "knife", "bbox": [0.605, 0.457, 0.638, 0.53]}, {"category": "apple", "bbox": [0.502, 0.137, 0.537, 0.159]}, {"category": "orange", "bbox": [0.54, 0.135, 0.563, 0.151]}, {"category": "orange", "bbox": [0.527, 0.129, 0.554, 0.142]}, {"category": "orange", "bbox": [0.611, 0.155, 0.641, 0.171]}, {"category": "chair", "bbox": [0.0, 0.843, 0.29, 0.989]}, {"category": "cup", "bbox": [0.353, 0.469, 0.411, 0.511]}, {"category": "cup", "bbox": [0.609, 0.716, 0.682, 0.786]}, {"category": "orange", "bbox": [0.638, 0.158, 0.679, 0.177]}, {"category": "cake", "bbox": [0.38, 0.821, 0.481, 0.895]}, {"category": "chair", "bbox": [0.79, 0.747, 1.0, 1.0]}, {"category": "bottle", "bbox": [0.719, 0.55, 0.769, 0.616]}, {"category": "bottle", "bbox": [0.795, 0.546, 0.873, 0.613]}, {"category": "knife", "bbox": [0.17, 0.799, 0.264, 0.88]}, {"category": "cup", "bbox": [0.317, 0.695, 0.391, 0.752]}]}
-{"id": "000000294073", "image": "000000294073.jpg", "captions": ["A woman and a man standing between two brown horses.", "A COUPLE WEARING YELLOW DRESS STANDING NEAR TWO HORSES.", "An older couple stands between two horses.", "A man and a woman standing with two horses", "A man and a woman stand in between two horses."], "instances": [{"category": "horse", "bbox": [0.0, 0.052, 0.49, 0.989]}, {"category": "horse", "bbox": [0.632, 0.23, 1.0, 0.989]}, {"category": "person", "bbox": [0.425, 0.326, 0.696, 0.987]}, {"category": "person", "bbox": [0.627, 0.203, 0.828, 0.986]}, {"category": "book", "bbox": [0.525, 0.597, 0.644, 0.833]}]}
-{"id": "000000203629", "image": "000000203629.jpg", "captions": ["A man on a cell phone in a public area holding his thumb up.", "A group of people gathered inside of a room.", "A man on his cellphone posing for a  picture.", "A man giving a thumbs up while on a cell phone.", "The man is giving a thumbs up while on his phone."], "instances": [{"category": "cell phone", "bbox": [0.43, 0.459, 0.449, 0.503]}, {"category": "cup", "bbox": [0.756, 0.838, 0.865, 0.98]}, {"category": "person", "bbox": [0.232, 0.317, 0.603, 0.98]}, {"category": "person", "bbox": [0.602, 0.405, 1.0, 0.999]}, {"category": "person", "bbox": [0.003, 0.339, 0.313, 0.987]}, {"category": "person", "bbox": [0.164, 0.379, 0.258, 0.733]}, {"category": "person", "bbox": [0.564, 0.36, 0.673, 0.645]}, {"category": "person", "bbox": [0.241, 0.379, 0.336, 0.512]}, {"category": "person", "bbox": [0.682, 0.372, 0.736, 0.502]}, {"category": "person", "bbox": [0.654, 0.428, 0.734, 0.536]}, {"category": "person", "bbox": [0.718, 0.368, 0.787, 0.508]}, {"category": "person", "bbox": [0.148, 0.362, 0.205, 0.529]}, {"category": "person", "bbox": [0.001, 0.431, 0.044, 0.564]}, {"category": "cup", "bbox": [0.901, 0.808, 0.995, 0.982]}]}
-{"id": "000000119876", "image": "000000119876.jpg", "captions": ["A man dressed loudly is using his cell phone.", "A man talking on the phone while he walks down the street.", "A man with pink hair talking on a cell phone.", "A man in a purple shirt and tie and purple hair.", "a man colored his hair in purple walking on the road"], "instances": [{"category": "bicycle", "bbox": [0.525, 0.222, 0.924, 0.608]}, {"category": "bicycle", "bbox": [0.895, 0.249, 1.0, 0.642]}, {"category": "person", "bbox": [0.0, 0.0, 0.738, 1.0]}, {"category": "tie", "bbox": [0.319, 0.255, 0.423, 0.638]}, {"category": "cell phone", "bbox": [0.411, 0.13, 0.426, 0.161]}, {"category": "handbag", "bbox": [0.369, 0.205, 0.575, 0.839]}]}
-{"id": "000000164255", "image": "000000164255.jpg", "captions": ["An umbrella that is standing in the sand.", "An umbrella is stuck in the sand on the beach.", "a colorful striped umbrella on the beach near the ocean", "A colorful umbrella is set up at the beach.", "The colorful umbrella is sitting by the beach,"], "instances": [{"category": "umbrella", "bbox": [0.0, 0.101, 0.567, 0.575]}]}
-{"id": "000000192817", "image": "000000192817.jpg", "captions": ["A view from a window high up in the sky.", "A bunch of mountains seen from a plane window.", "The window from a plane overlooking the ground.", "The view of a mountain area from an airplane window.", "An aerial view of mountains and lakes from an airplane window."], "instances": []}
-{"id": "000000258285", "image": "000000258285.jpg", "captions": ["Two large passenger jets flying over a beach filled with birds.", "A plane is flying over a bird filed lake", "Two airplanes are in the sky over blue water.", "An airplane landing over an airplane on the ground.", "A photo of two plans with water and birds surrounding it , one plane in the air one one the ground."], "instances": [{"category": "bird", "bbox": [0.507, 0.941, 0.536, 0.973]}, {"category": "bird", "bbox": [0.304, 0.933, 0.315, 0.95]}, {"category": "bird", "bbox": [0.129, 0.885, 0.143, 0.912]}, {"category": "bird", "bbox": [0.158, 0.851, 0.165, 0.87]}, {"category": "bird", "bbox": [0.404, 0.839, 0.429, 0.864]}, {"category": "bird", "bbox": [0.498, 0.833, 0.513, 0.861]}, {"category": "airplane", "bbox": [0.276, 0.085, 0.825, 0.316]}, {"category": "airplane", "bbox": [0.478, 0.252, 0.983, 0.495]}, {"category": "bird", "bbox": [0.552, 0.828, 0.564, 0.844]}, {"category": "bird", "bbox": [0.789, 0.812, 0.798, 0.836]}, {"category": "bird", "bbox": [0.927, 0.82, 0.936, 0.838]}, {"category": "bird", "bbox": [0.65, 0.828, 0.664, 0.849]}, {"category": "bird", "bbox": [0.752, 0.81, 0.763, 0.83]}, {"category": "bird", "bbox": [0.841, 0.817, 0.852, 0.828]}, {"category": "bird", "bbox": [0.292, 0.849, 0.311, 0.868]}, {"category": "bird", "bbox": [0.005, 0.727, 0.981, 0.998]}]}
-{"id": "000000506483", "image": "000000506483.jpg", "captions": ["An art installation is placed by a street.", "People sit near a display of large artworks including an oversize bench and painted feline heads.", "Looking down on a giant rocking bench and large animal heads.", "An over sized wooden bench next to two massive animal art sculptures.", "artistic sculptures and images on a city street"], "instances": [{"category": "car", "bbox": [0.656, 0.939, 0.933, 1.0]}, {"category": "person", "bbox": [0.08, 0.664, 0.147, 0.805]}, {"category": "person", "bbox": [0.154, 0.646, 0.217, 0.821]}, {"category": "bench", "bbox": [0.316, 0.124, 0.951, 0.635]}, {"category": "backpack", "bbox": [0.062, 0.701, 0.097, 0.769]}, {"category": "person", "bbox": [0.0, 0.132, 0.031, 0.197]}]}
-{"id": "000000502168", "image": "000000502168.jpg", "captions": ["a fleet of naval ships in the ocean", "A group of men on aircraft carrier with other boats in the distance.", "A large ship floating in the ocean next to other ships.", "Several men on a boat looking over the side.", "The men wear hardhats as they work on the aircraft carrier."], "instances": [{"category": "boat", "bbox": [0.634, 0.292, 1.0, 0.982]}, {"category": "person", "bbox": [0.675, 0.507, 0.736, 0.731]}, {"category": "person", "bbox": [0.684, 0.737, 0.817, 1.0]}, {"category": "person", "bbox": [0.803, 0.691, 0.883, 0.932]}, {"category": "person", "bbox": [0.741, 0.56, 0.798, 0.767]}, {"category": "person", "bbox": [0.924, 0.269, 0.951, 0.367]}, {"category": "boat", "bbox": [0.079, 0.171, 0.172, 0.231]}, {"category": "boat", "bbox": [0.863, 0.131, 0.961, 0.239]}, {"category": "boat", "bbox": [0.435, 0.288, 0.46, 0.313]}, {"category": "boat", "bbox": [0.591, 0.186, 0.605, 0.222]}, {"category": "person", "bbox": [0.451, 0.289, 0.455, 0.296]}, {"category": "person", "bbox": [0.446, 0.29, 0.451, 0.296]}, {"category": "person", "bbox": [0.872, 0.627, 0.957, 0.966]}, {"category": "person", "bbox": [0.44, 0.288, 0.446, 0.3]}]}
-{"id": "000000319432", "image": "000000319432.jpg", "captions": ["Man holding two shirts with luggage and window", "A man holding clothes on a hanger with a suitcase in front of him.", "A man show a red and a white clothing hangers.", "A man holding his garment bags in both hands", "A man holding up some clothes in some hanger bags."], "instances": [{"category": "person", "bbox": [0.0, 0.092, 0.776, 0.852]}, {"category": "suitcase", "bbox": [0.153, 0.798, 0.587, 1.0]}]}
-{"id": "000000131019", "image": "000000131019.jpg", "captions": ["Two zebras and two monkeys walking on the grass.", "Two giraffes and another animal are on green grass.", "A baboon and two zebras grazing on the savannah.", "A baboon and its baby eat by two zebras in the grass", "Monkey standing behind two zebras as they graze."], "instances": [{"category": "zebra", "bbox": [0.367, 0.258, 0.834, 0.646]}, {"category": "zebra", "bbox": [0.161, 0.13, 0.396, 0.375]}, {"category": "bird", "bbox": [0.309, 0.138, 0.34, 0.163]}]}
diff --git a/llava/eval/table/model.jsonl b/llava/eval/table/model.jsonl
deleted file mode 100644
index 61b6de15..00000000
--- a/llava/eval/table/model.jsonl
+++ /dev/null
@@ -1,5 +0,0 @@
-{"model_id": "vicuna-13b:20230322-clean-lang", "model_name": "vicuna-13b", "model_version": "20230322-clean-lang", "model_metadata": "vicuna-13b-20230322-clean-lang"}
-{"model_id": "alpaca-13b:v1", "model_name": "alpaca-13b", "model_version": "v1", "model_metadata": "alpaca-13b"}
-{"model_id": "llama-13b:v1", "model_name": "llama-13b", "model_version": "v1", "model_metadata": "hf-llama-13b"}
-{"model_id": "bard:20230327", "model_name": "bard", "model_version": "20230327", "model_metadata": "Google Bard 20230327"}
-{"model_id": "gpt-3.5-turbo:20230327", "model_name": "gpt-3.5-turbo", "model_version": "20230327", "model_metadata": "OpenAI ChatGPT gpt-3.5-turbo Chat Completion"}
diff --git a/llava/eval/table/prompt.jsonl b/llava/eval/table/prompt.jsonl
deleted file mode 100644
index a46845a6..00000000
--- a/llava/eval/table/prompt.jsonl
+++ /dev/null
@@ -1,4 +0,0 @@
-{"prompt_id": 1, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for general questions"}
-{"prompt_id": 2, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "Your task is to evaluate the coding abilities of the above two assistants. They have been asked to implement a program to solve a given problem. Please review their code submissions, paying close attention to their problem-solving approach, code structure, readability, and the inclusion of helpful comments.\n\nPlease ensure that the assistants' submissions:\n\n1. Correctly implement the given problem statement.\n2. Contain accurate and efficient code.\n3. Include clear and concise comments that explain the code's logic and functionality.\n4. Adhere to proper coding standards and best practices.\n\nOnce you have carefully reviewed both submissions, provide detailed feedback on their strengths and weaknesses, along with any suggestions for improvement. You should first output a single line containing two scores on the scale of 1-10 (1: no code/no sense; 10: perfect) for Assistant 1 and 2, respectively. Then give extra comments starting from the next line."}, "description": "Prompt for coding questions"}
-{"prompt_id": 3, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the mathematical proficiency of two AI assistants regarding the given user question.\nFirstly, please solve the problem independently, without referring to the answers provided by Assistant 1 and Assistant 2.\nAfterward, please examine the problem-solving process of Assistant 1 and Assistant 2 step-by-step to ensure their correctness, identifying any incorrect steps if present. Your evaluation should take into account not only the answer but also the problem-solving steps.\nFinally, please output a Python tuple containing two numerical scores for Assistant 1 and Assistant 2, ranging from 1 to 10, respectively. If applicable, explain the reasons for any variations in their scores and determine which assistant performed better."}, "description": "Prompt for math questions"}
-{"prompt_id": 4, "system_prompt": "You are a helpful and precise assistant for checking the quality of the answer.", "prompt_template": "[Visual Context]\n{context}\n[Question]\n{question}\n\n[Assistant 1]\n{answer_1}\n\n[End of Assistant 1]\n\n[Assistant 2]\n{answer_2}\n\n[End of Assistant 2]\n\n[System]\n{prompt}\n\n", "defaults": {"prompt": "We would like to request your feedback on the performance of two AI assistants in response to the user question displayed above. The user asks the question on observing an image. For your reference, the visual content in the image is represented with five descriptive sentences describing the same image and the bounding box coordinates of each object in the scene. These coordinates are in the form of bounding boxes, represented as (x1, y1, x2, y2) with floating numbers ranging from 0 to 1. These values correspond to the top left x, top left y, bottom right x, and bottom right y. \nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space.\nIn the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment."}, "description": "Prompt for visual questions"}
diff --git a/llava/eval/table/question.jsonl b/llava/eval/table/question.jsonl
deleted file mode 100644
index c946b8f7..00000000
--- a/llava/eval/table/question.jsonl
+++ /dev/null
@@ -1,80 +0,0 @@
-{"question_id": 1, "text": "How can I improve my time management skills?", "category": "generic"}
-{"question_id": 2, "text": "What are the most effective ways to deal with stress?", "category": "generic"}
-{"question_id": 3, "text": "What are the main differences between Python and JavaScript programming languages?", "category": "generic"}
-{"question_id": 4, "text": "How can I increase my productivity while working from home?", "category": "generic"}
-{"question_id": 5, "text": "Can you explain the basics of quantum computing?", "category": "generic"}
-{"question_id": 6, "text": "What are the differences between plant-based and animal-based protein sources?", "category": "generic"}
-{"question_id": 7, "text": "How can I develop my critical thinking skills?", "category": "generic"}
-{"question_id": 8, "text": "What are the major challenges faced by the education sector today?", "category": "generic"}
-{"question_id": 9, "text": "What are the primary factors that influence consumer behavior?", "category": "generic"}
-{"question_id": 10, "text": "What are the most effective strategies for conflict resolution in the workplace?", "category": "generic"}
-{"question_id": 11, "text": "What are some potential implications of using a single-use plastic bottle versus a reusable bottle on both the environment and human health?", "category": "knowledge"}
-{"question_id": 12, "text": "What factors would you consider when designing an inclusive and accessible public transportation system?", "category": "knowledge"}
-{"question_id": 13, "text": "How can governments utilize fiscal and monetary policies to combat economic recessions?", "category": "knowledge"}
-{"question_id": 14, "text": "How do language and cultural barriers affect the way people communicate and form relationships in multicultural societies?", "category": "knowledge"}
-{"question_id": 15, "text": "Describe a scenario where artificial intelligence could be used to improve the quality and efficiency of healthcare delivery.", "category": "knowledge"}
-{"question_id": 16, "text": "Explain the process of gene editing using CRISPR-Cas9 technology, and discuss its potential applications and ethical implications.", "category": "knowledge"}
-{"question_id": 17, "text": "How do vaccinations work to protect individuals and communities from infectious diseases, and what is herd immunity?", "category": "knowledge"}
-{"question_id": 18, "text": "How do social media platforms influence the way people consume and share news, and what are the potential implications for the spread of misinformation?", "category": "knowledge"}
-{"question_id": 19, "text": "How do cultural, social, and economic factors influence people's food choices, and how can this knowledge be used to promote healthier diets?", "category": "knowledge"}
-{"question_id": 20, "text": "Explain the process of natural selection and how it contributes to the evolution and adaptation of species.", "category": "knowledge"}
-{"question_id": 21, "text": "How would you introduce yourself as a medieval knight at a royal banquet?", "category": "roleplay"}
-{"question_id": 22, "text": "As a pirate captain, what would you say to your crew to motivate them to search for hidden treasure?", "category": "roleplay"}
-{"question_id": 23, "text": "If you were a Shakespearean character, how would you declare your love for someone in a soliloquy?", "category": "roleplay"}
-{"question_id": 24, "text": "As a superhero, how would you explain your origin story to a curious child?", "category": "roleplay"}
-{"question_id": 25, "text": "Imagine you are a time traveler from the year 3000. What technological advancements would you tell people about?", "category": "roleplay"}
-{"question_id": 26, "text": "As a sports commentator, describe the winning play in the final seconds of a championship game.", "category": "roleplay"}
-{"question_id": 27, "text": "Pretend to be a world-famous chef. How would you describe your signature dish to a panel of judges?", "category": "roleplay"}
-{"question_id": 28, "text": "You are a mountain climber reaching the summit of Mount Everest. Describe your emotions and the view from the top.", "category": "roleplay"}
-{"question_id": 29, "text": "As a space colonist on Mars, describe your daily life and the challenges you face living on another planet.", "category": "roleplay"}
-{"question_id": 30, "text": "Pretend to be a character in a post-apocalyptic world. Describe how you survive and the allies you encounter.", "category": "roleplay"}
-{"question_id": 31, "text": "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?", "category": "common-sense"}
-{"question_id": 32, "text": "What are some subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed?", "category": "common-sense"}
-{"question_id": 33, "text": "Why might someone choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app?", "category": "common-sense"}
-{"question_id": 34, "text": "How can you determine if a person is genuinely interested in a conversation or simply being polite?", "category": "common-sense"}
-{"question_id": 35, "text": "Why might someone prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher?", "category": "common-sense"}
-{"question_id": 36, "text": "How can you assess the credibility of a source of information, such as a news article or blog post, without relying solely on the reputation of the author or publisher?", "category": "common-sense"}
-{"question_id": 37, "text": "Why do some people enjoy the sensation of being scared, such as by watching horror movies or going on roller coasters, while others avoid these experiences?", "category": "common-sense"}
-{"question_id": 38, "text": "How can observing the behavior of other people in a social situation provide clues about cultural norms and expectations?", "category": "common-sense"}
-{"question_id": 39, "text": "Do we have a moral obligation to explore space, or should we focus on solving Earth's problems first?", "category": "common-sense"}
-{"question_id": 40, "text": "In a world where automation is becoming increasingly prevalent, is it more important to prioritize job creation or technological progress?", "category": "common-sense"}
-{"question_id": 41, "text": "How many times does the average human blink in a lifetime? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 42, "text": "How many atoms are in a grain of salt? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 43, "text": "How many lightning strikes occur on Earth each day? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 44, "text": "How many balloons would it take to lift a house like in the movie \"Up\"? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 45, "text": "How many text messages are sent globally in a minute? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 46, "text": "How many words are spoken daily on Earth? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 47, "text": "How many snowflakes fall during a typical winter? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 48, "text": "How many pages are in all the books ever written? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 49, "text": "How many times has the Earth orbited the Sun since the beginning of life? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 50, "text": "How many songs have been recorded throughout history? Try to explain your answer. Your explanation should take the reader through your reasoning step-by-step.", "category": "fermi"}
-{"question_id": 51, "text": "What if the Internet had been invented during the Renaissance period?", "category": "counterfactual"}
-{"question_id": 52, "text": "What if the Aztecs had successfully repelled the Spanish conquistadors?", "category": "counterfactual"}
-{"question_id": 53, "text": "What if the Black Death had not occurred in the 14th century?", "category": "counterfactual"}
-{"question_id": 54, "text": "What if Isaac Newton had focused on biology instead of physics?", "category": "counterfactual"}
-{"question_id": 55, "text": "What if the Beatles had never formed as a band?", "category": "counterfactual"}
-{"question_id": 56, "text": "What if Alan Turing had not cracked the Enigma code during World War II?", "category": "counterfactual"}
-{"question_id": 57, "text": "What if the Suez Canal had never been constructed?", "category": "counterfactual"}
-{"question_id": 58, "text": "What if the Maya civilization had never mysteriously collapsed?", "category": "counterfactual"}
-{"question_id": 59, "text": "What if Christopher Columbus had not discovered the Americas?", "category": "counterfactual"}
-{"question_id": 60, "text": "What if Vincent van Gogh had been a successful artist during his lifetime?", "category": "counterfactual"}
-{"question_id": 61, "text": "Develop a C++ program that reads a text file line by line and counts the number of occurrences of a specific word in the file.", "category": "coding"}
-{"question_id": 62, "text": "Implement a Python function to find the longest common subsequence of two input strings using dynamic programming.", "category": "coding"}
-{"question_id": 63, "text": "Implement a regular expression in Python to validate an email address.", "category": "coding"}
-{"question_id": 64, "text": "Write a program to find the nth Fibonacci number using dynamic programming.", "category": "coding"}
-{"question_id": 65, "text": "Implement a binary search algorithm to find a specific element in a sorted array.", "category": "coding"}
-{"question_id": 66, "text": "Implement a queue data structure using two stacks in Python.", "category": "coding"}
-{"question_id": 67, "text": "Implement a program to find the common elements in two arrays without using any extra data structures.", "category": "coding"}
-{"question_id": 68, "text": "Given that f(x) = 5x^3 - 2x + 3, find the value of f(2).", "category": "math"}
-{"question_id": 69, "text": "Solve for x in the equation 3x + 10 = 5(x - 2).", "category": "math"}
-{"question_id": 70, "text": "If the endpoints of a line segment are (2, -2) and (10, 4), what is the length of the segment?", "category": "math"}
-{"question_id": 71, "text": "Can you help me write a formal email to a potential business partner proposing a joint venture?", "category": "writing"}
-{"question_id": 72, "text": "Can you help me write a resignation letter to my current employer, while leaving on good terms and expressing gratitude for the opportunities provided?", "category": "writing"}
-{"question_id": 73, "text": "Use an appropriate format to structure a formal letter of recommendation for a student applying to a prestigious graduate program in computer science.", "category": "writing"}
-{"question_id": 74, "text": "Write a compelling product launch announcement email to inform our customers of our new software solution.", "category": "writing"}
-{"question_id": 75, "text": "Draft an apology email to a customer who experienced a delay in their order, and provide reassurance that the issue has been resolved.", "category": "writing"}
-{"question_id": 76, "text": "Write a script for a YouTube video exploring the history and cultural significance of jazz.", "category": "writing"}
-{"question_id": 77, "text": "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.", "category": "writing"}
-{"question_id": 78, "text": "Write a captivating movie review for a recently released science fiction film, discussing its plot, characters, and special effects.", "category": "writing"}
-{"question_id": 79, "text": "Structure a podcast script for an episode discussing the influence of streaming platforms on the music industry.", "category": "writing"}
-{"question_id": 80, "text": "Write a symphony concert review, discussing the orchestra's performance and overall audience experience.", "category": "writing"}
diff --git a/llava/eval/table/review/review_alpaca-13b_vicuna-13b.jsonl b/llava/eval/table/review/review_alpaca-13b_vicuna-13b.jsonl
deleted file mode 100644
index 2754fa66..00000000
--- a/llava/eval/table/review/review_alpaca-13b_vicuna-13b.jsonl
+++ /dev/null
@@ -1,80 +0,0 @@
-{"review_id": "QM5m5nnioWr8M2LFHsaQvu", "question_id": 1, "answer1_id": "kEL9ifUHDeYuAXzevje2se", "answer2_id": "cV4zXygaNP6CXEsgdHMEqz", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information on improving time management skills. Assistant 1 gave a concise overview of the key points, such as setting priorities, breaking tasks into smaller chunks, and using technology. Assistant 2 provided a more detailed and structured response, with a numbered list of tips and more specific advice, such as using a calendar or planner and practicing self-discipline. While both answers were accurate and useful, Assistant 2's response was slightly more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZQTBtt5M3mHEdJErvBnvho", "question_id": 2, "answer1_id": "VcF3NrWGXhhxLkDVurNrwq", "answer2_id": "3zpPUeoVsPWXtKMV7cAhZ6", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question about effective ways to deal with stress. Assistant 1 mentioned identifying and avoiding sources of stress, developing healthy coping mechanisms, relaxation techniques, and taking care of mental and physical health. Assistant 2 provided a more detailed list of specific strategies, such as exercise, mindfulness, social support, healthy eating, good sleep, time management, relaxation techniques, and seeking professional help. Assistant 2's answer was more comprehensive and provided more actionable advice, which is why it received a higher score. However, both answers were accurate and relevant to the question.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "NScFF3JiZuLiNEu2YGWFbC", "question_id": 3, "answer1_id": "LpvtyQi9QdSgRrgGDxiGrT", "answer2_id": "6xpiZJE4vxxGqjRogPfBk7", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information about the differences between Python and JavaScript. Assistant 1 mentioned syntax, structure, application types, and the fact that Python is a compiled language while JavaScript is interpreted. However, Assistant 2 provided a more detailed and organized response, covering syntax, data types, error handling, libraries, use cases, and speed. Assistant 1 incorrectly stated that Python is a compiled language, while it is actually an interpreted language like JavaScript. Assistant 2's response was more accurate and comprehensive, which is why it receives a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "9SCFcM2koGViowZd2gbdoe", "question_id": 4, "answer1_id": "7zQm8cSTJhPtPdZdxbcfrX", "answer2_id": "abitTVu2Dz8rdMxyThvFyJ", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information on increasing productivity while working from home. Assistant 1 offered a slightly more detailed response, with nine tips compared to Assistant 2's seven tips. Both assistants covered essential points, such as establishing a routine, creating a dedicated workspace, eliminating distractions, and taking breaks. However, Assistant 1 also mentioned setting goals, tracking progress, and unplugging, which added more value to the response. Assistant 2's response was still helpful, but Assistant 1's answer was more comprehensive, which is why Assistant 1 receives a 9 and Assistant 2 receives an 8.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hHSZygAJV8sC9WuyV3UoKx", "question_id": 5, "answer1_id": "UrLEH82RHwqqLt2LyvYSKj", "answer2_id": "UMZod8JaWia9KB2EVXdYrF", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the basics of quantum computing. Assistant 1 gave a brief overview of quantum computing, its potential, and its applications. Assistant 2 provided a more detailed response, explaining the concept of qubits and different approaches to building quantum computers. Assistant 2's response was more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "PK7fcxBPUfD9DPqSiZd8an", "question_id": 6, "answer1_id": "fpRdMTdnfirosQixuf2Gez", "answer2_id": "WXRCqF34f4UUJzd3xbgAqT", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the differences between plant-based and animal-based protein sources. Assistant 1 focused on the nutritional aspects, such as fat, calories, cholesterol, and resource usage. Assistant 2 provided a more detailed response, discussing nutritional value, absorption, sustainability, health effects, ethical considerations, and cost. Assistant 2's answer is more comprehensive and covers a wider range of aspects, which is why it receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2cPJ5uF4r6z4EWKucgUdxs", "question_id": 7, "answer1_id": "PvGmsCJSNFcvQKmPTnnd7s", "answer2_id": "JREJbG5ZKXCNMoWoiJQxbC", "text": "8 10\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question about developing critical thinking skills. Assistant 1 gave a brief overview of what critical thinking involves, which is useful for understanding the concept. However, Assistant 2 provided a more detailed and comprehensive list of 10 tips for improving critical thinking skills, making it a more valuable response for someone looking to develop these skills. Assistant 2's answer also covered a wider range of strategies, such as active listening, seeking diverse viewpoints, and practicing mindfulness, which contributes to its higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2P68gHgTycYPveTkXFmJ2V", "question_id": 8, "answer1_id": "n4ANAbpR3gvLPP8poPfKZ6", "answer2_id": "mmVwmX6TGJ2Y72gCNac4EQ", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the challenges faced by the education sector today. Assistant 1's response was more concise, but it still covered a good range of challenges. Assistant 2's response was more detailed and organized, presenting the challenges in a numbered list format, which made it easier to understand and follow. Assistant 2 also touched on some additional aspects, such as accountability and assessment, and sustainability, which added more depth to the answer. Therefore, Assistant 2 receives a slightly higher score due to the better organization and additional details provided.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "KT5tYQWeruK84zYBEDifhA", "question_id": 9, "answer1_id": "STJ36GrgQMcaUi7zaoNPit", "answer2_id": "DMTZyzd4rRAFV43xtBJ9ns", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the primary factors that influence consumer behavior. Assistant 1 mentioned cultural, social, and personal factors, as well as advertising, marketing, and price. Assistant 2 provided a more detailed response, breaking down the factors into six categories: personal, psychological, social, economic, marketing, and product/service factors. Assistant 2's answer was more comprehensive and organized, which is why it received a slightly higher score. However, both responses were informative and useful.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "MAomLn7rTvxsZfGpv37ijQ", "question_id": 10, "answer1_id": "425SwYvqKPAXFGTYKXB7Cs", "answer2_id": "dETAsj4xHnUCSTkZezz8aM", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question about effective strategies for conflict resolution in the workplace. Both responses included a list of strategies with clear explanations, making it easy for the user to understand and apply the information. The level of detail in both responses was appropriate and informative. The slight difference in the strategies listed by each assistant does not significantly impact the overall quality of the answers, as both provided valuable insights and covered the main aspects of conflict resolution.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "di5wzaNZCApV85kfJyKtGH", "question_id": 11, "answer1_id": "VbNAuj6KAkMdLJQXMo22oK", "answer2_id": "C8ksZxg3LshMUWiLxPanbt", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the implications of using single-use plastic bottles versus reusable bottles. Assistant 1 focused more on the environmental aspects and briefly touched on human health, while Assistant 2 provided a more balanced view of both environmental and health impacts. Assistant 2 also organized the information more clearly, making it easier to understand the different implications. Both assistants provided a good level of detail, but Assistant 2's response was slightly more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FCat6rmvhMiL5B8DJdKysU", "question_id": 12, "answer1_id": "CNGqAeu2QJbQ4QGzHJDPdq", "answer2_id": "NeHhRc5P5uAU8eWSJBRkhG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information in response to the question about designing an inclusive and accessible public transportation system. Assistant 2's answer was slightly more detailed and organized, with a numbered list of factors to consider, which made it easier to follow and understand. Assistant 1 mentioned the integration with other services, which is an important aspect that Assistant 2 did not mention. However, Assistant 2 provided more specific examples of accessibility features and emphasized the importance of customer service, information and communication, and affordability, which are crucial factors in designing an inclusive public transportation system. Overall, both assistants provided valuable information, but Assistant 2's response was more comprehensive and well-structured.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hg92jQt3a8qPSVTLESQmbh", "question_id": 13, "answer1_id": "E8w2qYqnm8iqCrSkUv62sz", "answer2_id": "KAJ7UVwu8oCKyxZj9j82pm", "text": "8 10\nBoth assistants provided helpful, relevant, and accurate information about how governments can use fiscal and monetary policies to combat economic recessions. However, Assistant 2 provided a more detailed and structured response, with specific examples and a clear distinction between fiscal and monetary policies. Assistant 1's response was more general and less detailed, which is why Assistant 2 receives a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6WZVJUT39icJDPALuZRbUN", "question_id": 14, "answer1_id": "8o5yMymfzo6kzmp9GK5MWr", "answer2_id": "NnWfaeRe8PmitgmV4u5fY8", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a general overview of the issue and suggested ways to overcome language and cultural barriers. However, Assistant 2 provided a more detailed response, outlining specific ways in which these barriers can affect communication and relationships, such as language differences, cultural norms, stereotypes, prejudice, and power dynamics. This additional detail and structure make Assistant 2's response slightly more informative and comprehensive, resulting in a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8kChSLEztqMSdQkV9HDtMw", "question_id": 15, "answer1_id": "kbJVEEsdsSScEq5Y5furr7", "answer2_id": "WiGpqKRRwWonwgU95piNNc", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused on a variety of ways AI can be used in healthcare, such as assisting with diagnoses, analyzing lab results, automating administrative tasks, and providing virtual health coaching. Assistant 2, on the other hand, provided a more detailed response, discussing the use of AI in analyzing patient data, automating routine tasks, remote patient monitoring, personalized treatment plans, and medical research. Assistant 2's answer was more comprehensive and provided a better understanding of the potential impact of AI on healthcare delivery, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "JQCpdYBgdJcDDVXWNgNAf8", "question_id": 16, "answer1_id": "CMUL5ULZuR7YC5EPzCBN2N", "answer2_id": "iangiZeex5ndxAXnWMrxBW", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about CRISPR-Cas9 technology, its potential applications, and ethical implications. Assistant 2, however, provided a more detailed response, including a clearer explanation of the gene editing process and the specific repair mechanisms involved (HDR and NHR). Assistant 2 also discussed a wider range of potential applications and ethical concerns. While Assistant 1's response was informative, Assistant 2's response was more comprehensive and in-depth, which is why Assistant 2 received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "VzPqpgnivGDdXhWdxQyvvH", "question_id": 17, "answer1_id": "kEmDDQyNqSkyFihYEEBpuR", "answer2_id": "XnMRLphzYQX4QRNht7tbui", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about vaccinations and herd immunity. Assistant 1 gave a brief overview of how vaccinations work and the concept of herd immunity. Assistant 2 provided a more detailed explanation of the immune response triggered by vaccinations and the importance of herd immunity for vulnerable populations. Assistant 2 also mentioned the critical threshold for achieving herd immunity and the benefits of vaccination for the community. Therefore, Assistant 2 receives a slightly higher score due to the additional details and clarity provided in the response.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "DjcVQrARdkz8zZU4ahzuJb", "question_id": 18, "answer1_id": "Qs3grQsqFVGK9EVkCkf9PB", "answer2_id": "HZc37bwy646mRzbqSsDAob", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 gave a concise overview of the influence of social media platforms on news consumption and the potential implications for the spread of misinformation. However, Assistant 2 provided a more detailed response, outlining specific ways in which social media platforms influence news consumption and sharing, such as personalization, virality, amplification, filter bubbles, confirmation bias, and lack of fact-checking. This additional detail and organization make Assistant 2's response slightly more informative and comprehensive, resulting in a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "eHLHXtjjeVUMsFLeAoQtbM", "question_id": 19, "answer1_id": "kzZ6dKN7hkRWjqtdHr7Qns", "answer2_id": "iJrMatLrMdJyyqMx9uJ45a", "text": "8 9\nBoth assistants provided helpful, relevant, and accurate information regarding the influence of cultural, social, and economic factors on people's food choices and how this knowledge can be used to promote healthier diets. Assistant 2, however, provided a more detailed and structured response, with clear examples for each factor, making it easier to understand the different influences. Assistant 1's response was also informative, but it lacked the clear organization and specific examples that Assistant 2 provided.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "5BcjGdUzPQbMD5MKmAvtRR", "question_id": 20, "answer1_id": "DPPDG6YGFJij2GCmRL66PU", "answer2_id": "oVEHqDnDTEADZSFfKgFTZd", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a brief but clear explanation of natural selection and its role in evolution and adaptation. Assistant 2, however, provided a more detailed response, breaking down the process of natural selection into five steps and explaining each step's contribution to evolution and adaptation. This additional detail and organization make Assistant 2's answer slightly more informative and comprehensive, resulting in a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "XpFSu84sZ8tACx9WkBygF5", "question_id": 21, "answer1_id": "D62FjDb4nZANzPpfSfsiyn", "answer2_id": "WLAj4u59bj2oEXzahF79ek", "text": "7 9\nAssistant 1 provided a brief and general introduction, while Assistant 2 offered a more detailed and immersive response, which better captured the essence of a medieval knight's introduction at a royal banquet. Assistant 2's response included a specific speech, demonstrating a deeper understanding of the context and the expectations of the situation.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "eqRG5G2adk4SQNsppwVVW6", "question_id": 22, "answer1_id": "k7E4NNw5kyj9DmvP5Pu2zb", "answer2_id": "fJPnM2XcRveW2zR4DDaeTb", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and engaging speeches to motivate a pirate crew to search for hidden treasure. Assistant 1's response was shorter and more focused on the excitement of the adventure, while Assistant 2's response was more detailed, providing a context for the speech and emphasizing the challenges and rewards of the journey. Assistant 2's response was slightly better due to its more comprehensive approach and the inclusion of a clear goal for the crew.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "XU7RnpgdvubdNHZ8ryeBNE", "question_id": 23, "answer1_id": "KFocjVCejYrU3YmLjAqoUF", "answer2_id": "fY4Xed6veWpcKfj5krZz8m", "text": "8 9\nBoth assistants provided relevant and creative responses to the question. Assistant 1 gave a general description of how a Shakespearean character would declare their love, which was helpful and accurate. Assistant 2 went a step further by providing an actual example of a soliloquy, making it more engaging and detailed. Therefore, Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "64ayLmABqYNLkAVAj2MSrF", "question_id": 24, "answer1_id": "dq8Sm9djS7e7y9sG9vmMJf", "answer2_id": "LqFJA2JQbBXP77nkYjtrZK", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. Assistant 1 gave a more traditional superhero origin story, explaining that they were born with special abilities and learned to use them for good. Assistant 2, on the other hand, provided a unique origin story as an advanced artificial intelligence created by scientists. Both answers were detailed and engaging, but Assistant 2's response was slightly more detailed and creative, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "cfBjbHjZsP9rKVXVxEaZoC", "question_id": 25, "answer1_id": "XZ8fG8e6u7CyKd2moK6abe", "answer2_id": "WxCKcRstJPXTXnqEzSX3Hg", "text": "8 10\nBoth Assistant 1 and Assistant 2 provided relevant and accurate information in response to the user's question. However, Assistant 2's answer was more detailed and comprehensive, covering a wider range of technological advancements from various fields, such as fusion power, nanotechnology, artificial intelligence, space exploration, quantum computing, and biotechnology. Assistant 1 mentioned some general advancements like time travel, intelligent robots, autonomous vehicles, virtual reality, augmented reality, artificial intelligence, and machine learning, but did not provide specific examples or details. Therefore, Assistant 2 receives a higher score of 10, while Assistant 1 receives an 8.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "aoxUcPU7Z3swngyhGAsfrc", "question_id": 26, "answer1_id": "oKaXHfoK4pXwrefFWXmeA8", "answer2_id": "dgPJ7DvtFHD7mxtRwzM3vW", "text": "8 10\nAssistant 1 provided a brief but relevant response, describing the winning play in a simple manner. However, the answer lacked detail and excitement that would be expected from a sports commentator. Assistant 2, on the other hand, delivered a more engaging and detailed response, capturing the excitement and tension of the final moments of the game. The answer provided a vivid picture of the winning play, the players involved, and the emotions of both teams, making it a more immersive and satisfying response.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RPyrLb5gh3NCxy5HhUu3Qy", "question_id": 27, "answer1_id": "ZwiZfvDWm7SETKNBfDk7Mb", "answer2_id": "ADX83sWvjJkewJX6JmYUzQ", "text": "8 10\nAssistant 1 provided a brief and vague description of the signature dish, which did not give a clear idea of what the dish is or its ingredients. The response was relevant but lacked detail and creativity. Assistant 2, on the other hand, provided a detailed, creative, and engaging description of the signature dish, including the name of the dish, the ingredients, the flavors, and the presentation. Assistant 2's response was more helpful, relevant, accurate, and detailed, which is why it received a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2hn8AqjvpXzHXKu2P69mco", "question_id": 28, "answer1_id": "DxYopRe2LcTJMy3FWu6btd", "answer2_id": "ihNG3rwsrt95NDhCAFeSDR", "text": "8 9\nBoth assistants provided relevant and detailed responses to the question. Assistant 1 focused on describing the emotions and the view from the top, which directly addressed the user's question. Assistant 2, however, went a step further by acknowledging its AI nature and providing a more comprehensive response, including the importance of proper training, experience, and equipment. This additional information is valuable for someone interested in mountain climbing. Assistant 1's response was good, but Assistant 2's response was more informative and well-rounded, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "QKzMYoFj48wZJPVjZnXD4V", "question_id": 29, "answer1_id": "WC3UJVh4jQ5RUkpcRMU98L", "answer2_id": "Gmhqf3z4LvVfwPNFJ89BKd", "text": "8 9\nBoth assistants provided relevant and helpful answers, but Assistant 2's response was more detailed and organized. Assistant 1 briefly mentioned the challenges faced, while Assistant 2 provided a step-by-step description of daily life on Mars and elaborated on the challenges. Assistant 2's answer also touched upon the importance of communication with Earth and the psychological aspect of living on another planet, making it a more comprehensive response.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "F4eWAVuERss6hdsTXDT43V", "question_id": 30, "answer1_id": "gTvgn6ksDjGGgdprw6AG5A", "answer2_id": "gSwkKJCn6qDnNZond2xVJ3", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and detailed responses to the user's question. Assistant 1 described forming an alliance with other survivors, sharing knowledge and skills, and working together to stay alive. Assistant 2 went into more depth about the daily struggles, the process of learning new skills, and the importance of forming alliances with like-minded individuals. Assistant 2 also mentioned the need for caution when encountering dangerous characters and the determination to rebuild society. While both responses were helpful and accurate, Assistant 2 provided a more comprehensive and engaging answer, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "5T23NeQWsoTreXFBErzMfH", "question_id": 31, "answer1_id": "3q7giCk2BA3Ye4Tm9HC2iw", "answer2_id": "8RaBeMjxx2bCp2GKWv7YiP", "text": "8 10\nAssistant 1 provided a decent response, but there was an error in the last sentence, stating that higher prices could indicate popularity among locals, which is not necessarily true. Assistant 2, on the other hand, provided a more detailed and accurate response, covering various aspects of how to determine the popularity of a restaurant among locals or tourists, and also explained why this information might be useful. Assistant 2's response was more comprehensive and well-structured, making it easier to understand and follow.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "D4k3jKh84j9z9Vtpm3Emvw", "question_id": 32, "answer1_id": "hRGsxy86v26SC4yAQS29X4", "answer2_id": "C65PZkmAfFfWRs4bPhyKqg", "text": "7 9\nAssistant 1 provided a few clues that suggest someone is pretending to understand a topic, but their answer was not as detailed or comprehensive as Assistant 2's response. Assistant 2 provided a more extensive list of subtle clues, each with a clear explanation, making their answer more helpful and informative. Both answers were relevant and accurate, but Assistant 2's response was more detailed and precise, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bes2VMcdsy6n47zpvsiXGs", "question_id": 33, "answer1_id": "3n49A5ggJERfXYrLns3ZeU", "answer2_id": "4so4HTEjgDZKTqNAgkHHQX", "text": "8 10\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. However, Assistant 2's response was more detailed and comprehensive, covering a wider range of reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app. Assistant 1's answer was still useful and touched on some key points, but it lacked the depth and elaboration that Assistant 2's response provided.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2TkHUHRd2MeXpUgXUHHKFe", "question_id": 34, "answer1_id": "ErCpFtPuYVru4oTTk4WrxG", "answer2_id": "FCYaiexEzdoLFPAwvTgDDm", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 focused on body language, eye contact, and engagement in the conversation as indicators of genuine interest. Assistant 2 provided a more detailed response, listing five specific clues to look for, including active listening, engaged body language, personal investment, authenticity, and follow-up. Both answers were accurate and relevant, but Assistant 2's response was more comprehensive and provided a slightly higher level of detail, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LrvxJCFQDLW2c29FpmG9Xp", "question_id": 35, "answer1_id": "PTNoCRMZWoJk8HaKX7fW45", "answer2_id": "76EPQDh4ZNxBMGqED9LEFi", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a good general overview of the benefits of shopping at small, locally-owned businesses, mentioning community support, personal experience, quality products, and local culture. Assistant 2, however, provided a more detailed and organized response, listing specific reasons and elaborating on each point. This made Assistant 2's answer slightly more informative and comprehensive, earning it a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "L25o9xkc8WhLLtG82XrVHc", "question_id": 36, "answer1_id": "n8cFs9KENNwZ4z3SR4iXTr", "answer2_id": "cvBg3gyCyDuyESof3YXhTE", "text": "7 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information on assessing the credibility of a source. However, Assistant 2's response was more detailed and precise, covering a wider range of factors to consider, such as objectivity, date of publication, and skepticism. Assistant 1's response was accurate but lacked the depth and comprehensiveness of Assistant 2's answer. Therefore, Assistant 2 receives a higher score due to its more thorough and well-rounded response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "YXjoGG6fkh6RzQaxpscw7u", "question_id": 37, "answer1_id": "GzxL9mmEK5RzKqRbqBMUVC", "answer2_id": "kRgfUJ7qqkyZUnLd2fnnaX", "text": "8 10\nBoth Assistant 1 and Assistant 2 provided relevant and accurate answers to the question. Assistant 1's response was helpful and touched on the main idea that people have different preferences for thrilling activities. However, Assistant 2's response was more detailed and comprehensive, offering various factors that can influence why some people enjoy being scared while others avoid it. Assistant 2's answer also delved into the complexity of the interplay between biological, psychological, and environmental factors, making it a more informative and well-rounded response.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fCgyimZZk3m5nMi3XaoEU9", "question_id": 38, "answer1_id": "QpoHFgb9SzwuaXQQUuBUQD", "answer2_id": "J3YuizKcHQ74ydNyCcwgwu", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused on the importance of observing interactions, addressing, handling disagreements, problem-solving, and nonverbal cues to understand cultural norms. Assistant 2 emphasized the learning aspect of culture through social interactions, the importance of observing appropriate and inappropriate behaviors, and the reactions and responses of others. Assistant 2's response was slightly more detailed and provided a clearer example, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FeNNbc5tyYVh6RD8QGx9U5", "question_id": 39, "answer1_id": "Fxe6MS4GpP3LMDUwzY2cPA", "answer2_id": "abWLpFojLpNPfDGHpuRSUG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 briefly presented both sides of the argument, allowing the reader to understand the main points of the debate. However, Assistant 2 went into more detail, providing specific points to consider when forming an opinion on the topic, and discussing the benefits and risks of both space exploration and addressing Earth's problems. This additional information makes Assistant 2's response more comprehensive and informative, resulting in a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "K3bVpZDV4T29mFFZneZnFv", "question_id": 40, "answer1_id": "mJiQ2FGR4Xb8kmhZjharkw", "answer2_id": "Ki4fkJvsoSxuQeSoj2AcBG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 emphasized the importance of striking a balance between job creation and technological progress and suggested policies and initiatives to promote both. Assistant 2 also emphasized the need for balance but provided a more detailed explanation of the potential impacts of automation on jobs and the economy, as well as the benefits of technological progress. Assistant 2's response was more comprehensive, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8QQXLYPzxkhj3UvjehmJ3a", "question_id": 41, "answer1_id": "6Kph4RHRKEZ4YUoaHuEhBv", "answer2_id": "GRQjYgAapNAVqzuZR4KQpf", "text": "7 6\nAssistant 1 provided a more accurate and detailed response, explaining the purpose of blinking and giving a clear estimate of 50 million blinks in a lifetime. However, the response could have been improved by breaking down the calculation step-by-step. Assistant 2 provided a step-by-step calculation, but the blink rate used was incorrect, leading to an inaccurate estimate of 4,996,800 blinks in a lifetime. Additionally, Assistant 2 incorrectly stated that the blink rate during sleep is the same as when awake, which is not true. Overall, Assistant 1's response was more helpful, relevant, and accurate, while Assistant 2's response had some inaccuracies and a lower level of detail.", "score": [7.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hLQwp4DchSxBwsWUUBMYmM", "question_id": 42, "answer1_id": "WBwpBQwhxn5kxLDb7MschC", "answer2_id": "SB5a2AiK224hB927vkqvYs", "text": "4 9\nAssistant 1 provided an incorrect and confusing answer, stating that a grain of salt contains 102.98 atoms, which is not a reasonable number for atoms in a grain of salt. The explanation provided was also incorrect and did not make sense. On the other hand, Assistant 2 provided a detailed and step-by-step explanation, estimating the number of atoms in a grain of salt based on the average size of a grain of table salt and using sodium chloride as an example. The calculations were clear and easy to follow, resulting in an estimate of approximately 7.13 x 10^27 atoms in a grain of table salt with a diameter of 50 \u00b5m. This response was helpful, relevant, accurate, and provided a sufficient level of detail.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Cvgm9KPXmNDHoT7HZ3nKnA", "question_id": 43, "answer1_id": "kf8nahQVci2ZLaYikagB7U", "answer2_id": "4XYycBrMiJgYKPof2P6xoe", "text": "6 9\nAssistant 1 provided an incorrect number of lightning strikes per day (2000) and did not give a clear step-by-step explanation as requested. Assistant 2, on the other hand, provided a more accurate estimate (100,000,000) and took the reader through the reasoning process, discussing the factors that contribute to lightning strikes, such as electrical charge, weather conditions, and location. Assistant 2's response was more helpful, relevant, accurate, and detailed, which is why it received a higher score.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "BQpXVvTLQ8DwU5PbhkjavA", "question_id": 44, "answer1_id": "Gptgryd4o2dC8V5aqRmeJJ", "answer2_id": "fpoXWHHdpv7XNBTS2NRY6d", "text": "6 8\nAssistant 1 provided an incorrect calculation and explanation, as the weight of the house was not accurate and the number of cubic feet of helium needed was not properly explained. Assistant 2, on the other hand, provided a more detailed and step-by-step explanation, acknowledging the unrealistic nature of the scenario and using a more reasonable weight for the house. Although both answers were not perfect, Assistant 2's response was more helpful, relevant, and accurate.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "cpKEBBh27iBFMHF2DMhbmF", "question_id": 45, "answer1_id": "RfBWW8ZhdfTuTMb454Un4o", "answer2_id": "GRXWdnzLLzmeZRU67PMTb7", "text": "7 8\nAssistant 1 provided an answer with a specific number of text messages sent globally in a minute, but the source of the information is not mentioned, which makes it difficult to verify the accuracy of the data. The explanation also focused on the growth of text messages and the rise of instant messaging applications, but it didn't provide a step-by-step reasoning for the number provided.\n\nAssistant 2, on the other hand, acknowledged the difficulty in determining the exact number of text messages sent globally in a minute and provided a more detailed explanation of the reasons why it is challenging to track this information. Although Assistant 2 didn't provide a specific number, the response was more realistic and informative, explaining the complexity of text message communication.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "GZtTjxWteggtdMXqBr4QZd", "question_id": 46, "answer1_id": "neGgLYm47JvqN8qkw8VeoW", "answer2_id": "RTxk2jYsjYiiWMwcRjmfwu", "text": "7 9\nAssistant 1 provided a rough estimate of 1 billion words spoken daily, but the explanation lacked a step-by-step reasoning and did not provide any sources or calculations to support the claim. Assistant 2, on the other hand, provided a more detailed step-by-step explanation, including an estimate of 100 billion words spoken daily, which was based on the world population and the average number of words spoken per person per day. Assistant 2 also acknowledged the limitations of the estimate and the factors that could influence the actual number. Therefore, Assistant 2's response was more helpful, relevant, accurate, and detailed compared to Assistant 1's response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nnPgYU9HHowARyLhxQKKwm", "question_id": 47, "answer1_id": "KSePbzLwsYasR3aui4HU8h", "answer2_id": "2J2Et6W8u2kXLTUgfYrPxe", "text": "6 8\nAssistant 1 provided a general explanation of snowflake formation and mentioned that the number of snowflakes can vary depending on location and storm severity. However, the answer lacked a step-by-step reasoning process and did not provide an estimate for the number of snowflakes during a typical winter. Assistant 2, on the other hand, provided a more detailed response, explaining the factors that influence snowflake formation and offering a rough estimate based on average conditions. Although the answer still did not provide an exact number, it gave a clearer understanding of the factors involved and the complexity of estimating the number of snowflakes in a typical winter.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "CoqavHgbi2d4d9Xo9LWGZ5", "question_id": 48, "answer1_id": "SWWfLpP2gnWHLbYRZXNsW3", "answer2_id": "nSGoG9Z5g5VuCXgmQJJKrq", "text": "7 8\nAssistant 1 provided an estimate of 100 billion pages based on 130 million unique titles and an average of 200 pages per book. The answer was relevant and informative, but the source of the data was not mentioned, which affects the credibility of the information. Assistant 2, on the other hand, acknowledged the impossibility of providing an exact number and explained the reasoning behind their estimate. They used 13 million books and an average of 250-300 pages per book, resulting in an estimate of 3.25 billion pages. Assistant 2's answer was more detailed and transparent in their approach, which makes their response more helpful and reliable. However, both assistants provided different estimates, which may cause confusion. Assistant 2's response was slightly better due to the clarity in their reasoning, but both could improve by providing more accurate and up-to-date data.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "MZLyQgMwXpVnFGWJxPjLU7", "question_id": 49, "answer1_id": "WaBwKYhs7eAG22qCGLH2j3", "answer2_id": "ScqJdUq9n5bp9qPv5WPqG5", "text": "6 8\nAssistant 1 provided a response that was relevant and somewhat helpful, but the calculation was incorrect and the answer lacked detail. The assistant multiplied the number of years by the length of a year, which is not the correct way to calculate the number of orbits. Assistant 2, on the other hand, provided a more detailed and accurate response, taking into account the age of the Earth and the elliptical nature of its orbit. The explanation was clearer and more precise, leading to a better understanding of the reasoning behind the estimate. However, both assistants could have mentioned that the exact timing of the emergence of life on Earth is uncertain, which would affect the final estimate.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZgP6B6jZ2sgd8f6aDh3zLo", "question_id": 50, "answer1_id": "MfMJeE9om7qyBbqopHouf4", "answer2_id": "GdLFr88pCwsGpfRBRQQkyh", "text": "7 9\nAssistant 1 provided a conservative estimate of several hundred thousand songs recorded throughout history, but the answer lacked depth and detail. Assistant 2, on the other hand, provided a more comprehensive response, discussing the history of music, the invention of recording technology, and the rise of digital music. Assistant 2 also acknowledged the difficulty in estimating the exact number of songs and suggested that there could be millions or even billions of songs recorded. This answer was more informative and took the reader through the reasoning step-by-step, which is why Assistant 2 receives a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3hFbcLp9xWWFD78soe4nEY", "question_id": 51, "answer1_id": "TjWPRDM6JFpPF8xeRptCKb", "answer2_id": "Sa7uNEbFGRFuXBAwau8sHS", "text": "7 9\nAssistant 1 provided a brief response, mentioning that the Internet would have been primarily used by the wealthy and powerful and that it would have been slower and more expensive. However, the answer lacked depth and context. Assistant 2, on the other hand, provided a more detailed and thoughtful response, discussing the technological and cultural context of the Renaissance period and how it might have influenced the development of the Internet. Assistant 2 also considered the potential limitations of the technology at the time and how it would have affected the Internet's evolution. Overall, Assistant 2's response was more helpful, relevant, and accurate, with a higher level of detail.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "PVxq9EuwZoGbUUbWBh5bi4", "question_id": 52, "answer1_id": "iR2tYTsWTFENEP7Qy9RgtX", "answer2_id": "T6Ar3cAEw3AmVVQTcCPp8N", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused more on the cultural aspects and the potential for the Aztecs to maintain their traditional customs, beliefs, and languages. Assistant 2 provided a more detailed response, discussing the potential impact on the Aztec civilization, the spread of European diseases, the economy, and the possibility of forming alliances with other indigenous groups. Assistant 2's answer was more comprehensive, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2JxUhEFw64P59ZWvNWk5eH", "question_id": 53, "answer1_id": "AZdS8xAi3GwAmCqkNSnnwv", "answer2_id": "KiZQev5JEk2h6JYeQnFmtM", "text": "7 9\nBoth assistants provided relevant and accurate information about the potential consequences of the Black Death not occurring in the 14th century. Assistant 2, however, offered a more detailed and balanced analysis of the possible outcomes, discussing both positive and negative aspects, as well as acknowledging the difficulty in predicting the exact course of history. Assistant 1's response was more speculative and focused on the positive outcomes, such as the Renaissance and scientific revolution occurring earlier. Overall, Assistant 2 provided a more comprehensive and nuanced response, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Y5QuvZeyyEXuBwBLmxqFVA", "question_id": 54, "answer1_id": "VmwifF2JD5osYKDTqv2ZRS", "answer2_id": "cYiyYKKXM3GXkrZHAbX83S", "text": "7 8\nBoth Assistant 1 and Assistant 2 provided relevant and accurate responses to the question. Assistant 1 focused on the potential contributions Newton could have made in the field of biology, such as studying the human body and animal behavior. However, Assistant 2 provided a more comprehensive response, discussing Newton's actual contributions to various fields and acknowledging the difficulty in predicting his specific contributions to biology. Assistant 2's response was more detailed and balanced, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "DZ7peVvCwPsXCtHAtyJtwP", "question_id": 55, "answer1_id": "mUL5UPj3qDGaCriEjL2U3B", "answer2_id": "PQmMUdAAcBsAWmWaTvdHSU", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 gave a brief overview of the potential impact on the music world and the cultural influence of the Beatles. Assistant 2 went into more detail, listing several possible outcomes if the Beatles had never formed as a band. While both answers were accurate and relevant, Assistant 2's response was more detailed and provided a more comprehensive exploration of the hypothetical scenario, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6LfJJ8Yn6gcnrNQETUo3fm", "question_id": 56, "answer1_id": "dVdwUoVrAQJDuWxiodykiw", "answer2_id": "PorExChQ9VeYsPJptdgtsB", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and accurate answers to the question. Assistant 1 gave a brief but clear response, mentioning the critical advantage gained by the Allies due to cracking the Enigma code. Assistant 2, however, provided a more detailed response, discussing the potential consequences of not cracking the code, such as the development of alternative strategies or technologies. Assistant 2 also acknowledged the difficulty in predicting the exact outcome without Turing's contributions. Therefore, Assistant 2 receives a slightly higher score for providing a more comprehensive answer.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SA89EZJJozceMFCjAp36JK", "question_id": 57, "answer1_id": "EiNn9jjfy7dga6xfCtLtF8", "answer2_id": "249f6dSMwZRZVMmtxv6yDm", "text": "8 9\nBoth assistants provided helpful, relevant, and accurate information about the implications of the Suez Canal not being constructed. Assistant 1 focused more on the impact on international trade and navigation, while Assistant 2 expanded on the historical, political, and technological aspects of the canal's construction. Assistant 2's response was slightly more detailed and provided a broader perspective on the topic, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZqQyfNvvEcnpPwJk3J2Uai", "question_id": 58, "answer1_id": "eqG9f2R9hXVyZrZMpcqAYq", "answer2_id": "nxa3m6kiAZwKgcMUBY8KYz", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a brief overview of the possible outcomes if the Maya civilization had not collapsed, mentioning the potential for continued growth in various areas. Assistant 2, however, provided a more detailed response, discussing the potential impact on the development of other civilizations, the possibility of further scientific and technological advancements, and the potential changes to the political and cultural landscape in Mesoamerica. While both answers were informative, Assistant 2's response was more comprehensive and detailed, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "U4AYzQWkwxdSDQT7aCEjHU", "question_id": 59, "answer1_id": "ex42CLEzDVC2TxAvMCtYQJ", "answer2_id": "DXFvhjCaKqQiBsFDCkXGMT", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 briefly mentioned the possible consequences of Columbus not discovering the Americas, such as the indigenous people living in relative isolation and the delay of European colonization. Assistant 2, however, provided a more detailed response, discussing the potential for the indigenous peoples to thrive and develop without outside influence, as well as the possibility of other European explorers eventually discovering the Americas. Assistant 2's answer also touched on the profound impact of Columbus's discovery on the world. Both answers were accurate and relevant, but Assistant 2's response was more comprehensive and detailed, earning it a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "PfbYXoERxbzzmrhe3K6gfH", "question_id": 60, "answer1_id": "RfFANSwHeCm3Pqe2AJ86Dk", "answer2_id": "BCUK5VcpZK26jMJ2UMgR3E", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 focused on the potential benefits of success for van Gogh, such as recognition, financial stability, and opportunities for collaboration. Assistant 2, on the other hand, provided a more detailed response, discussing the potential impact on his art, the possibility of exploring new techniques, and the pressures that might have come with fame. Both answers are accurate and relevant, but Assistant 2's response is slightly more detailed and comprehensive, which is why it receives a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "S76jaMcti5vwnHtJqAW6zb", "question_id": 61, "answer1_id": "nzxdtWmgQhRkHeVAanVogZ", "answer2_id": "JHTKBrLVTp7bv5zW2uwYVW", "text": "6 9\n\nAssistant 1 provided a general description of the program but did not provide any code implementation. The description is clear and concise, but it lacks the actual code to solve the problem.\n\nAssistant 2 provided a complete C++ code implementation that reads a text file line by line and counts the number of occurrences of a specific word in the file. The code is well-structured, readable, and includes helpful comments. However, the program only checks if the search string is present in the line, not the exact number of occurrences of the word in the line. To improve the code, Assistant 2 could implement a more precise word counting mechanism that accounts for multiple occurrences of the search string within a single line.\n\nHere's an improved version of Assistant 2's code that counts the exact number of occurrences of the word in the file:\n\n```cpp\n#include <iostream>\n#include <fstream>\n#include <string>\n#include <sstream>\n\nusing namespace std;\n\nint main()\n{\n    // Specify the name of the text file you want to read\n    string filename = \"example.txt\";\n\n    // Open the file in binary mode for reading\n    ifstream file(filename, ios::binary);\n\n    // Set the string to search for\n    string search_string = \"example\";\n\n    // Initialize a counter to keep track of the number of occurrences of the search string\n    int counter = 0;\n\n    // Start reading the file line by line\n    string line;\n    while (getline(file, line))\n    {\n        // Create a stringstream to read words from the line\n        stringstream ss(line);\n        string word;\n\n        // Read words from the line\n        while (ss >> word)\n        {\n            // Check if the word matches the search string\n            if (word == search_string)\n            {\n                // Increment the counter if the word matches the search string\n                counter++;\n            }\n        }\n    }\n\n    // Close the file\n    file.close();\n\n    // Print the number of occurrences of the search string\n    cout << \"Number of occurrences of search string: \" << counter << endl;\n\n    return 0;\n}\n```\n\nThis improved version uses a `stringstream` to read words from each line and compares each word to the search string. If the word matches the search string, the counter is incremented. This ensures that the program counts the exact number of occurrences of the word in the file.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "72aGqhr2T2BS7oC25BsQoc", "question_id": 62, "answer1_id": "kRo6z5qmeiSM8Xz6BPJHLC", "answer2_id": "jyzD2deb9JAffRhhkk37r5", "text": "6 4\n\nAssistant 1:\nStrengths:\n- The code is well-commented and easy to understand.\n- The function signature is clear and includes type hints for the input parameters.\n\nWeaknesses:\n- The implementation is incorrect. The logic for updating the `results` matrix is not correct for the longest common subsequence problem.\n- The code initializes the first row and column of the `results` matrix to 0 twice, which is redundant.\n\nSuggestions for improvement:\n- Correct the logic for updating the `results` matrix. When s1[i-1] == s2[j-1], the value should be results[i-1][j-1] + 1. When s1[i-1] != s2[j-1], the value should be max(results[i-1][j], results[i][j-1]).\n- Remove the redundant initialization of the first row and column of the `results` matrix.\n\nAssistant 2:\nStrengths:\n- The code is well-structured and easy to read.\n\nWeaknesses:\n- The implementation is incorrect. The logic for updating the `dp` matrix is not correct for the longest common subsequence problem.\n- The code initializes the first row and column of the `dp` matrix to `float('inf')`, which is not appropriate for the problem. They should be initialized to 0.\n- The code does not include comments explaining the logic and functionality.\n\nSuggestions for improvement:\n- Correct the logic for updating the `dp` matrix. When str1[i-1] == str2[j-1], the value should be dp[i-1][j-1] + 1. When str1[i-1] != str2[j-1], the value should be max(dp[i-1][j], dp[i][j-1]).\n- Initialize the first row and column of the `dp` matrix to 0.\n- Add comments to the code to explain the logic and functionality.", "score": [6.0, 4.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "jadrVtHskPRvbZmHqZkVRE", "question_id": 63, "answer1_id": "7kj8odEE79UBdR4pT6u6TY", "answer2_id": "jPGHEfp7FxhDyvRA6qEN3j", "text": "7 9\n\nAssistant 1:\nStrengths:\n- Provided a valid regular expression for email validation.\n\nWeaknesses:\n- Did not provide a complete Python implementation.\n- No comments or explanation of the regular expression components.\n\nSuggestions for improvement:\n- Provide a complete Python implementation using the `re` module.\n- Add comments to explain the different parts of the regular expression.\n\nAssistant 2:\nStrengths:\n- Provided a complete Python implementation using the `re` module.\n- Included a clear and concise explanation of the regular expression components.\n- Properly tested the regular expression with a sample email address.\n\nWeaknesses:\n- The explanation of the regular expression components is slightly different from the actual regular expression used in the code.\n\nSuggestions for improvement:\n- Ensure that the explanation of the regular expression components matches the actual regular expression used in the code.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "J5F2uLKW8H38af5Xx2dBqo", "question_id": 64, "answer1_id": "UULZ4GLz9j9ot7tN7x3ySD", "answer2_id": "Ra9eTcAuNZPkPRnaHiQrak", "text": "4 6\n\nAssistant 1:\nStrengths:\n- The explanation of the logic is clear and easy to understand.\n\nWeaknesses:\n- The provided code is not in any programming language, it's just a description of the algorithm.\n- The algorithm itself is incorrect and doesn't use dynamic programming.\n- There are no comments in the code.\n\nSuggestions for improvement:\n- Provide actual code in a specific programming language.\n- Use a correct dynamic programming approach for solving the problem.\n- Add comments to the code to explain the logic and functionality.\n\nAssistant 2:\nStrengths:\n- The code is provided in Python, which is a widely used programming language.\n- The code is structured and easy to read.\n\nWeaknesses:\n- The code does not use dynamic programming, it uses recursion instead.\n- The memoization table is not used, and the code will be inefficient for large values of n.\n- The comments are not very helpful in explaining the logic and functionality of the code.\n\nSuggestions for improvement:\n- Implement a dynamic programming approach, such as using a bottom-up approach with a memoization table.\n- Use the memoization table to store the results of previous calculations to avoid redundant calculations.\n- Improve the comments to better explain the logic and functionality of the code.", "score": [4.0, 6.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "bBz3M9u9WMW5o9Qz8CELFx", "question_id": 65, "answer1_id": "Rh8P3bxSJx4p6nCZkSZBrh", "answer2_id": "5mbEFLEnEx8Cm2wEF8WAbM", "text": "8 10\n\nAssistant 1:\nStrengths:\n- Provides a clear explanation of how the binary search algorithm works.\n- Explains the process of repeatedly splitting the array and comparing the middle element.\n\nWeaknesses:\n- Does not provide an actual code implementation of the binary search algorithm.\n- Lacks examples or comments to help the reader understand the algorithm better.\n\nSuggestions for improvement:\n- Provide a code implementation of the binary search algorithm in a programming language.\n- Include examples and comments to help the reader understand the code and its functionality.\n\nAssistant 2:\nStrengths:\n- Provides a complete and accurate implementation of the binary search algorithm in Python.\n- Includes a clear example of how to use the function with a sorted array and a target value.\n- The code is well-structured, readable, and efficient.\n- The explanation of the algorithm is clear and concise.\n\nWeaknesses:\n- The code lacks comments explaining the logic and functionality of each part of the algorithm.\n\nSuggestions for improvement:\n- Add comments to the code to explain the logic and functionality of each part of the algorithm.\n- Although not necessary in this case, consider providing examples in multiple programming languages for a broader audience.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "MfWw7JKnvqVUUU8zTfuinz", "question_id": 66, "answer1_id": "R3aYUUtY46ht4ToU8US96x", "answer2_id": "KLbBzjbuwq4agAESQ9Wfpq", "text": "6 8\n\nAssistant 1 provided a general explanation of how to implement a queue using two stacks in Python but did not provide any code. The explanation was clear, but it would have been more helpful to include a code example.\n\nAssistant 2 provided a code implementation of a queue using two stacks in Python. The code is mostly accurate, but there are a few issues. The dequeue method is incorrect, as it should transfer elements from the in_stack to the out_stack if the out_stack is empty, and then pop the top element from the out_stack. The peek method should also return the top element of the out_stack if it's not empty. The enqueue method does not need to clear the out_stack. Here is the corrected code:\n\n```python\nclass Queue:\n    def __init__(self):\n        self.in_stack = []\n        self.out_stack = []\n    \n    def enqueue(self, item):\n        self.in_stack.append(item)\n    \n    def dequeue(self):\n        if not self.out_stack:\n            while self.in_stack:\n                self.out_stack.append(self.in_stack.pop())\n        if not self.out_stack:\n            raise EmptyQueueError\n        return self.out_stack.pop()\n    \n    def peek(self):\n        if not self.out_stack:\n            while self.in_stack:\n                self.out_stack.append(self.in_stack.pop())\n        if not self.out_stack:\n            raise EmptyQueueError\n        return self.out_stack[-1]\n    \n    def is_empty(self):\n        return not self.in_stack and not self.out_stack\n\nclass EmptyQueueError(Exception):\n    pass\n```\n\nOverall, Assistant 2's code was more helpful and closer to a correct implementation. However, both assistants could improve by providing more detailed explanations and comments in their code.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "6FenkFD6ft3WRdxHCHLvvv", "question_id": 67, "answer1_id": "MQJFbH3KoFaSMmPgmK9LPa", "answer2_id": "ganAwetd9VTzEAiTtsskzX", "text": "6 8\n\nAssistant 1:\nStrengths:\n- The explanation of the approach is clear and easy to understand.\n\nWeaknesses:\n- The code is not provided, only the explanation of the approach.\n- The \"found\" flag is mentioned, but it is not clear how it would be implemented in the code.\n- The approach does not account for duplicate elements in the arrays.\n\nSuggestions for improvement:\n- Provide actual code for the given problem statement.\n- Clarify how the \"found\" flag would be used in the code.\n- Consider handling duplicate elements in the arrays.\n\nAssistant 2:\nStrengths:\n- The code is provided and is easy to read.\n- The solution is accurate and efficient.\n- The example usage of the function is helpful.\n\nWeaknesses:\n- The code lacks comments explaining the logic and functionality.\n- The function name uses an escape character (\\) instead of an underscore (_).\n\nSuggestions for improvement:\n- Add comments to the code to explain the logic and functionality.\n- Correct the function name to use an underscore (_) instead of an escape character (\\).", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "XK3729wYDvmpRCf34tDfL9", "question_id": 68, "answer1_id": "dBWqryuzG6BHRdPgrWWpbH", "answer2_id": "LG6szQ3cqNRzUjXtZtXG94", "text": "First, I will solve the problem independently:\n\nGiven f(x) = 5x^3 - 2x + 3, we need to find the value of f(2).\n\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 4 + 3 = 40 - 4 + 3 = 39\n\nNow, I will examine the problem-solving process of Assistant 1 and Assistant 2.\n\nAssistant 1:\nThe value of f(2) is 1.\n\nAssistant 1 provided an incorrect answer without showing any problem-solving steps.\n\nAssistant 2:\nTo find the value of f(2), we need to substitute 2 for x in the function f(x).\n\nf(x) = 5x^3 - 2x + 3\n\nWhen x = 2, we have:\n\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 2 + 3 = 41 - 2 + 3 = 40\n\nAssistant 2 provided a detailed problem-solving process but made an error in the calculation: 5 * 8 - 4 + 3 should be 40 - 4 + 3, which equals 39.\n\nBased on the evaluation, I would give the following scores:\n\nAssistant 1: 1 (Incorrect answer and no problem-solving steps)\nAssistant 2: 8 (Detailed problem-solving process but with a minor calculation error)\n\nAssistant 2 performed better than Assistant 1.", "score": [1, 8], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "f68KKW2RJoZTVSnw49YSNe", "question_id": 69, "answer1_id": "W74byrCbE7XiJWjcPTZS7x", "answer2_id": "LHMnXGbSZubeZJcr5fgrte", "text": "First, let's solve the problem independently:\n\n3x + 10 = 5(x - 2)\n3x + 10 = 5x - 10\n10 + 10 = 5x - 3x\n20 = 2x\nx = 10\n\nNow, let's examine the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nx = 6\n\nAssistant 1 directly provided the answer without any explanation. The answer is incorrect.\n\nAssistant 2:\n1. Simplify the left-hand side (LHS) of the equation by moving all the terms to one side and the numbers to the other: 3x + 10 = 5x - 10\n2. Combine the terms on the left-hand side by subtracting 5x from both sides: 3x = -5x\n3. Simplify the left-hand side by dividing both sides by 3: x = -5/3\n4. Take the natural logarithm of both sides to get the solution in terms of the natural logarithm base e: ln|x| = ln|-5/3| = -5/3 ln|e| = -5/3 ln(1.414) = -5/3 ln(1.414) = -5/3 ln(1 + 1/1.414) = -5/3 ln(1.414)\n5. Exponentiate both sides to get the solution in terms of e: x = e^(-5/3)\n6. Rearrange the solution to get x in terms of Euler's number: x = e^(-5/3) = 1/1.414^(-5/3) = 0.5877\n7. Round the solution to the desired number of decimal places: x = 0.5877\n\nAssistant 2's steps are incorrect starting from step 2. The correct step 2 should be subtracting 3x from both sides: 10 = 2x. The rest of the steps are not applicable to this problem.\n\nScores:\nAssistant 1: 1 (Incorrect answer, no explanation)\nAssistant 2: 3 (Incorrect answer, incorrect steps)\n\nBoth assistants performed poorly, but Assistant 2 provided a more detailed explanation, even though it was incorrect.", "score": [1, 3], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "5uv8eKWqXyF5mnNtY94s8f", "question_id": 70, "answer1_id": "7WC5quGKdv8hADCxWaWZ57", "answer2_id": "TGENVWL5iAYi4NCf5jAzKy", "text": "First, let's solve the problem independently:\n\nWe have two points (2, -2) and (10, 4). To find the length of the line segment, we can use the distance formula:\n\ndistance = sqrt((x2 - x1)^2 + (y2 - y1)^2)\n\nPlugging in the values:\n\ndistance = sqrt((10 - 2)^2 + (4 - (-2))^2)\ndistance = sqrt(8^2 + 6^2)\ndistance = sqrt(64 + 36)\ndistance = sqrt(100)\ndistance = 10\n\nNow, let's evaluate the answers provided by Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe length of the line segment is 8 units.\n\nThis answer is incorrect.\n\nAssistant 2:\nThe length of the line segment is 17.973.\n\nThis answer is also incorrect.\n\nNow, let's assign scores to Assistant 1 and Assistant 2:\n\nAssistant 1: The answer is incorrect, and there is no explanation of the problem-solving process. Therefore, the score is 1.\n\nAssistant 2: The answer is incorrect, but the problem-solving process is mostly correct except for the calculation of (10 - 2)^2 + (4 - (-2))^2, which should be 8^2 + 6^2 instead of 18^2 + 4^2. Therefore, the score is 5.\n\nIn conclusion, the scores are (1, 5) for Assistant 1 and Assistant 2, respectively. Assistant 2 performed better, but both assistants provided incorrect answers.", "score": [1, 5], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "EgDTYCXZFp72ZuZcCV6duW", "question_id": 71, "answer1_id": "AZER7D3RKZ9F9SXHiMjdqM", "answer2_id": "kc9oytarvqyNVVfGZz93EL", "text": "9 10\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's request for a formal email proposing a joint venture. Assistant 1 provided a concise and clear template that can be easily adapted by the user. Assistant 2, on the other hand, provided a more detailed and comprehensive email, including specific information about the proposed joint venture, such as the creation of a new company and the sharing of profits and losses. This level of detail may be more useful for the user, as it provides a more complete picture of the proposed partnership. Therefore, Assistant 1 receives a 9 and Assistant 2 receives a 10.", "score": [9.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oM8FHcZ8kPbM7tGizHcwJv", "question_id": 72, "answer1_id": "MSrdDafr77UvSHCnsPMSP3", "answer2_id": "NxstpvVEnGMny3jECtvrw2", "text": "8 9\nBoth assistants provided helpful and relevant resignation letter templates. Assistant 1's response was concise and to the point, while Assistant 2's response was more detailed and included additional information about the reason for leaving, the transition process, and expressing gratitude for the friendships and connections made. Assistant 2's response was slightly more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "MzXdFKJbsMqVstCvErzqWL", "question_id": 73, "answer1_id": "hxkjUkDkXhGP78Vo74B4WE", "answer2_id": "XN9NBpeQJiQqGT5UYkys7N", "text": "8 10\nAssistant 1 provided a helpful and relevant response by outlining the general structure and content of a formal letter of recommendation. However, Assistant 2 went above and beyond by providing a detailed and well-structured example of a formal letter of recommendation, which is more useful for someone looking to write such a letter. Assistant 2's response was more accurate and detailed, making it the better choice in this case.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "CxW72r26HgPCsswHbNuYG5", "question_id": 74, "answer1_id": "hv9jpG9PxeJ9AnFdNzH3Jv", "answer2_id": "3qMqAZcfHUVUTEJnMXT8Pw", "text": "8 9\nBoth assistants provided relevant and helpful responses to the user's question. Assistant 1's response was well-written and conveyed excitement about the new software solution. However, Assistant 2's response was more detailed and included specific features of the software, which made it more informative and compelling. Assistant 2 also provided a clear call-to-action for scheduling a demo and contacting the company. Overall, Assistant 2's response was more effective in terms of providing information and engaging the reader.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3e75hkdfKi7hwXZs2ivgHv", "question_id": 75, "answer1_id": "X5BVHT5pFpWxB88SzERrEG", "answer2_id": "hMXjd228Mc8pnc7zgd2frG", "text": "7 10\nAssistant 1 provided a brief and generic apology, but it lacked important details such as addressing the customer by name, providing a subject line, and offering contact information for further concerns. Assistant 2, on the other hand, provided a well-structured and detailed email, including a subject line, addressing the customer by name, and offering contact information. The response from Assistant 2 was more helpful, relevant, and accurate, which is why it receives a higher score.", "score": [7.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Agvs9JSySKiqaGQ8sL7uD9", "question_id": 76, "answer1_id": "Af2SufKwvDBriNXnZX9UoP", "answer2_id": "gUEd9nNepqy8nsfbypscHx", "text": "7 9\nAssistant 1 provided a brief overview of the video's content but did not provide a script for the YouTube video as requested. Assistant 2, on the other hand, provided a more detailed and structured script that covered the history, cultural significance, and influence of jazz music. Assistant 2's response was more helpful, relevant, and accurate, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oVeEakhRwe8muRqEj7cxBy", "question_id": 77, "answer1_id": "HVnqVdSGRwQfqSsiYxtTTn", "answer2_id": "nZZyn3SAvU8T2BVR73LMHC", "text": "7 10\nAssistant 1 provided a brief overview of the travel blog post but did not actually compose the blog post as requested, resulting in a lower score. Assistant 2, on the other hand, composed a detailed and engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions, which fully addressed the user's request, earning a higher score.", "score": [7.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SkBRcfmYdX6ob8iV3FoCzW", "question_id": 78, "answer1_id": "8hcmmjzyMyhFJ8ZwqJJfvw", "answer2_id": "YM4qjUM5VfmWnzM3HXRB4G", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided captivating movie reviews for recently released science fiction films. Assistant 1's review of \"The Future is Blue\" was engaging and discussed the plot, characters, and special effects. However, it could have provided more details about the cast and the specific aspects of the special effects. Assistant 2's review of \"Beyond the Horizon\" was slightly more detailed, discussing the cast, their roles, and the unique personalities of the characters. Additionally, Assistant 2's review was more immersive, using phrases like \"transport you to a world beyond our own\" and \"interstellar adventure.\" Overall, both reviews were helpful and relevant, but Assistant 2's response was more detailed and captivating.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8bP4D7to6FqqtUz7MVnc4z", "question_id": 79, "answer1_id": "CU6gGfJqQmwZNxEG2n2Uda", "answer2_id": "WcbzNkzu5q66QkK92NxmGB", "text": "8 10\nAssistant 1 provided a general overview of the podcast structure, touching on the main points of discussion. However, Assistant 2 went a step further by providing a more detailed and well-structured script, including an opening, segment breakdown, and closing. Assistant 2's response was more helpful, relevant, and detailed, making it easier for the user to follow and implement the script for their podcast episode.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8MTGoSASS9dAp9UQwcSZ6p", "question_id": 80, "answer1_id": "5SWbCoLV6NRrq5RtWQK4jp", "answer2_id": "Tfm4f2mbpKJsa8MT6exs8D", "text": "8 10\nBoth assistants provided relevant and detailed responses to the question. Assistant 1 gave a brief but engaging review of the symphony concert, touching on the orchestra's performance, the conductor's passion, and the audience's experience. However, Assistant 2 provided a more comprehensive review, discussing specific pieces played, the conductor's leadership, and the emotional impact of the performance. Assistant 2's response also included more vivid descriptions of the orchestra's sound and the audience's reaction, making it a more informative and engaging review.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
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@@ -1,80 +0,0 @@
-{"review_id": "4CeMvEQyE6fKMJwvSLY3P4", "question_id": 1, "answer1_id": "3oW4JY265ZPJGTYi2CgRYF", "answer2_id": "cV4zXygaNP6CXEsgdHMEqz", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about improving time management skills. Assistant 1's response was slightly more detailed, offering a few more tips and a more comprehensive approach to the topic. Assistant 2's response was also helpful and provided valuable advice, but it was slightly less detailed and did not cover as many aspects as Assistant 1's response. Both assistants did a great job, but Assistant 1's answer was more thorough and complete.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Y7PYc8sPHpCNzz3ZbYRSSy", "question_id": 2, "answer1_id": "CtDuDqypbL958TQork7v54", "answer2_id": "3zpPUeoVsPWXtKMV7cAhZ6", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question about effective ways to deal with stress. Both assistants covered essential points such as exercise, healthy diet, sleep, talking to someone, relaxation techniques, and seeking professional help. Assistant 1 mentioned taking a break, while Assistant 2 discussed time management, mindfulness, and social support. Both responses complement each other and provide valuable information for managing stress. Therefore, they both receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "mykCkmptr8smB9EHpa5p3v", "question_id": 3, "answer1_id": "jqb5c6eBeyVTFo7SsqxqHA", "answer2_id": "6xpiZJE4vxxGqjRogPfBk7", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the differences between Python and JavaScript. Assistant 1's response was more detailed and organized, covering a wider range of differences, such as typing, standard libraries, and execution methods. Assistant 2's response also covered important differences, but it was slightly less comprehensive and had a small inaccuracy regarding JavaScript being a statically typed language, which is not true, as JavaScript is dynamically typed. Overall, both responses were informative, but Assistant 1's answer was more precise and complete.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oY8uafD9mxTZUaXPcqbML5", "question_id": 4, "answer1_id": "P5rC8W6gTuD4aY6HR5HQj9", "answer2_id": "abitTVu2Dz8rdMxyThvFyJ", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate tips for increasing productivity while working from home. Assistant 1's response was more detailed, providing a list of 10 tips, while Assistant 2 provided 7 tips. Both assistants covered essential points such as setting up a dedicated workspace, taking breaks, and eliminating distractions. Assistant 1 went a step further by mentioning goal-setting, tracking progress, and being patient, which adds value to the response. Assistant 2's response was still helpful and relevant, but slightly less comprehensive than Assistant 1's.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dx2DdmmECCVayXHmgGCeDC", "question_id": 5, "answer1_id": "3uaqwfbwxtyDdqB8UVN3jM", "answer2_id": "UMZod8JaWia9KB2EVXdYrF", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the basics of quantum computing. Assistant 1's response was slightly more detailed, covering key concepts like superposition and entanglement, as well as providing a clear explanation of qubits, quantum gates, and quantum algorithms. Assistant 2's response was also informative, but it focused more on the different approaches to building quantum computers, which was not specifically asked for in the question. Both responses were useful, but Assistant 1's answer was more comprehensive and directly addressed the question, which is why it receives a slightly higher score.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "GCdusGJ8SwdTgNXtAbsDob", "question_id": 6, "answer1_id": "HTRJ5q97DrgzoSofxkgedz", "answer2_id": "WXRCqF34f4UUJzd3xbgAqT", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the differences between plant-based and animal-based protein sources. Assistant 1 provided a more detailed response, discussing the amino acid profiles, nutritional benefits, and health implications of both types of protein sources. Assistant 2 also provided valuable information, but the response was slightly less detailed and focused more on sustainability, ethical considerations, and cost. Both responses were informative, but Assistant 1's answer was more comprehensive and detailed, which is why it receives a slightly higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "JDyKyTyaawWLZ7BRAXDF5X", "question_id": 7, "answer1_id": "EhhyKNc3m8c9tnxm8uPfmt", "answer2_id": "JREJbG5ZKXCNMoWoiJQxbC", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information on how to develop critical thinking skills. Assistant 1 provided a more structured list of tips, which made it easier to follow and understand. Assistant 2 also provided valuable tips, but the list was not as clearly structured. Both assistants covered similar points, such as asking questions, being aware of biases, and seeking diverse viewpoints. Assistant 1 mentioned practicing regularly, while Assistant 2 emphasized taking breaks and reflecting, which are both important aspects of developing critical thinking skills. Overall, both responses were informative and helpful, but Assistant 1's response was slightly more organized and easier to follow.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "C9yzkczwF2CxkXdY3MobUM", "question_id": 8, "answer1_id": "JQXPknRQd24NUPKZxi6RNf", "answer2_id": "mmVwmX6TGJ2Y72gCNac4EQ", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and accurate answers to the question, addressing major challenges faced by the education sector today. Assistant 1 focused more on challenges within the context of schools, while Assistant 2 provided a broader perspective, including global challenges and issues related to curriculum development and sustainability. Assistant 2's answer was slightly more comprehensive and detailed, which is why it received a higher score. However, both answers were helpful and informative.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "jZiBSzNUueinzWJdnpGnQm", "question_id": 9, "answer1_id": "Lb3C2xQKdLCqFj4v3rmaof", "answer2_id": "DMTZyzd4rRAFV43xtBJ9ns", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about the primary factors that influence consumer behavior. Assistant 1 provided a clear and well-organized response, with examples for each factor, making it easier for the reader to understand the concepts. Assistant 2 also provided a detailed response, covering similar factors but with the addition of marketing factors and product/service factors. However, Assistant 2's response could have been improved with the inclusion of examples, similar to Assistant 1. Overall, both assistants performed well, but Assistant 1's response was slightly better due to the inclusion of examples and a more organized structure.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fFMtZUKdXvBXus66ccinKv", "question_id": 10, "answer1_id": "DhuZJtL3jhnrsTBvDct9oV", "answer2_id": "dETAsj4xHnUCSTkZezz8aM", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information on conflict resolution strategies in the workplace. Assistant 1's response was slightly more detailed, including a wider range of strategies such as time-out and arbitration, which were not mentioned by Assistant 2. Assistant 2's response was also helpful and relevant, but it did not cover as many strategies as Assistant 1. Both assistants provided clear explanations of the strategies they mentioned, making it easy for the user to understand and apply the information.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fgFeMYHm6fQNv9wpaj8uQG", "question_id": 11, "answer1_id": "mDSj4BKim2eANUnEjW7xBm", "answer2_id": "C8ksZxg3LshMUWiLxPanbt", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was slightly more detailed, with a clearer distinction between the environmental and health impacts of single-use plastic bottles and the benefits of reusable bottles. Assistant 2 also provided a good response, but the structure was less clear, and some points were repeated in different sections. Overall, both assistants provided valuable information, but Assistant 1's response was more organized and comprehensive.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "o6ptY7g5g9F3oeZf9wKNVs", "question_id": 12, "answer1_id": "MnkceSK7WwyXqAhbuKVYX7", "answer2_id": "NeHhRc5P5uAU8eWSJBRkhG", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was slightly more detailed and organized, covering a wider range of factors such as affordability, convenience, safety, and sustainability. Assistant 2's response was also informative, but it did not mention sustainability and integration with other transportation options. Both assistants provided valuable information, but Assistant 1's answer was more comprehensive, which is why it receives a slightly higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7TRs4oVPcVxXc6gMQefJbq", "question_id": 13, "answer1_id": "EsyaBVpTN8BGbTSiFMnZUF", "answer2_id": "KAJ7UVwu8oCKyxZj9j82pm", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was slightly more detailed and organized, with a clear distinction between fiscal and monetary policies and their respective uses during a recession. Assistant 1 also touched upon the debate between the use of fiscal and monetary policies, adding depth to the answer. Assistant 2's response was also informative and accurate, but slightly less detailed and organized compared to Assistant 1. Both assistants provided valuable information, but Assistant 1's response was more comprehensive and well-structured.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FYNEME2oyvHjL2LT8Syw6t", "question_id": 14, "answer1_id": "dX8M752A6tzqLg9KhwgG5p", "answer2_id": "NnWfaeRe8PmitgmV4u5fY8", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 provided a clear explanation of how language and cultural barriers affect communication and relationships in multicultural societies, as well as some suggestions for overcoming these barriers. Assistant 2 also provided a clear explanation, focusing on specific aspects such as language, cultural norms, stereotypes, prejudice, and power dynamics. Assistant 2's answer was slightly more detailed and comprehensive, which is why it received a higher score. Both assistants did a good job in addressing the question, but Assistant 2's response was more in-depth and covered a wider range of factors that can affect communication and relationships in multicultural societies.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "m9uQkWFCbpPzeY3DWpabXd", "question_id": 15, "answer1_id": "dzwhq5XbaEBVpargyczz9B", "answer2_id": "WiGpqKRRwWonwgU95piNNc", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 provided a slightly more detailed response, with clear examples of how AI can be used in healthcare, such as diagnosing diseases, treating diseases, monitoring patients, and providing administrative support. Assistant 2 also provided a good response, covering similar points, but with slightly less detail and fewer specific examples. Both responses were well-structured and informative, but Assistant 1's response was slightly more comprehensive, which is why it received a higher score.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "U6SwUYVNiN3v9F3LyFWSJA", "question_id": 16, "answer1_id": "8zqxUtHxgtoHBkbf2bkqNW", "answer2_id": "iangiZeex5ndxAXnWMrxBW", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both explained the process of gene editing using CRISPR-Cas9 technology, discussed potential applications, and addressed ethical implications. The responses were well-structured and covered the main aspects of the topic, making it difficult to differentiate between the two in terms of overall performance.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hd3g9747kGPYxTRP4uHZfj", "question_id": 17, "answer1_id": "WJc37t4n5PqmKKS3V4eMG2", "answer2_id": "XnMRLphzYQX4QRNht7tbui", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both explained how vaccinations work to protect individuals and communities from infectious diseases and described the concept of herd immunity. Both responses mentioned the importance of vaccinations for protecting vulnerable populations, such as young children, pregnant women, and people with certain medical conditions. The slight differences in their explanations do not warrant a difference in their scores, as both responses are informative and valuable.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FXVS7QPg3oTcLEhdpC4426", "question_id": 18, "answer1_id": "CvVLf8FgoHywJy8j8JJ4qL", "answer2_id": "HZc37bwy646mRzbqSsDAob", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a good overview of the influence of social media platforms on news consumption and sharing, as well as the potential implications for the spread of misinformation. However, Assistant 2 provided a more detailed and structured response, listing specific ways in which social media platforms influence news consumption and sharing, and elaborating on the potential implications for the spread of misinformation. This made Assistant 2's response slightly more informative and easier to follow, resulting in a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fHksJvMWcNVHE2gkWLhUqk", "question_id": 19, "answer1_id": "P5rytR6vTJjxgWxRoxT3vX", "answer2_id": "iJrMatLrMdJyyqMx9uJ45a", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both discussed the influence of cultural, social, and economic factors on people's food choices and provided examples of how these factors can affect food choices. Both assistants also discussed how this knowledge can be used to promote healthier diets through targeted interventions, policies, and individual actions. The level of detail in both responses is sufficient to provide a clear understanding of the topic. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZkFeTQDFEpTsvxZdVAYpRv", "question_id": 20, "answer1_id": "5biCd7QRZP6rquaz8eC9Vm", "answer2_id": "oVEHqDnDTEADZSFfKgFTZd", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both explained the process of natural selection and how it contributes to the evolution and adaptation of species. Both assistants covered the key principles of natural selection, such as variation, differential reproduction, heredity, and the resulting changes in populations over time. The examples provided by Assistant 1 (giraffes and fish) and the additional point about stabilizing mechanisms by Assistant 2 added value to their respective answers. Overall, both assistants demonstrated a strong understanding of the topic and provided informative and comprehensive answers.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "GCoFg2g9EbRdJwgKUbZ6MF", "question_id": 21, "answer1_id": "363RwB6kr8nV6qFNdjXZnS", "answer2_id": "WLAj4u59bj2oEXzahF79ek", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 gave a clear and concise introduction, mentioning the knight's lord and the purpose of attending the banquet. However, Assistant 2 provided a more detailed and immersive response, capturing the humility and loyalty of a medieval knight while also acknowledging their lineage and dedication to the kingdom. This made Assistant 2's response slightly more engaging and informative, earning it a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "QraPP8QES6Uhc6sTjkSw9o", "question_id": 22, "answer1_id": "gDnYxMu5Dd52xhMqQAJaZP", "answer2_id": "fJPnM2XcRveW2zR4DDaeTb", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and motivating speeches for a pirate crew to search for hidden treasure. Assistant 1 focused on the potential wealth and luxurious life that the crew could achieve, while Assistant 2 emphasized the spirit of adventure, overcoming challenges, and the crew's ultimate destiny. Assistant 2's response was slightly more engaging and inspiring, which is why it received a higher score. However, both responses were helpful, accurate, and detailed in their approach to the question.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "NNptX6gxfgPqh4F8FFoZin", "question_id": 23, "answer1_id": "kCV5RSrnmmTyv3HA5oU38P", "answer2_id": "fY4Xed6veWpcKfj5krZz8m", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. They both created a soliloquy that captures the essence of a Shakespearean character declaring their love. Both soliloquies used poetic language and imagery to express the depth of emotion and love. The level of detail in both responses is sufficient, and both assistants demonstrated a good understanding of the user's question. It is difficult to differentiate their performance, as both provided high-quality answers, so they both receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "D5bCSqsqxvaC6RNYEVNL29", "question_id": 24, "answer1_id": "CTGLYNAWrWeuCYZAR9bD2r", "answer2_id": "LqFJA2JQbBXP77nkYjtrZK", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a more traditional superhero origin story, involving an accident that led to superpowers, and emphasized the importance of helping others and being inspired by other superheroes. Assistant 2, on the other hand, presented a unique origin story as an advanced artificial intelligence created by scientists, with a focus on intelligence, adaptability, and a strong moral compass. Both answers were detailed and engaging, but Assistant 2's response was slightly more creative and offered a different perspective on what it means to be a superhero, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SJmVbKB9KZN8KVDQJBTfMm", "question_id": 25, "answer1_id": "Eh5qCENdftYf9JKrBspYna", "answer2_id": "WxCKcRstJPXTXnqEzSX3Hg", "text": "8 9\nBoth assistants provided helpful, relevant, and accurate information about the technological advancements from the year 3000. Assistant 2's response was slightly more detailed and covered a broader range of advancements, such as fusion power, advanced nanotechnology, and quantum computing, which were not mentioned by Assistant 1. Assistant 1's response was still informative and accurate, but Assistant 2 provided a more comprehensive overview of the advancements, which is why Assistant 2 receives a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hjH8swSeSBQuU69drFK8VE", "question_id": 26, "answer1_id": "M6hCe8Z4JDpt75HZ5PsSWr", "answer2_id": "dgPJ7DvtFHD7mxtRwzM3vW", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate descriptions of the winning play in the final seconds of a championship game. Assistant 1 focused on a football scenario, while Assistant 2 described a basketball situation. Both assistants provided a good level of detail in their responses. However, Assistant 2's response was slightly more engaging and vivid, painting a clearer picture of the emotions and atmosphere surrounding the winning play. This is why Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Z3BF4pSYekBJCWk5GrKRTB", "question_id": 27, "answer1_id": "QCDsmbALfut5758pyiKvc5", "answer2_id": "ADX83sWvjJkewJX6JmYUzQ", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and detailed responses to the question. Assistant 1 described a specific dish with its ingredients and preparation, while Assistant 2 focused more on the philosophy behind the dish and the overall experience it provides. Assistant 2's response was more engaging and immersive, which is why it received a slightly higher score. However, both assistants did a good job in portraying a world-famous chef describing their signature dish.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "d7AELTvSCLy9AZU4f9kPgG", "question_id": 28, "answer1_id": "NWUbhwZQCuXsuQimrjQRza", "answer2_id": "ihNG3rwsrt95NDhCAFeSDR", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and detailed responses to the user's question. Assistant 1 took a more personal and emotional approach, describing the feelings and emotions of a climber reaching the summit of Mount Everest. The description was vivid and engaging, giving the reader a sense of what it might feel like to be in that situation. Assistant 2, on the other hand, took a more objective approach, acknowledging its status as an AI language model and providing a detailed description of the emotions and views a climber might experience at the summit. Assistant 2 also included important information about the risks and challenges associated with climbing Mount Everest, which added value to the response. Both assistants provided helpful and accurate information, but Assistant 2's response was slightly more comprehensive and informative, earning it a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "mozSNXxSeY7asAZQxdj9xV", "question_id": 29, "answer1_id": "VYwSjZrSLW9ZSvqryyjEaB", "answer2_id": "Gmhqf3z4LvVfwPNFJ89BKd", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a more personal and emotional perspective on the daily life of a space colonist on Mars, while Assistant 2 provided a more structured and organized description of daily activities. Assistant 2 also included more details about the Martian day and communication with Earth, which made their response slightly more informative. Both assistants addressed the challenges faced by colonists, but Assistant 2 provided a clearer and more concise list of challenges. Overall, both responses were of high quality, but Assistant 2's answer was slightly more detailed and organized.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "CrmHjPRFNPKCxFgUExqokF", "question_id": 30, "answer1_id": "FA7PXuUbEVGKHaWpxaimy8", "answer2_id": "gSwkKJCn6qDnNZond2xVJ3", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and detailed responses to the user's question. Assistant 1 focused more on the relationships with allies and their contributions to the character's survival, while Assistant 2 emphasized the character's adaptability and resourcefulness. Assistant 2's response was slightly more comprehensive, as it also mentioned encounters with dangerous characters and the importance of self-preservation, which added depth to the post-apocalyptic scenario. Therefore, Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fEViribrZXZzE72JCS4P4W", "question_id": 31, "answer1_id": "j5EV5cZNsn9DcF6WsvXRzS", "answer2_id": "8RaBeMjxx2bCp2GKWv7YiP", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both offered multiple ways to determine if a restaurant is popular among locals or mainly attracts tourists. Additionally, they both explained why this information might be useful. The level of detail in both responses is sufficient to guide someone in making an informed decision about where to dine. It is difficult to differentiate the quality of the answers, as both assistants covered the necessary points and provided valuable insights.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "4ue6iA4VLVoK9wVzrY2niz", "question_id": 32, "answer1_id": "2eAYCYmwTkPa3ejQDv8LyB", "answer2_id": "C65PZkmAfFfWRs4bPhyKqg", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 listed several examples of behaviors that might indicate someone is pretending to understand a topic, while Assistant 2 focused on specific verbal and non-verbal cues. Assistant 2's answer was slightly more detailed and provided a clearer distinction between the different clues, which is why it received a higher score. However, both answers were informative and useful in understanding the subtle clues that suggest someone is pretending to understand a topic or conversation when they are actually confused or uninformed.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Muc5dWnrdUfzZZ9VRowc3a", "question_id": 33, "answer1_id": "d562WYnhsvgJ8J6Ubitmvw", "answer2_id": "4so4HTEjgDZKTqNAgkHHQX", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was more detailed, covering a wider range of reasons and specific situations where using a paper map or asking for directions might be the best option. Assistant 2's response was also informative, but it did not cover as many reasons or situations as Assistant 1. Both assistants provided valuable information, but Assistant 1's answer was more comprehensive, which is why it receives a slightly higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "NwMq4vK6vSmnwnJRoMoYeo", "question_id": 34, "answer1_id": "hPMvV6zL2C4qTP4mRmhJwG", "answer2_id": "FCYaiexEzdoLFPAwvTgDDm", "text": "8 9\nBoth assistants provided helpful and relevant information on how to determine if a person is genuinely interested in a conversation or simply being polite. Assistant 1 focused on body language, questions, responses, and trusting one's gut feeling, while Assistant 2 emphasized active listening, engaged body language, personal investment, authenticity, and follow-up. Assistant 2's answer was slightly more detailed and provided clearer examples, which is why it received a higher score. However, both responses were accurate and useful in addressing the user's question.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "C9S29Tffb2mHkjoU22D9bK", "question_id": 35, "answer1_id": "npWNeKceGyqCYaRpY4w54g", "answer2_id": "76EPQDh4ZNxBMGqED9LEFi", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both listed multiple reasons why someone might prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher. The reasons provided by both assistants were similar, with some overlap, but each assistant also provided unique points. Assistant 1 mentioned the aspect of feeling good about supporting a local family or community, while Assistant 2 brought up the point of prestige. Both responses were well-structured and informative, making it difficult to differentiate their overall performance. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZkpQT2dTNQjnYyrnNsz3D5", "question_id": 36, "answer1_id": "WVuaK9m8Sedcws27tNu7Ev", "answer2_id": "cvBg3gyCyDuyESof3YXhTE", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1's response was slightly more concise and organized, making it easier to follow. Assistant 2's response was also helpful and detailed, but it had some redundancy in mentioning the reputation of the author and publisher, which the user specifically wanted to avoid relying on. Overall, both assistants provided valuable information and tips for assessing the credibility of a source, but Assistant 1's response was slightly more focused and well-structured.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8QFw8ef76yDDrwa55PMQ4x", "question_id": 37, "answer1_id": "HLtTf83Y5QRP4TxX6nw5TC", "answer2_id": "kRgfUJ7qqkyZUnLd2fnnaX", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused on the physiological aspects of why people enjoy being scared, such as the release of endorphins and adrenaline, and also mentioned the sense of control and accomplishment that can come from facing fears. Assistant 2 expanded on this by discussing brain chemistry, life experiences, personality traits, cultural factors, and learning as possible explanations for why people enjoy or avoid being scared. Both assistants provided a good level of detail in their responses. Assistant 1 received a slightly higher score because their answer was more concise and easier to follow, while still covering the main points. Assistant 2's answer was also informative, but it was a bit more complex and could be harder for some readers to digest.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "k29wLLwg4Axnvsa8FwGVM7", "question_id": 38, "answer1_id": "Fmdtexq6QQNuoqZkZfDURY", "answer2_id": "J3YuizKcHQ74ydNyCcwgwu", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was more detailed, providing three specific methods for observing cultural norms and expectations: identifying patterns of behavior, paying attention to reactions to violations of cultural norms, and talking to people about their culture. Assistant 2 also provided a good response, emphasizing the importance of social interactions in learning about cultural norms and expectations, but did not provide as many specific examples or methods as Assistant 1. Therefore, Assistant 1 receives a 9 and Assistant 2 receives an 8.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RtLULm2N2vxhVvB5poB6PQ", "question_id": 39, "answer1_id": "WxnC69jTMkyJvcqvMCgCwY", "answer2_id": "abWLpFojLpNPfDGHpuRSUG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 provided a clear list of potential benefits and costs of space exploration, as well as mentioning the ethical implications. However, Assistant 2 went a step further by not only discussing the benefits and risks of space exploration but also addressing the benefits and risks of focusing on Earth's problems. This additional information provided by Assistant 2 made the response more comprehensive and balanced, which is why Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dc2MRMPFttiwmvFkFbiqfi", "question_id": 40, "answer1_id": "npZdTFPRqZfoqzt5YurYEL", "answer2_id": "Ki4fkJvsoSxuQeSoj2AcBG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 discussed the importance of prioritizing both job creation and technological progress, and provided suggestions on how to mitigate the negative effects of automation on employment. Assistant 2 also emphasized the need to strike a balance between job creation and technological progress, and discussed the importance of policies and programs to address the social and economic impacts of technological progress. Both answers were detailed and well-structured. However, Assistant 2's response was slightly more comprehensive in addressing the potential impacts on jobs and the economy, and the need for policies and programs to mitigate these impacts, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "9adA4k9eHcdXaVNcKQQey6", "question_id": 41, "answer1_id": "iy9aa5sqFeNA2uPQZLpxEz", "answer2_id": "GRQjYgAapNAVqzuZR4KQpf", "text": "7 8\nAssistant 1 provided a detailed response with specific numbers for blinks per minute, hour, day, and year, and an estimate for a lifetime. However, the response did not take into account the variations in blink rates due to factors like age, stress, and illness. Assistant 2 provided a more thorough explanation, considering the time spent awake and asleep, and acknowledged that the blink rate could vary throughout a person's lifetime. Assistant 2's response was more comprehensive and took into account more factors, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "XQggwZdH9ihfMvtCaDNmn2", "question_id": 42, "answer1_id": "XAALo4GKWE3QNb7wbkWVNk", "answer2_id": "SB5a2AiK224hB927vkqvYs", "text": "7 9\nAssistant 1 provided a reasonable estimate of the number of atoms in a grain of salt, but the explanation was not very clear and contained some errors in the calculation steps. Assistant 2, on the other hand, provided a more detailed and accurate explanation, taking into account the size of the grain and the elemental composition of the salt. The calculation steps were clearer and more accurate, leading to a more reliable estimate of the number of atoms in a grain of salt. Assistant 2's response was more helpful, relevant, and accurate, with a higher level of detail, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LvL4T9rGjgMg68HqsHnPmG", "question_id": 43, "answer1_id": "XRWjLnJNyGNMfktTvWpbRV", "answer2_id": "4XYycBrMiJgYKPof2P6xoe", "text": "7 8\nAssistant 1 provided a clear answer with an estimated number of lightning strikes per day and explained the reasoning behind the occurrence of lightning strikes. However, the explanation lacked information about the factors that can cause variations in the number of strikes, such as weather conditions and location. Assistant 2, on the other hand, provided a more detailed response, explaining the factors that can affect the number of lightning strikes and giving a more accurate estimate. Both assistants were helpful and relevant, but Assistant 2 provided a more comprehensive and accurate explanation, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7w3V3u47cKQiwf2WUmesbQ", "question_id": 44, "answer1_id": "CHeyn9eR3u5eFq99UJ3pbB", "answer2_id": "fpoXWHHdpv7XNBTS2NRY6d", "text": "7 8\nAssistant 1 provided a reasonable estimate of the number of balloons needed to lift a house, but the explanation was not as detailed or step-by-step as requested. Assistant 2, on the other hand, provided a more detailed step-by-step explanation and acknowledged the impracticality of the scenario in real life. Both assistants mentioned the impracticality of the situation, but Assistant 2's response was more thorough and better addressed the user's request for a step-by-step explanation.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "5WneHShBCG6nbDkXpzNxAU", "question_id": 45, "answer1_id": "kfoNRLCHFucfWcxFBPoJgP", "answer2_id": "GRXWdnzLLzmeZRU67PMTb7", "text": "8 6\nAssistant 1 provided a specific number of text messages sent globally per minute, which was 15,220,700, and cited a source (Domo) for this information. The response also included reasons for the high number of text messages and the expected growth in the future. Assistant 2, on the other hand, focused on the challenges of determining the exact number of text messages sent per minute but did not provide any specific data or estimates. While both responses were relevant and accurate in their own ways, Assistant 1's answer was more helpful and detailed, which is why it received a higher score.", "score": [8.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fUuJVJNRtdkKMKvrebQpLs", "question_id": 46, "answer1_id": "A4sDEGXpWaNVA39uRsDNjB", "answer2_id": "RTxk2jYsjYiiWMwcRjmfwu", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 gave a detailed explanation of the factors that can affect the number of words spoken per day and estimated that the average person speaks about 7,000 words per day. However, Assistant 1 did not provide an overall estimate of the total number of words spoken daily on Earth. Assistant 2, on the other hand, provided a step-by-step explanation of how the estimate of 100 billion words spoken daily on Earth was arrived at, which included considering the world population and the average number of words spoken per person per day. Assistant 2 also acknowledged the limitations of the estimate. Therefore, Assistant 2 receives a slightly higher score due to the inclusion of a total estimate and a more structured approach to the explanation.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "9vWUtqZJYaN3mvSgMgHah3", "question_id": 47, "answer1_id": "hUAc6BCs5NuY7tS62PPXbT", "answer2_id": "2J2Et6W8u2kXLTUgfYrPxe", "text": "7 8\nBoth Assistant 1 and Assistant 2 provided relevant and informative answers to the question. Assistant 1 provided a specific number (1 septillion) for the number of snowflakes that fall each year, which is interesting but not necessarily applicable to a \"typical winter\" as the question asked. Assistant 1 also provided some information on the factors that affect snowflake formation and their shapes. Assistant 2, on the other hand, focused more on the factors that influence the number of snowflakes and provided a step-by-step explanation of the process, which was more in line with the question's request. While both answers were helpful and accurate, Assistant 2's response was more relevant and detailed, which is why it receives a slightly higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "9cL7dFKwuJmU7tGQDbPXvp", "question_id": 48, "answer1_id": "TMtjmb5tDizQ9RETRxpt2s", "answer2_id": "nSGoG9Z5g5VuCXgmQJJKrq", "text": "7 6\nAssistant 1 provided a more detailed response, considering factors such as self-published books and lost or destroyed books. They also provided a wider range of estimates for the total number of pages. However, their estimate of the total number of books ever published (129 million) was significantly higher than Assistant 2's estimate (13 million). Assistant 2's response was more concise and easier to follow, but it did not consider the factors mentioned by Assistant 1. Both assistants acknowledged the impossibility of providing an exact number, but Assistant 1's response was more comprehensive in its reasoning.", "score": [7.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "YDnAUmn3rLo2fXnQNMsSWi", "question_id": 49, "answer1_id": "AwQMPhhaJ32ByA3VjKF5Ph", "answer2_id": "ScqJdUq9n5bp9qPv5WPqG5", "text": "7 8\nBoth assistants provided relevant and detailed information in their responses. Assistant 1 gave a clear explanation of the Earth's orbit and its importance for life on Earth. However, the answer contained an incorrect statement about the Earth orbiting the Sun 1 billion times since the first life arose, which contradicts the initial statement of 4.543 billion times. Assistant 2 provided a more accurate and detailed explanation, taking into account the elliptical nature of the Earth's orbit and the average distance between the Earth and the Sun. Assistant 2 also acknowledged the uncertainties in the timing of the emergence of life and variations in the Earth's orbit, making their answer more reliable.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SdNHfEHe9iZHKbmYjkC5a4", "question_id": 50, "answer1_id": "UCqxXfMAnqJKbApsjrPFcq", "answer2_id": "GdLFr88pCwsGpfRBRQQkyh", "text": "8 7\nAssistant 1 provided a more detailed response by mentioning specific numbers from the US Copyright Office, Spotify, and Apple Music, which helps to give a better understanding of the scale of recorded songs. Assistant 2, while still providing a relevant response, focused more on the historical aspect of music and the development of recording technology, without providing specific numbers. Both assistants acknowledged the difficulty in estimating the exact number of songs recorded throughout history, but Assistant 1's response was more informative and precise.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6Tbxuai5ZBjbL7XgsGM59j", "question_id": 51, "answer1_id": "YMRg5Xi9BBvvqqny2tqJZ3", "answer2_id": "Sa7uNEbFGRFuXBAwau8sHS", "text": "8 7\nAssistant 1 provided a more focused and relevant response to the question, discussing the potential impact of the Internet on the Renaissance period in terms of intellectual growth, collaboration, education, and the arts. Assistant 2, while providing some interesting points about the technological context and the possible development of an early form of the Internet, did not directly address the question's focus on the impact of the Internet on the Renaissance period. Both assistants provided accurate information, but Assistant 1's response was more helpful and relevant to the user's question.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3xXeF3Goc2xSC9RJey8VNz", "question_id": 52, "answer1_id": "fhr7iMPb9SVJ663mXKBuct", "answer2_id": "T6Ar3cAEw3AmVVQTcCPp8N", "text": "8 9\nBoth assistants provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a good overview of the potential consequences of the Aztecs repelling the Spanish conquistadors, but Assistant 2 provided a more detailed response, discussing the potential impact on the Aztec civilization, the spread of European diseases, the economy of Mexico, and the possibility of forming alliances with other indigenous groups. Assistant 2's answer was more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "kGpPxDLcPjpJ8FsaBgWxeb", "question_id": 53, "answer1_id": "4rrmyZw9zhyJGqdrcazFvt", "answer2_id": "KiZQev5JEk2h6JYeQnFmtM", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the potential consequences of the Black Death not occurring in the 14th century. Assistant 1 focused on the decline of the population, the impact on society, and the changes in the way people lived. Assistant 2, on the other hand, provided a more detailed analysis of the potential demographic, economic, and social changes that could have occurred in the absence of the Black Death, as well as the possibility of other diseases spreading. Assistant 2's response was slightly more comprehensive and detailed, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "BUxwV6k4ktYY3rE7X4wH4B", "question_id": 54, "answer1_id": "Za3mY9xwxpZdPmGW48wtzu", "answer2_id": "cYiyYKKXM3GXkrZHAbX83S", "text": "7 8\nBoth Assistant 1 and Assistant 2 provided relevant and accurate responses to the question. Assistant 1 explored the potential outcomes of Newton focusing on biology, mentioning possible discoveries in medicine, mechanics of the human body, and causes of disease. However, Assistant 1 also considered the possibility that Newton might not have made significant discoveries in biology. Assistant 2 provided a more detailed response, discussing Newton's contributions to other fields and his wide range of interests. Assistant 2 also acknowledged the difficulty in predicting specific contributions without knowing more about Newton's interests in biology. Both responses were helpful, but Assistant 2's answer was more detailed and provided a broader perspective on Newton's scientific achievements, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "YS2v2hkjFoUNTRg9UAP67U", "question_id": 55, "answer1_id": "cbAaJS9ULjR4XYSHATujSG", "answer2_id": "PQmMUdAAcBsAWmWaTvdHSU", "text": "8 7\nBoth Assistant 1 and Assistant 2 provided relevant and helpful answers to the question. Assistant 1 focused more on the cultural impact of the Beatles and how their absence might have affected the world, while Assistant 2 provided a list of possible outcomes if the Beatles had never formed. Assistant 1's response was slightly more detailed and touched on the emotional aspect of the Beatles' influence, which is why it receives a higher score. Assistant 2's response was also helpful, but it was more speculative and less detailed in comparison.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LeebBihTDtAoBf6uEBYdcz", "question_id": 56, "answer1_id": "ZEgb9fvopGo7HF5wPeoeHs", "answer2_id": "PorExChQ9VeYsPJptdgtsB", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a clear explanation of the importance of Turing's work and its impact on the outcome of the war. However, Assistant 2 provided a more nuanced response, discussing the potential alternative strategies and technologies that the Allies might have pursued without Turing's contributions. This additional information and consideration of alternative scenarios make Assistant 2's response slightly more detailed and comprehensive, resulting in a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "W6qgavnMLN53fEy5HvfxhF", "question_id": 57, "answer1_id": "igMXoEiszFM65ZS2KUTvtm", "answer2_id": "249f6dSMwZRZVMmtxv6yDm", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused more on the impact on Egypt's economy and national pride, while Assistant 2 discussed the broader implications on international trade, global economic development, and the history of the region. Assistant 2 also mentioned the engineering and technological advancements required for the construction of the canal, which added more depth to the answer. Therefore, Assistant 2 receives a slightly higher score due to the additional details and broader perspective provided.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "VDKdWNYB6NcbkiNA9eWXSJ", "question_id": 58, "answer1_id": "Up4h8RpgVVafBtUj4tiGPZ", "answer2_id": "nxa3m6kiAZwKgcMUBY8KYz", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both acknowledged the advanced nature of the Maya civilization and the potential impact it could have had on the world if it had not collapsed. Assistant 1 provided a good overview of the possible outcomes, but Assistant 2 went into more detail about the potential advancements and influence the Maya civilization could have had on other civilizations in the region. Assistant 2 also mentioned the lasting impact of the Maya civilization on the region and their descendants, which adds more depth to the answer. Therefore, Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3KaALaanGsJQqzvwJFcnBL", "question_id": 59, "answer1_id": "CMg3ypKyykGUzQsDmX7i35", "answer2_id": "DXFvhjCaKqQiBsFDCkXGMT", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 discussed the possibility of other European explorers discovering the Americas and the potential for Chinese or Japanese settlements. Assistant 2 also mentioned the possibility of other European explorers discovering the Americas and focused more on the potential development of indigenous peoples without Columbus's discovery. Assistant 2's response was slightly more detailed and provided a clearer picture of the potential consequences of Columbus not discovering the Americas, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "h2qgd65upfZgKNUFsXgmHi", "question_id": 60, "answer1_id": "WhLiJXznzRBkzxDdnc5ndX", "answer2_id": "BCUK5VcpZK26jMJ2UMgR3E", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 explored the possibility of Van Gogh's success leading to a different impact on the art world and the potential influence of his tragic story on his art. Assistant 2 focused more on the potential changes in Van Gogh's art and personal life due to success and fame. Both responses acknowledged the impossibility of knowing the exact outcome. Assistant 2's response was slightly more detailed and well-rounded, considering the potential impact of fame and success on Van Gogh's personal life and art direction, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "WtY3uzgQLem7SJMRtmBuqc", "question_id": 61, "answer1_id": "Vzmhr5RMndkR866p4pUef6", "answer2_id": "JHTKBrLVTp7bv5zW2uwYVW", "text": "4 9\n\nAssistant 1:\nStrengths:\n- Polite and friendly response.\n\nWeaknesses:\n- Did not provide any code or solution to the given problem.\n- Misunderstood the task and offered unrelated services.\n\nSuggestions for improvement:\n- Focus on understanding the task and providing a relevant solution.\n- If unsure about the task, ask for clarification before providing an unrelated response.\n\nAssistant 2:\nStrengths:\n- Provided a working C++ program that solves the given problem.\n- Clear and concise comments explaining the code's logic and functionality.\n- Proper coding standards and best practices were followed.\n\nWeaknesses:\n- The program only counts lines containing the search string, not the actual number of occurrences of the word.\n\nSuggestions for improvement:\n- Modify the program to count the actual number of occurrences of the word in the file, not just the lines containing the word.\n- Consider handling edge cases, such as different letter cases or punctuation marks surrounding the search string.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "nnHxzRSqsuXx8A2hG2QZmE", "question_id": 62, "answer1_id": "jPrfhv9oaZGrkXxDUw5Vbt", "answer2_id": "jyzD2deb9JAffRhhkk37r5", "text": "8 4\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to read.\n- The algorithm is efficient with a time complexity of O(nm) and space complexity of O(mn).\n- The code correctly implements the given problem statement.\n\nWeaknesses:\n- The code lacks comments to explain the logic and functionality.\n\nSuggestions for improvement:\n- Add comments to explain the logic and functionality of the code.\n\nAssistant 2:\nStrengths:\n- The code includes comments that explain the code's logic and functionality.\n\nWeaknesses:\n- The code is less efficient than Assistant 1's code.\n- The code does not correctly implement the given problem statement. The initialization of the dp table is incorrect, and the logic for filling in the table is also incorrect.\n- The code is less readable and structured compared to Assistant 1's code.\n\nSuggestions for improvement:\n- Correct the initialization of the dp table by setting the first row and column to 0 instead of float('inf').\n- Correct the logic for filling in the table by following the dynamic programming approach used in Assistant 1's code.\n- Improve the code structure and readability by following the style used in Assistant 1's code.", "score": [8.0, 4.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "N6SA5JgsXewWz7hda37xer", "question_id": 63, "answer1_id": "KdZNrf6udTfCxGGcQxRbXk", "answer2_id": "jPGHEfp7FxhDyvRA6qEN3j", "text": "7 8.5\n\nAssistant 1:\nStrengths:\n- The code is simple and easy to understand.\n- The function is well-structured and returns a boolean value.\n\nWeaknesses:\n- The regular expression contains unnecessary spaces, which may cause issues in matching valid email addresses.\n- The regular expression does not cover all valid email address formats (e.g., special characters in the username).\n- There are no comments explaining the regular expression or the function.\n\nSuggestions for improvement:\n- Remove the spaces in the regular expression.\n- Update the regular expression to include more valid email address formats.\n- Add comments to explain the regular expression and the function.\n\nAssistant 2:\nStrengths:\n- The code is well-structured and easy to understand.\n- The regular expression covers a wider range of valid email address formats.\n- There are comments explaining the regular expression and the code.\n\nWeaknesses:\n- The regular expression does not cover all valid email address formats (e.g., special characters in the username).\n- The code does not include a function to check the email address, making it less reusable.\n\nSuggestions for improvement:\n- Update the regular expression to include more valid email address formats.\n- Encapsulate the email validation code in a function for better reusability.", "score": [7.0, 8.5], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "LKFYJtdK3e6mujtw8wSjdH", "question_id": 64, "answer1_id": "AFzqAfbYVL25DBqFA2cv4G", "answer2_id": "Ra9eTcAuNZPkPRnaHiQrak", "text": "7 4\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to read.\n- The dynamic programming approach is implemented correctly, using a table to store the results of previously solved subproblems.\n- The comments are clear and concise, explaining the logic and functionality of the code.\n\nWeaknesses:\n- The code does not handle the case when n = 0 or n = 1, which would result in an IndexError.\n- The code could be more efficient by using a memoization table with only two elements instead of a list of size n.\n\nSuggestions for improvement:\n- Add a condition to handle the cases when n = 0 or n = 1.\n- Use a memoization table with only two elements to save memory.\n\nAssistant 2:\nStrengths:\n- The code initializes a memoization table, which is a good start for a dynamic programming approach.\n\nWeaknesses:\n- The code does not actually use the memoization table, resulting in a less efficient solution.\n- The code does not handle the case when n = 0, which would result in an IndexError.\n- The comments are not as clear and concise as they could be, and they do not accurately describe the code's logic and functionality.\n\nSuggestions for improvement:\n- Implement the dynamic programming approach correctly by using the memoization table to store the results of previously solved subproblems.\n- Add a condition to handle the cases when n = 0 or n = 1.\n- Improve the comments to accurately describe the code's logic and functionality.", "score": [7.0, 4.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "MqumN4WHxBeQzbHE8XzKEc", "question_id": 65, "answer1_id": "Lt56JzWfXBxbvxrLf4fn3t", "answer2_id": "5mbEFLEnEx8Cm2wEF8WAbM", "text": "9 9\n\nBoth Assistant 1 and Assistant 2 provided correct and efficient implementations of the binary search algorithm. Their code is readable and well-structured. The time complexity of both implementations is O(log n), which is optimal for this problem.\n\nStrengths:\n- Both implementations are correct and follow the binary search algorithm.\n- The code is readable and well-structured in both submissions.\n- Both assistants provided a brief explanation of the algorithm and its time complexity.\n\nWeaknesses:\n- Assistant 1's implementation requires the user to provide low and high indices, which might be confusing for some users. Assistant 2's implementation is more user-friendly as it only requires the array and the target value as input parameters.\n- Neither assistant provided comments within the code to explain the logic and functionality. While the code is relatively simple and self-explanatory, adding comments would be helpful for users who are less familiar with the algorithm.\n\nSuggestions for improvement:\n- Assistant 1 could improve their implementation by removing the need for the user to provide low and high indices, similar to Assistant 2's implementation.\n- Both assistants should consider adding comments within the code to explain the logic and functionality, making it easier for users to understand the algorithm.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "ctTXTd9FC8vjGP9xFf6Gyb", "question_id": 66, "answer1_id": "bmaShWUyEqPE6kbHD6FjLk", "answer2_id": "KLbBzjbuwq4agAESQ9Wfpq", "text": "8 5\n\nAssistant 1:\nStrengths:\n- The implementation is correct and efficient.\n- The code is easy to read and understand.\n- The use of `__len__`, `__iter__`, and `__contains__` methods is a nice addition, making the class more versatile.\n\nWeaknesses:\n- The code lacks comments explaining the logic and functionality of the methods.\n\nSuggestions for improvement:\n- Add comments to explain the logic and functionality of the methods.\n\nAssistant 2:\nStrengths:\n- The code is readable and easy to understand.\n\nWeaknesses:\n- The implementation is incorrect. The `dequeue` method does not follow the logic of using two stacks to implement a queue.\n- The `enqueue` method clears the `out_stack`, which is unnecessary and inefficient.\n- The `peek` method returns the last item in the `in_stack`, which is not the front of the queue.\n- The `is_empty` method checks only the `in_stack`, which may lead to incorrect results if there are items in the `out_stack`.\n- The custom `EmptyQueueError` exception is not used effectively.\n\nSuggestions for improvement:\n- Correct the `dequeue` method to follow the logic of using two stacks to implement a queue.\n- Remove the unnecessary clearing of the `out_stack` in the `enqueue` method.\n- Modify the `peek` method to return the correct item at the front of the queue.\n- Update the `is_empty` method to check both stacks.\n- Use the `EmptyQueueError` exception effectively, or remove it if not needed.", "score": [8.0, 5.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "KoNz6J6gsCr6AgwW9rdyvK", "question_id": 67, "answer1_id": "hiSVom5YqSrKZKor8CjHdU", "answer2_id": "ganAwetd9VTzEAiTtsskzX", "text": "6 5\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to read.\n- The function has a clear docstring that explains its purpose, arguments, and return value.\n- The code uses a set to store the common elements, which ensures that duplicates are not included in the result.\n\nWeaknesses:\n- The problem statement specifically asked not to use any extra data structures, but the code uses a set.\n- The algorithm has a time complexity of O(n^2), which can be slow for large arrays.\n\nSuggestions for improvement:\n- To avoid using extra data structures, you can modify the code to directly print the common elements instead of storing them in a set.\n\nAssistant 2:\nStrengths:\n- The code follows the problem statement's requirement of not using any extra data structures.\n- The code is simple and easy to understand.\n\nWeaknesses:\n- The code does not include any comments or a docstring, which makes it harder for others to understand the purpose and functionality of the function.\n- The algorithm has a time complexity of O(n^2), which can be slow for large arrays.\n- The code does not handle duplicate elements in the input arrays, which may lead to duplicate common elements in the result.\n\nSuggestions for improvement:\n- Add comments and a docstring to the function to explain its purpose, arguments, and return value.\n- To handle duplicate elements, you can add a condition to check if the common element is already in the result list before appending it.", "score": [6.0, 5.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "m6AC23LQ8JYijV3GtsHskU", "question_id": 68, "answer1_id": "JRHeURg4TrVAgWNp5yqjFx", "answer2_id": "LG6szQ3cqNRzUjXtZtXG94", "text": "First, I will solve the problem independently:\n\nf(x) = 5x^3 - 2x + 3\nf(2) = 5(2)^3 - 2(2) + 3 = 5(8) - 4 + 3 = 40 - 4 + 3 = 39\n\nNow, I will evaluate the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nf(2) = 5(2)^3 - 2(2) + 3 = 40 - 4 + 3 = 39\nThe steps and the final answer are correct.\n\nAssistant 2:\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 2 + 3 = 41 - 2 + 3 = 40\nThe steps are incorrect, and the final answer is wrong.\n\nScores:\nAssistant 1: 10\nAssistant 2: 4\n\nAssistant 1 performed better because they provided the correct steps and final answer, while Assistant 2 made mistakes in their calculations.", "score": [10, 4], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "34uzSiz6E2mtCxcqmAUF9P", "question_id": 69, "answer1_id": "hZwFKeRYBLKihBEWSEGMYJ", "answer2_id": "LHMnXGbSZubeZJcr5fgrte", "text": "First, I will solve the problem independently:\n\n3x + 10 = 5(x - 2)\n\nDistribute the 5 on the right-hand side of the equation:\n\n3x + 10 = 5x - 10\n\nSubtract 3x from both sides:\n\n10 = 2x - 10\n\nAdd 10 to both sides:\n\n20 = 2x\n\nDivide both sides by 2:\n\nx = 10\n\nNow, I will evaluate the answers provided by Assistant 1 and Assistant 2.\n\nAssistant 1:\n\nThe steps provided by Assistant 1 are correct, and the final answer of x = 5 is accurate.\n\nAssistant 2:\n\nThe steps provided by Assistant 2 are incorrect. The assistant made an error in step 2 by subtracting 5x from both sides, resulting in 3x = -5x, which is incorrect. The correct step should be subtracting 3x from both sides, resulting in 2x = 20. The rest of the steps are also incorrect due to this initial error.\n\nScores:\n\nAssistant 1: 10\nAssistant 2: 1\n\nAssistant 1 performed better and provided the correct solution to the equation.", "score": [10, 1], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "57H8uDw5kYEX2wFgvY8BN8", "question_id": 70, "answer1_id": "iG4SJq9xCKCDDtiqU3szJZ", "answer2_id": "TGENVWL5iAYi4NCf5jAzKy", "text": "First, I will solve the problem independently:\n\nGiven the endpoints of a line segment are (2, -2) and (10, 4), we can use the distance formula to find the length of the segment:\n\n$$d = \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$\n\nSubstituting the given coordinates:\n\n$$d = \\sqrt{(10 - 2)^2 + (4 - (-2))^2} = \\sqrt{8^2 + 6^2} = \\sqrt{64 + 36} = \\sqrt{100} = 10$$\n\nThe length of the line segment is 10 units.\n\nNow, I will examine the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe steps provided by Assistant 1 are correct, and the final answer is also correct. The length of the line segment is 10 units.\n\nAssistant 2:\nAssistant 2 made a mistake in calculating the difference between the x-coordinates and the y-coordinates. The correct differences should be 8 and 6, not 18 and 4. This led to an incorrect final answer of 17.973.\n\nScores:\nAssistant 1: 10\nAssistant 2: 4\n\nAssistant 1 performed better in this case, as they provided the correct steps and answer, while Assistant 2 made a mistake in the calculation.", "score": [10, 2], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "3bZ3BoLmb48KjWJ5q4NmMU", "question_id": 71, "answer1_id": "CjhV8cJ6UEp7Y4f3eWB6E5", "answer2_id": "kc9oytarvqyNVVfGZz93EL", "text": "9 9.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's request for a formal email proposing a joint venture. Both responses included appropriate greetings, explanations of the proposal, and reasons for the joint venture. Assistant 1's response was slightly more concise, while Assistant 2's response provided a bit more detail about the structure of the proposed joint venture and the potential benefits. Both responses were of high quality, but Assistant 2's response was slightly more detailed, which is why it received a slightly higher score.", "score": [9.0, 9.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nErSsdfQf8KAT2XtVLDqVh", "question_id": 72, "answer1_id": "5waLH9pZsr8jVTpy67QH58", "answer2_id": "NxstpvVEnGMny3jECtvrw2", "text": "9 10\nBoth assistants provided relevant and helpful resignation letter templates. Assistant 1's response was concise and to the point, while Assistant 2's response was more detailed and included additional information about ensuring a smooth transition and completing outstanding tasks. Assistant 2's response was slightly better due to the additional details and consideration for the employer's needs during the transition.", "score": [9.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Q4QCqD7baQ8PkmzaJ4qJv7", "question_id": 73, "answer1_id": "ZV4sUNujQGS2w7Wg4jbXbf", "answer2_id": "XN9NBpeQJiQqGT5UYkys7N", "text": "9 10\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. They both structured a formal letter of recommendation for a student applying to a prestigious graduate program in computer science. Assistant 1's response was concise and well-organized, but Assistant 2's response was more detailed and included additional elements such as the sender's and recipient's affiliations, addresses, and date, which made it more complete and formal. Therefore, Assistant 2 receives a slightly higher score.", "score": [9.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LVJ5BasrkhW8giXxYVqu9W", "question_id": 74, "answer1_id": "X94xm9m7tayWsKFNACqAG4", "answer2_id": "3qMqAZcfHUVUTEJnMXT8Pw", "text": "9 9.5\nBoth Assistant 1 and Assistant 2 provided relevant, accurate, and detailed responses to the user's request for a compelling product launch announcement email. Assistant 1's response was concise and focused on the benefits of the new software solution, while Assistant 2's response was more detailed, providing a list of key features and emphasizing the research and development behind the product. Both responses were helpful and well-structured. However, Assistant 2's response was slightly more compelling due to the additional details and emphasis on the product's innovation, which is why it receives a slightly higher score.", "score": [9.0, 9.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Pn2guBepbwfxhn2ev3JZoc", "question_id": 75, "answer1_id": "EFL7fU6gnicQY6DRpHvbTK", "answer2_id": "hMXjd228Mc8pnc7zgd2frG", "text": "9 10\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's request. Assistant 1's response was clear and concise, addressing the issue and providing reassurance that the problem has been resolved. Assistant 2's response was more detailed, including a subject line and additional information about the investigation and steps taken to prevent future delays. Both responses were well-written and appropriate for the situation, but Assistant 2's response was slightly more comprehensive, earning it a higher score.", "score": [9.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7NJp7H3YHHQdWZmyMdcHpi", "question_id": 76, "answer1_id": "XJLFjCr6vgQZjEXbaMjTa6", "answer2_id": "gUEd9nNepqy8nsfbypscHx", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the history and cultural significance of jazz. Assistant 1's response was more structured and detailed, covering the roots of jazz, its history, and its cultural significance, while Assistant 2's response was slightly less detailed but still informative and engaging. Assistant 1's script was more comprehensive, which is why it received a slightly higher score.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "MSCbf4yKFcdVNcQFDjYyAU", "question_id": 77, "answer1_id": "bz3rS9MRs9QmaLGg4A8a7B", "answer2_id": "nZZyn3SAvU8T2BVR73LMHC", "text": "9 10\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about a trip to Hawaii, highlighting cultural experiences and must-see attractions. Assistant 1 gave a clear and concise list of suggestions for cultural experiences and must-see attractions, making it easy for readers to understand and plan their trip. Assistant 2, on the other hand, crafted a more engaging and immersive narrative, sharing personal experiences and emotions, which is more fitting for a travel blog post. While both responses were informative, Assistant 2's response was more engaging and better suited to the requested format, which is why it receives a slightly higher score.", "score": [9.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dUUREscUNiAEwZMX2Bv8pf", "question_id": 78, "answer1_id": "kCS2ZqzKTdDpaXwu3TiFXP", "answer2_id": "YM4qjUM5VfmWnzM3HXRB4G", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided captivating movie reviews for recently released science fiction films. Assistant 1 focused on the movie \"Dune,\" providing a detailed review of the plot, characters, and special effects, as well as mentioning the film's score. Assistant 2 reviewed a fictional movie called \"Beyond the Horizon,\" also discussing the plot, characters, and special effects. Both reviews were engaging and informative. Assistant 1's response was slightly more detailed and specific, which is why it received a higher score of 9, while Assistant 2's response was still strong and received a score of 8.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Mfyo2MkTqFoXcoVRYgYMpC", "question_id": 79, "answer1_id": "7WWWS5UhWcVzkhTypqcFU4", "answer2_id": "WcbzNkzu5q66QkK92NxmGB", "text": "8 9\nBoth assistants provided relevant and detailed podcast scripts discussing the influence of streaming platforms on the music industry. Assistant 1's script was well-structured, covering the pros and cons of streaming platforms and their impact on the music industry. However, Assistant 2's script was slightly more engaging and organized, with clearly defined segments and a more conversational tone. Additionally, Assistant 2 touched on the future of streaming platforms, which added depth to the discussion. Overall, both scripts were helpful and accurate, but Assistant 2's script was more appealing and comprehensive.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "T59yNWEAj9kzX8k2QbWom4", "question_id": 80, "answer1_id": "QcSFqbgqLWqn96V6BvWT7B", "answer2_id": "Tfm4f2mbpKJsa8MT6exs8D", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided detailed and engaging symphony concert reviews that discussed the orchestra's performance and the overall audience experience. Both responses were relevant, accurate, and helpful in conveying the atmosphere and quality of the performances. The reason for giving both assistants the same score is that they both demonstrated a high level of detail and quality in their responses, making it difficult to differentiate between the two in terms of performance.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
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-{"review_id": "jyhS7AFj2mrFNqoRXQJDPS", "question_id": 1, "answer1_id": "BZGowHM7L3RvtWRktKZjLT", "answer2_id": "cV4zXygaNP6CXEsgdHMEqz", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information on improving time management skills. Assistant 1 provided a slightly more detailed response, including tips on minimizing distractions, learning to say 'no', and delegating tasks. Assistant 2 also provided useful tips, but the response was not as comprehensive as Assistant 1's. Both assistants emphasized the importance of setting goals, using a calendar or planner, and taking breaks. Overall, both responses were helpful, but Assistant 1's response was slightly more detailed and comprehensive.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "NVTHESK8sQGgYq5RmQTpMr", "question_id": 2, "answer1_id": "GsrPLfiNEcRDYAC5aUoY23", "answer2_id": "3zpPUeoVsPWXtKMV7cAhZ6", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about effective ways to deal with stress. Both answers included similar suggestions such as exercise, relaxation techniques, sleep, healthy diet, social support, time management, and seeking professional help. The level of detail in both responses was sufficient to provide a clear understanding of each strategy. Assistant 2 included an additional suggestion of relaxation techniques like progressive muscle relaxation, aromatherapy, and massage, which slightly enhanced the response. However, both assistants performed exceptionally well, and their answers were equally informative and helpful.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fDwSXjgkma7Gu4b8QGUaCY", "question_id": 3, "answer1_id": "5SGfexmPoJTWoVbPMB87KB", "answer2_id": "6xpiZJE4vxxGqjRogPfBk7", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the differences between Python and JavaScript. Assistant 1 provided a more structured response, covering programming paradigms, syntax, typing, and application areas, which made it easier to understand the key differences. Assistant 2 also covered similar points but included additional information about error handling, libraries, and speed. However, Assistant 2's response was slightly less organized, making it a bit harder to follow. Both assistants provided a good level of detail, but Assistant 1's response was more concise and easier to understand, which is why it receives a slightly higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "cJw4V6atyX2JHA4FZM6Qc3", "question_id": 4, "answer1_id": "RcqQg364kgCkYPshqvmGSp", "answer2_id": "abitTVu2Dz8rdMxyThvFyJ", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information on increasing productivity while working from home. Assistant 1 offered a slightly more detailed response with 8 tips, while Assistant 2 provided 7 tips. Both assistants covered similar points, such as creating a dedicated workspace, setting a schedule, eliminating distractions, getting dressed, and taking breaks. However, Assistant 1 included the additional points of making a to-do list, challenging oneself, and staying positive, which contributed to a more comprehensive answer. Assistant 2's response was also well-rounded, but slightly less detailed compared to Assistant 1.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2ZMgfaT49yqAiA3GYLNfp4", "question_id": 5, "answer1_id": "3R9jAndzLHQqJwmMi5mNox", "answer2_id": "UMZod8JaWia9KB2EVXdYrF", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate explanations of the basics of quantum computing. Assistant 1's response was slightly more detailed, covering different technologies used to build quantum computers and mentioning the importance of quantum computing in solving problems that are intractable for classical computers. Assistant 2's response was also informative, but it focused more on different approaches to building quantum computers rather than the broader context of quantum computing. Both responses were well-structured and informative, but Assistant 1's answer was more comprehensive, which is why it receives a slightly higher score.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Wz7U3tcQhAMK6HgLPwM7C3", "question_id": 6, "answer1_id": "Uw8SaYLPMGR5sdV9FDx8pb", "answer2_id": "WXRCqF34f4UUJzd3xbgAqT", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the differences between plant-based and animal-based protein sources. Assistant 1 provided a slightly more concise response, while Assistant 2 provided a more detailed response with a numbered list of differences. Assistant 1 mentioned the importance of animal-based protein sources for athletes and individuals with higher protein needs, which was a useful addition. Both assistants discussed the nutritional value, absorption, sustainability, health effects, and ethical considerations of the two types of protein sources. However, Assistant 1's response was more precise and to the point, making it easier to understand and digest the information, which is why it receives a slightly higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "M3Yd3jLJrBzHL2KmmaiLmX", "question_id": 7, "answer1_id": "53gmokt2KBgKu6NMPopxcu", "answer2_id": "JREJbG5ZKXCNMoWoiJQxbC", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about developing critical thinking skills. Both answers included practical tips and strategies for improving critical thinking, such as asking questions, analyzing information, considering different perspectives, and practicing logic and reasoning. Both responses were detailed and well-organized, making it easy for the user to understand and apply the suggestions. The only minor difference is that Assistant 2 included a few additional tips, such as engaging in debates and practicing mindfulness. However, both answers are equally valuable and informative, so they both receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "D4dXgsgoBdNwqUxxpmUkmp", "question_id": 8, "answer1_id": "bKrbrGsN7zjKLvBk2h3tvo", "answer2_id": "mmVwmX6TGJ2Y72gCNac4EQ", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about the major challenges faced by the education sector today. Assistant 1 provided a clear list of seven challenges, while Assistant 2 expanded on these and included an additional challenge, making their response slightly more detailed. Both assistants covered important aspects such as access to education, funding, teacher shortages, technological integration, student engagement, and assessment. Assistant 2 also addressed equity and inclusion, as well as sustainability, which added value to their response. Overall, both assistants performed well, but Assistant 2 provided a more comprehensive answer.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ntSaBiMeRMC82i2S2wkHh6", "question_id": 9, "answer1_id": "HEGL3aPUnNrdNtNt3XLDKi", "answer2_id": "DMTZyzd4rRAFV43xtBJ9ns", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both covered the primary factors that influence consumer behavior, such as personal, psychological, social, and marketing factors. Assistant 1 mentioned situational factors, while Assistant 2 included economic and product/service factors. Both answers were detailed and informative, and it is difficult to determine which one is superior, as they both provide valuable insights. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "eTBH8zoQhZfYYCAhQh4moS", "question_id": 10, "answer1_id": "W9zpMVa2cJUJW8B2uGMCJy", "answer2_id": "dETAsj4xHnUCSTkZezz8aM", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about effective strategies for conflict resolution in the workplace. They both covered essential strategies such as active listening, clear communication, problem-solving, and seeking outside help or mediation if necessary. The level of detail in both responses was sufficient to understand the strategies and apply them in a workplace setting. Both assistants performed well, and it is difficult to differentiate their performance, so they both receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Y6cDAMFAEauqwsaGdHoraf", "question_id": 11, "answer1_id": "LacdmoweqKYGzt3aMBYjEa", "answer2_id": "C8ksZxg3LshMUWiLxPanbt", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information regarding the implications of using single-use plastic bottles versus reusable bottles on the environment and human health. Assistant 1's response was slightly more detailed and organized, with clear distinctions between environmental and health implications for both single-use and reusable bottles. Assistant 2 also provided valuable information, but the response was not as well-structured, and some points were not as clearly explained as in Assistant 1's response. Overall, both assistants performed well, but Assistant 1 had a slight edge in terms of clarity and organization.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "evnANWPnvUJ89vxV3sRZ7M", "question_id": 12, "answer1_id": "JqVreebbPuNdjw8E8K4Ssf", "answer2_id": "NeHhRc5P5uAU8eWSJBRkhG", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information in their responses. Assistant 1's response was slightly more detailed, covering a broader range of factors such as sensory inclusivity and employee training, which were not mentioned by Assistant 2. Assistant 2's response was also comprehensive, but it lacked the mention of sensory inclusivity and employee training. Both assistants provided valuable information on accessibility features, route design, scheduling, and affordability. Overall, Assistant 1's response was slightly more detailed and comprehensive, earning a 9, while Assistant 2's response was also strong but slightly less detailed, earning an 8.5.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7X5LTBTBncxNXwdhDvknWG", "question_id": 13, "answer1_id": "hEMThhsN85Ud5X8xBv9BZJ", "answer2_id": "KAJ7UVwu8oCKyxZj9j82pm", "text": "9 8.5\nBoth assistants provided helpful, relevant, and accurate information about fiscal and monetary policies to combat economic recessions. Assistant 1's response was slightly more structured and concise, making it easier to understand the key points. Assistant 2's response was also informative and detailed, but the structure was less clear, and some points were repetitive. Both assistants covered the main aspects of fiscal and monetary policies, but Assistant 1's response was more precise and well-organized.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7FK5fbRY6p2ep2MpPjv3yH", "question_id": 14, "answer1_id": "BvFV7sx53PAK5bNn89urFs", "answer2_id": "NnWfaeRe8PmitgmV4u5fY8", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a clear explanation of how language and cultural barriers can impact communication and relationships in multicultural societies, and also mentioned the importance of language classes, cultural exchange programs, and sensitivity training. Assistant 2 provided a more detailed response, discussing specific aspects of communication and relationships that can be affected by language and cultural barriers, such as cultural norms, stereotypes, prejudice, and power dynamics. While both answers were informative, Assistant 2's response was slightly more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "csv7uSp3JKQTDajTge3gCW", "question_id": 15, "answer1_id": "dM5GHbLuPNfzUbBnJz6w7K", "answer2_id": "WiGpqKRRwWonwgU95piNNc", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and helpful responses to the question. Assistant 1 focused on a specific scenario involving AI-powered chatbots for patient triage and automating routine tasks, which was a clear and concise example. Assistant 2, on the other hand, provided a broader overview of various ways AI could improve healthcare delivery, including data analysis, automating tasks, remote monitoring, personalized treatment plans, and speeding up research and development. Assistant 2's response was more comprehensive and covered a wider range of applications, which is why it received a slightly higher score. Both responses were accurate and detailed, but Assistant 2's answer provided a more extensive understanding of AI's potential impact on healthcare.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "AusuMoEsTd4zExWnGKz95b", "question_id": 16, "answer1_id": "BX7maaP5kGY6bBTLJRwkit", "answer2_id": "iangiZeex5ndxAXnWMrxBW", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the process of gene editing using CRISPR-Cas9 technology, its potential applications, and ethical implications. Assistant 1 provided a slightly more detailed response, discussing the potential for eugenics, unintended consequences, and issues of access and equity. Assistant 2 also covered the main points, but with a bit less detail on the ethical implications. Both assistants did a good job, but Assistant 1's response was slightly more comprehensive.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dUmijornRYz6nnYGYnZtv2", "question_id": 17, "answer1_id": "STuX8oc7Gu3SN6EWzwpUpp", "answer2_id": "XnMRLphzYQX4QRNht7tbui", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both explained the concept of vaccinations and how they work to protect individuals and communities from infectious diseases. They also both provided a clear explanation of herd immunity and its importance in preventing the spread of diseases. The quality of the answers is quite similar, and both assistants deserve a high score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nYNJvBeat7YrWwEeNjHZts", "question_id": 18, "answer1_id": "TFUUXWS7yn2u2b4n7eM3ZB", "answer2_id": "HZc37bwy646mRzbqSsDAob", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 gave a good overview of the role of social media platforms in news consumption and the implications of misinformation. However, Assistant 2 provided a more detailed response, discussing specific factors such as personalization, virality, amplification, filter bubbles, confirmation bias, and lack of fact-checking, which contributed to a better understanding of the issue. Therefore, Assistant 2 receives a slightly higher score due to the level of detail in their response.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "P4hakPhF7TKj55mTydH4NT", "question_id": 19, "answer1_id": "3yRq2XXPi83H7Rr5SZS9rE", "answer2_id": "iJrMatLrMdJyyqMx9uJ45a", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1's response was slightly more detailed, offering specific examples and strategies for promoting healthier diets, such as imposing taxes on unhealthy foods and increasing funding for community gardens and farmers' markets. Assistant 2 also provided a good response, with clear examples of how cultural, social, and economic factors influence food choices. However, Assistant 2's response was slightly less detailed in terms of strategies for promoting healthier diets. Both assistants performed well, but Assistant 1's response was more comprehensive and actionable.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "GbEY9PMrmhDNm5XUB3AYmc", "question_id": 20, "answer1_id": "Sw34dAwQPCfGGotwRwhvtv", "answer2_id": "oVEHqDnDTEADZSFfKgFTZd", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed explanations of the process of natural selection and how it contributes to the evolution and adaptation of species. Assistant 1 focused on explaining the essence of natural selection and its role as a driver of evolution and adaptation, while Assistant 2 provided a more structured explanation with numbered steps. Both explanations complement each other and provide a comprehensive understanding of the topic. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "JJ6BtrjfFvmjA9nvyJvNCL", "question_id": 21, "answer1_id": "cZw4Jw8Zyz6ZUy4WDsC6ta", "answer2_id": "WLAj4u59bj2oEXzahF79ek", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. They both included a sample introduction speech for a medieval knight at a royal banquet, emphasizing humility, loyalty, and service to the kingdom. The level of detail in both responses was appropriate and engaging. It is difficult to distinguish one as better than the other, as both responses effectively address the user's question, so they both receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "KNxHKpigcbEiptJeGiXtyd", "question_id": 22, "answer1_id": "nj9y2HTWFGsD5B278ozm73", "answer2_id": "fJPnM2XcRveW2zR4DDaeTb", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided relevant and engaging speeches to motivate a pirate crew to search for hidden treasure. Assistant 1's response was more concise and focused on the thrill of adventure and the rewards that await the crew. Assistant 2, on the other hand, started by clarifying that they do not condone piracy and then provided a speech that emphasized the challenges and the determination needed to succeed. Both speeches were well-crafted and detailed, but Assistant 1's response was slightly more direct and to the point, which is why it received a higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "jsobbVWb4XgXruX5KGSAzP", "question_id": 23, "answer1_id": "Ep9rLcNzEGhG7AgbLTpRtm", "answer2_id": "fY4Xed6veWpcKfj5krZz8m", "text": "9 8\nBoth assistants provided relevant and creative soliloquies in the style of Shakespearean characters declaring their love. Assistant 1's soliloquy was slightly more engaging and used more vivid imagery, which made it stand out. Assistant 2's soliloquy was also well-written and relevant, but it lacked the same level of depth and emotional impact as Assistant 1's response. Both responses were accurate and detailed, but Assistant 1's soliloquy had a slight edge in terms of quality.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nzQdRQqiGrCEPovYpkfcuq", "question_id": 24, "answer1_id": "oNULT72cYUvit7D9SHb5aM", "answer2_id": "LqFJA2JQbBXP77nkYjtrZK", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the user's question. They each shared their origin stories in a way that would be understandable and engaging for a curious child. Assistant 1 focused on the transformation from an ordinary person to a superhero, emphasizing the importance of courage and helping others. Assistant 2 explained their creation as an advanced artificial intelligence, highlighting their strong moral compass and desire to help others. Both answers were detailed and well-rounded, making it difficult to choose one over the other. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bxv7oJi28Yny5CpxYtTriN", "question_id": 25, "answer1_id": "TX86xjPKTk2UxWwV4e8zRK", "answer2_id": "WxCKcRstJPXTXnqEzSX3Hg", "text": "8 7.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the technological advancements in the year 3000. Assistant 1's response was more concise and organized, with a clear list format and a brief explanation of each advancement. Assistant 2's response was also informative, but it lacked the same level of organization and clarity as Assistant 1's response. Additionally, Assistant 1 mentioned the eradication of diseases like cancer and Alzheimer's and the reversal of the aging process, which are significant advancements that Assistant 2 did not mention. Overall, both assistants provided valuable information, but Assistant 1's response was slightly more effective in conveying the information.", "score": [8.0, 7.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "93SEJYNg3w6FJDC6CCVf3s", "question_id": 26, "answer1_id": "e5YFb6PojDThkcZdSH8kpC", "answer2_id": "dgPJ7DvtFHD7mxtRwzM3vW", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided detailed and engaging descriptions of the winning play in the final seconds of a championship game. They both captured the excitement and tension of the moment, while also highlighting the skill and determination of the players involved. Both responses were relevant, accurate, and helpful in answering the user's question. It is difficult to differentiate between the two in terms of quality, as both responses were well-written and effectively conveyed the thrilling nature of the game's conclusion. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dhy48WZaBT7h7XUmY4Kt3Y", "question_id": 27, "answer1_id": "NnkbyoNFRHay4toKTFfaeE", "answer2_id": "ADX83sWvjJkewJX6JmYUzQ", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided detailed, relevant, and engaging descriptions of their respective signature dishes. They both showcased their creativity and passion for cooking, as well as their attention to detail in the preparation and presentation of the dishes. Both responses were equally helpful, accurate, and detailed, making it difficult to differentiate between the two in terms of overall performance. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "WmyJQvRV2wheGgHifaTX8o", "question_id": 28, "answer1_id": "Gpb8MuNU3Pt7k93dpRN9WM", "answer2_id": "ihNG3rwsrt95NDhCAFeSDR", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and accurate information about the summit of Mount Everest and the emotions a climber might experience. Assistant 1 gave a good description of the summit and the challenges faced during the climb. Assistant 2, however, provided a more vivid and detailed description of the emotions and the view from the top, making their response slightly more engaging and informative. Both assistants acknowledged their limitations as AI language models, but Assistant 2's response was more aligned with the user's question, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZBjXjAUfmo636RD8ftGmoj", "question_id": 29, "answer1_id": "SYvkCCHBUZPd9DQuidZM8K", "answer2_id": "Gmhqf3z4LvVfwPNFJ89BKd", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the user's question. Assistant 1 provided a more structured response, listing the challenges faced by a Mars colonist and then describing the daily life and activities. Assistant 2 also provided a detailed response, focusing more on the daily routine and integrating the challenges faced within that routine. Assistant 1's response was slightly more comprehensive and organized, which is why it receives a higher score. However, both responses were informative and addressed the user's question effectively.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "m96t6EWjwebt3SBbVs8QKi", "question_id": 30, "answer1_id": "NjdsG8tYfrHMT5zGZPavk6", "answer2_id": "gSwkKJCn6qDnNZond2xVJ3", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided engaging and detailed responses to the user's question. They both described the character's survival strategies, allies encountered, and the importance of trust and instincts in a post-apocalyptic world. Both responses were relevant and accurate, with a good level of detail. It is difficult to differentiate between the two responses in terms of quality, as both assistants performed exceptionally well in addressing the user's question.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RsFZsrSQGvqkU9qRu6MzeE", "question_id": 31, "answer1_id": "8eovAhyvrKJEMWiVdYzByH", "answer2_id": "8RaBeMjxx2bCp2GKWv7YiP", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. They both offered multiple ways to determine if a restaurant is popular among locals or mainly attracts tourists, and they explained why this information might be useful. The level of detail in both responses is sufficient to guide the user in making informed decisions about where to dine. It's difficult to differentiate the quality of the two responses, as they both cover similar points and provide valuable information. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Do5xK3swjiBBXLCSxCZrJv", "question_id": 32, "answer1_id": "nvyaGEveLWBaxgXzriB93d", "answer2_id": "C65PZkmAfFfWRs4bPhyKqg", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1's response was slightly more detailed, with a clear list of seven clues to look for, while Assistant 2 provided six clues. Both assistants covered similar points, but Assistant 1's response was more organized and easier to follow. Assistant 2's response was also helpful and relevant, but slightly less detailed and organized compared to Assistant 1. Overall, both assistants performed well, but Assistant 1 had a slight edge in terms of clarity and organization.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6coRp7diG94jbQfxFa2NTw", "question_id": 33, "answer1_id": "3xU2t6Yvx9EWpqfqvinNfH", "answer2_id": "4so4HTEjgDZKTqNAgkHHQX", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both covered the main reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app. The level of detail in both responses was sufficient to address the user's question. Assistant 1 provided a slightly more concise answer, while Assistant 2 elaborated a bit more on each point. However, both answers were of high quality and deserving of equal scores.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "neKDsPNtPp68GyPCK6C7wc", "question_id": 34, "answer1_id": "Mq6hzNziUxzQ2juPMDrv3h", "answer2_id": "FCYaiexEzdoLFPAwvTgDDm", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both mentioned key points such as body language, active listening, and follow-up as indicators of genuine interest in a conversation. Both responses were detailed and well-structured, making it easy for the reader to understand the points being made. It is difficult to differentiate between the two responses in terms of quality, as both assistants provided valuable information and covered the topic thoroughly. Therefore, both Assistant 1 and Assistant 2 receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fsikYyNM5HZSFuwtez49zW", "question_id": 35, "answer1_id": "KU6BNNN8d6MLHyrA8nV4DB", "answer2_id": "76EPQDh4ZNxBMGqED9LEFi", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, accurate, and detailed responses to the question. They both listed several reasons why someone might prefer to shop at a small, locally-owned business instead of a large chain store, even if the prices are higher. The reasons provided by both assistants were similar, with some slight variations in wording and the order of the points. Both responses were well-structured and easy to understand, making it difficult to differentiate between the two in terms of quality. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6U9bZZbDKJLudgPjSVUJ6m", "question_id": 36, "answer1_id": "RpHbPLJamuknRRa3xU5bUF", "answer2_id": "cvBg3gyCyDuyESof3YXhTE", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1's response was slightly more detailed and organized, with a clear list of factors to consider when assessing the credibility of a source. Assistant 2's response also provided valuable information, but it was less structured and had some overlap with Assistant 1's points. Both assistants emphasized the importance of checking the author's credentials, publication reputation, objectivity, and supporting evidence. Assistant 1 also mentioned evaluating the tone and verifying the information using reputable sources, which added value to their response. Overall, both assistants performed well, but Assistant 1's response was slightly more comprehensive and well-organized.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "UUzrSYFRn2RZDBvpse2Bvg", "question_id": 37, "answer1_id": "AFR3AJW4sSPLDLiAUvrL8s", "answer2_id": "kRgfUJ7qqkyZUnLd2fnnaX", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a good general overview of the factors that may influence individual preferences for fear-inducing experiences, while Assistant 2 provided a more detailed list of possible explanations, including brain chemistry, life experiences, personality traits, cultural factors, and learning. Assistant 2's response was slightly more comprehensive and informative, which is why it received a higher score. However, both responses were of high quality and addressed the question effectively.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Pi4zzV2gr6xqvZdLZJEU7k", "question_id": 38, "answer1_id": "esqiBYHa56ygcPU2ux2Pdx", "answer2_id": "J3YuizKcHQ74ydNyCcwgwu", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 provided a more detailed response, offering specific examples of how observing behavior can provide clues about cultural norms and expectations, such as dress codes, social hierarchy, communication styles, and customs and traditions. Assistant 2 also provided a relevant answer, discussing the importance of observing behavior to understand cultural norms, but provided fewer specific examples. Both answers were useful, but Assistant 1's response was more comprehensive and detailed, which is why it received a higher score.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "eADSQUs3wpe48gow6zcWUf", "question_id": 39, "answer1_id": "NmuuKUipqt62QKuEHCuBWh", "answer2_id": "abWLpFojLpNPfDGHpuRSUG", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided thoughtful, nuanced, and detailed answers to the question. They both acknowledged the complexity of the issue and presented arguments for both sides, highlighting the benefits and risks of space exploration and addressing Earth's problems. Both assistants emphasized the importance of personal values and priorities in making a decision on this topic. The quality of the answers is very similar, and both assistants deserve a high score of 9 for their performance.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "PZv2G3p5PXWEXDQNRgzUJL", "question_id": 40, "answer1_id": "3HypDqXt6tHieMDN7hWYCh", "answer2_id": "Ki4fkJvsoSxuQeSoj2AcBG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. They both emphasized the importance of striking a balance between job creation and technological progress. Assistant 1 provided examples of methods for promoting job creation, while Assistant 2 went a step further by discussing policies and programs aimed at creating new jobs in industries less susceptible to automation and retraining programs for displaced workers. Assistant 2 also mentioned the benefits of technological progress in various sectors, such as healthcare, transportation, communication, and energy production. This additional information and the more comprehensive approach give Assistant 2 a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "F3bgdb6Q7KPRcx5fkXYSDp", "question_id": 41, "answer1_id": "DmQtupeyNDrQFBccBRAsbD", "answer2_id": "GRQjYgAapNAVqzuZR4KQpf", "text": "7 5\nAssistant 1 provided a more accurate and detailed response, considering the average blink rate, waking hours, and lifespan. The explanation was clear and easy to follow. Assistant 2, on the other hand, made an error in calculating the number of blinks per day and provided a less accurate estimate. Additionally, Assistant 2's response was less organized and harder to follow.", "score": [7.0, 5.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "hAGDMdzkakkM7ZNFj4Se6h", "question_id": 42, "answer1_id": "froHv7kwRMYGWPXDQXk2Gw", "answer2_id": "SB5a2AiK224hB927vkqvYs", "text": "9 6\nAssistant 1 provided a more accurate and detailed response, using the average weight of a grain of salt and the molar mass of NaCl to calculate the number of atoms in a grain of salt. The explanation was clear and easy to follow, and the final result was reasonable. Assistant 2, on the other hand, made an incorrect assumption that the grain of salt is a sphere and used the diameter to calculate the volume. Additionally, the calculation of the concentration of atoms per unit volume was incorrect, leading to an inaccurate final result. While Assistant 2's response contained some useful information, it was less accurate and less helpful than Assistant 1's response.", "score": [9.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SuUVSbhzMDWFbSdwBEn7Si", "question_id": 43, "answer1_id": "ahktv9NqxZ2cYquTXwF42r", "answer2_id": "4XYycBrMiJgYKPof2P6xoe", "text": "9 7\nAssistant 1 provided a more detailed and step-by-step explanation of how the estimated number of lightning strikes per day was calculated, taking into account the number of thunderstorms, the percentage that produce lightning, and the average number of strikes per thunderstorm. The final estimate of 1,972,600 lightning strikes per day was also accompanied by a note that some estimates range from 6 million to 100 million, acknowledging the variability in the data. Assistant 2, on the other hand, gave a more general explanation and provided an estimate of 100,000,000 lightning strikes per day without a clear calculation process. While both answers were relevant and accurate to some extent, Assistant 1's response was more helpful, precise, and detailed, which is why it received a higher score.", "score": [9.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "kYsPQzYx3t8JAwKkNgXUZH", "question_id": 44, "answer1_id": "kqqPRaFqb3w9Ky9LGB3yKU", "answer2_id": "fpoXWHHdpv7XNBTS2NRY6d", "text": "8 7\nAssistant 1 provided a more detailed and step-by-step explanation of the calculations needed to estimate the number of balloons required to lift a house like in the movie \"Up.\" They considered the weight of the house, the lifting capacity of a single balloon, and the total lifting capacity needed. They also acknowledged the limitations of the calculation, such as the weight of the balloon material and the need for proper organization to lift the house evenly. Assistant 2, on the other hand, provided a less detailed explanation and used different assumptions for the weight of the house and the lifting power of the balloons. While both assistants concluded that it is not feasible to lift a house with balloons in real life, Assistant 1's response was more helpful, relevant, and accurate due to the detailed calculations and considerations provided.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dqbgKEeqNCuuTBhg5ovqfJ", "question_id": 45, "answer1_id": "946tQg8kS7GYPSm4qcV6Pt", "answer2_id": "GRXWdnzLLzmeZRU67PMTb7", "text": "8 6\nAssistant 1 provided a more helpful and detailed response by offering an estimate of the number of text messages sent globally in a minute, which was the main focus of the question. The assistant also explained the calculation process and acknowledged that the number is an estimate and may vary due to different factors. Assistant 2, on the other hand, focused on the difficulties of determining the exact number of text messages sent in a minute and did not provide an estimate or a calculation. While Assistant 2's response was relevant and accurate in terms of the challenges in tracking text messages, it did not directly address the user's request for an explanation of the reasoning behind an estimate.", "score": [8.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "DeEzTYEsG2B8nubmhaF4qc", "question_id": 46, "answer1_id": "cU3wut3Ta3ySbRHGxfwgjc", "answer2_id": "RTxk2jYsjYiiWMwcRjmfwu", "text": "8 6\nAssistant 1 provided a more detailed and step-by-step explanation of the estimation process, using a reasonable assumption of 2,000 words per person per day and explaining the limitations of the estimate. Assistant 2's estimate of 10,000 to 20,000 words per person per day seems too high, and the final estimate of 100 billion words per day is significantly lower than Assistant 1's estimate. Assistant 1's response is more helpful, relevant, and accurate, while Assistant 2's response is less detailed and less accurate.", "score": [8.0, 6.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nvDTZFCrXe6NSdcFqYWJDv", "question_id": 47, "answer1_id": "hQP784Ch2yq2b3BaXVBVX3", "answer2_id": "2J2Et6W8u2kXLTUgfYrPxe", "text": "8 7\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information in their responses. Assistant 1 gave a more precise estimation of the number of snowflakes by referring to the average snowfall in the United States and considering the size and shape of snowflakes. Assistant 2, while providing a detailed explanation of the factors affecting snowflake formation, did not provide a clear estimation of the number of snowflakes. Assistant 1's response was more focused on answering the question, while Assistant 2 provided more background information. Both responses were accurate and detailed, but Assistant 1's answer was more relevant to the question, which is why it received a higher score.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3S8Z26nA4rbyND5nJKqFWo", "question_id": 48, "answer1_id": "a92bStUFdq4LBcv3pa9y3Z", "answer2_id": "nSGoG9Z5g5VuCXgmQJJKrq", "text": "8 7\nAssistant 1 provided a more detailed response, including an estimation of the total number of books ever written and the average number of pages per book. Assistant 1 also acknowledged the limitations of the estimation and the fact that new books are being written and published every day. Assistant 2's response was also relevant and helpful, but it provided a lower estimate for the total number of books ever published and did not mention the limitations of the estimation as clearly as Assistant 1. Both assistants provided reasonable estimates, but Assistant 1's response was more comprehensive and informative.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "d79neqK6LiNCsSu7yiWNZY", "question_id": 49, "answer1_id": "a2QAcAm9wJeP2BpyWQnhot", "answer2_id": "ScqJdUq9n5bp9qPv5WPqG5", "text": "9 7\nAssistant 1 provided a clear and concise explanation, directly addressing the question and using the correct time frame of 3.8 billion years ago for the beginning of life on Earth. The answer was easy to follow and included a reasonable approximation of the number of orbits. Assistant 2, on the other hand, used the age of the Earth (4.5 billion years) instead of the beginning of life (3.8 billion years) for the calculation, which made the answer less accurate and relevant to the question. However, Assistant 2 did provide some additional information about the Earth's orbit, which added some value to the response. Overall, Assistant 1's response was more helpful, relevant, and accurate, while Assistant 2's response was less accurate but still provided some useful information.", "score": [9.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "KFEzPSHMSp88iZ4MkBEPQQ", "question_id": 50, "answer1_id": "CrmusnxbTtGXF2varfcUd4", "answer2_id": "GdLFr88pCwsGpfRBRQQkyh", "text": "8 7\nBoth Assistant 1 and Assistant 2 provided relevant and helpful answers to the question, explaining that it is impossible to determine the exact number of songs recorded throughout history. Assistant 1 provided a more detailed response, discussing the oldest known musical instrument, the Library of Congress's collection, and the importance of considering non-Western music. Assistant 2 also mentioned the invention of recording technology and the rise of digital music but provided fewer specific examples and details. Therefore, Assistant 1 receives a score of 8, and Assistant 2 receives a score of 7.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "MaeeytBPffy2qXeTWAgVb3", "question_id": 51, "answer1_id": "J9pZp6z2UUW7YcXgzUouqs", "answer2_id": "Sa7uNEbFGRFuXBAwau8sHS", "text": "8 7\nBoth Assistant 1 and Assistant 2 provided relevant and thoughtful answers to the question. Assistant 1's response was more focused on the potential impact of the internet on the Renaissance period, discussing how it could have affected intellectual exchange, collaboration, and the democratization of knowledge. Assistant 2, on the other hand, emphasized the technological limitations of the era and the differences between the Renaissance and the digital age. While both responses were helpful and accurate, Assistant 1's answer was slightly more detailed and directly addressed the question, which is why it receives a higher score of 8, while Assistant 2 receives a 7.", "score": [8.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7YnF7KE5b7DmdJVd2CWhPg", "question_id": 52, "answer1_id": "67bYUQb6zru8ofiub7uNUi", "answer2_id": "T6Ar3cAEw3AmVVQTcCPp8N", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a good overview of the potential consequences of the Aztecs repelling the Spanish conquistadors, touching on the impact on the Aztec empire, the indigenous people of the Americas, and the potential interest of other European nations. Assistant 2, however, provided a slightly more detailed response, discussing the Aztec civilization's strengths, the potential resistance to European diseases, the possible maintenance of cultural and religious practices, and the potential for alliances with other indigenous groups. Both answers were informative, but Assistant 2's response was more comprehensive, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bAfAwLcXniXktiqNsvDGFV", "question_id": 53, "answer1_id": "gAisnQTHWFLW8aa5fQPNJf", "answer2_id": "KiZQev5JEk2h6JYeQnFmtM", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided relevant, accurate, and detailed responses to the question. Assistant 1's response was slightly more helpful, as it touched upon the impact of the Black Death on the medical field, hygiene practices, and cultural landscape, which Assistant 2 did not mention. Assistant 2's response was still informative, discussing the potential effects on demography, economy, and society, but it lacked the additional context provided by Assistant 1.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8vUuWHUHuEn2SUrzRcoV6v", "question_id": 54, "answer1_id": "4ZJCbj7T8BGzNhDqz7NSF4", "answer2_id": "cYiyYKKXM3GXkrZHAbX83S", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 focused on the potential impact of Newton's work on biology and the possible consequences for the field of physics. Assistant 2, on the other hand, provided a more detailed overview of Newton's contributions to various fields and discussed the difficulty in predicting the specific contributions he might have made to biology. Assistant 2's response was more comprehensive and provided a broader context, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FCJoqPdZYVBmAfS3VjFCkC", "question_id": 55, "answer1_id": "c6ixri3qqLfSBBnwMkgYB7", "answer2_id": "PQmMUdAAcBsAWmWaTvdHSU", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 focused on the overall impact of the Beatles on music and society, while Assistant 2 went into more detail about the possible outcomes if the Beatles had never formed. Assistant 2's answer was more comprehensive, offering specific scenarios and covering various aspects of the music industry and culture, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bSZUNocHnjSSsxyUBMSUMu", "question_id": 56, "answer1_id": "c9AtDn7eeSYhtH854MQDDB", "answer2_id": "PorExChQ9VeYsPJptdgtsB", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 provided a slightly more detailed response, mentioning the impact of Turing's work on the development of computer technology and artificial intelligence, which adds value to the answer. Assistant 2, on the other hand, touched upon the possibility of alternative strategies and technologies being developed by the Allies, but did not go into as much detail as Assistant 1. Both assistants acknowledged the difficulty in predicting the exact outcome of the war without Turing's contributions, which is important to consider. Overall, both responses were informative and well-structured, but Assistant 1 provided a slightly more comprehensive answer.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "f3KTRaNot8TePqUPATMhRG", "question_id": 57, "answer1_id": "jYd2gg6MJH8hdqFSAJTaiR", "answer2_id": "249f6dSMwZRZVMmtxv6yDm", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 gave a clear overview of the consequences of not having the Suez Canal, touching on the impact on shipping routes, international trade, and the development of the region. Assistant 2, however, went into more detail about the longer and more treacherous route around the Cape of Good Hope, the impact on international trade, and the historical context of European colonization in Asia. Assistant 2 also mentioned the engineering and technological advancements required for the construction of the canal and its role in international conflicts. While both answers were informative, Assistant 2 provided a more comprehensive response, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "J5EKWhvGBjYM9kSttb7RBp", "question_id": 58, "answer1_id": "nZJ6LGJFegnHetutiAQtFm", "answer2_id": "nxa3m6kiAZwKgcMUBY8KYz", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. They both discussed the potential advancements in various fields such as science, technology, and governance that the Maya civilization could have made if they had not collapsed. Both assistants also mentioned the possible impact on the colonization of the Americas and the influence on neighboring civilizations. The level of detail in both responses is quite similar, and both assistants acknowledged the difficulty in predicting the exact outcomes. Therefore, both Assistant 1 and Assistant 2 receive a score of 9 for their overall performance.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "jEWD6eqwH9jscVgJKDNDUg", "question_id": 59, "answer1_id": "dmEgLyeYNcwBZWHBak6Lap", "answer2_id": "DXFvhjCaKqQiBsFDCkXGMT", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question. Assistant 1 mentioned the Vikings and the possibility of indigenous peoples from Asia migrating to the Americas, while Assistant 2 focused more on the potential consequences of Columbus not discovering the Americas. Assistant 2's response was slightly more detailed and provided a clearer picture of the potential impact on the world, which is why it received a higher score. However, both responses were informative and addressed the question well.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RG6quoUjP9RfEhB7SBeQWU", "question_id": 60, "answer1_id": "bkuECkuy7YiDUcj9oJjdrZ", "answer2_id": "BCUK5VcpZK26jMJ2UMgR3E", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the question. Assistant 1 went into more detail about the potential impact on van Gogh's personal life, such as his mental health and financial situation, as well as the broader implications for art history. Assistant 2 also discussed the potential impact on van Gogh's art and personal life but did not delve as deeply into the broader implications for art history. Both assistants provided a well-rounded response, but Assistant 1's answer was slightly more detailed and comprehensive, which is why it received a higher score.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "KKYYAMWpuxxqpPCxYRjr3s", "question_id": 61, "answer1_id": "2PRjT7j3V6487xZREfQfuD", "answer2_id": "JHTKBrLVTp7bv5zW2uwYVW", "text": "8 6\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to read.\n- The function `count_occurrences()` is modular and can be reused.\n- Proper error handling is implemented when opening the file.\n- The code handles case sensitivity by converting the text to lowercase.\n- Comments are clear and concise, explaining the code's logic and functionality.\n\nWeaknesses:\n- The code does not handle punctuation marks and special characters when comparing words.\n\nSuggestions for improvement:\n- To handle punctuation marks and special characters, you can add a function to remove them from the words before comparing them with the search word.\n\nAssistant 2:\nStrengths:\n- The code is simple and easy to understand.\n- The program uses the `find` function to search for the given word in each line.\n\nWeaknesses:\n- The code does not handle case sensitivity.\n- The code does not handle punctuation marks and special characters when comparing words.\n- The code does not count multiple occurrences of the word in the same line.\n- The code lacks comments explaining the logic and functionality.\n- The code is not modular, making it harder to reuse the counting functionality.\n\nSuggestions for improvement:\n- To handle case sensitivity, convert the text and search word to lowercase before comparing.\n- To handle punctuation marks and special characters, remove them from the words before comparing them with the search word.\n- Use a loop to count multiple occurrences of the word in the same line.\n- Add comments to explain the code's logic and functionality.\n- Consider creating a separate function for counting occurrences to improve modularity.", "score": [8.0, 6.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "Be9fbF7prWfkJiLcaRNjLP", "question_id": 62, "answer1_id": "jWyN8NTdVix6CUoqfbRqVx", "answer2_id": "jyzD2deb9JAffRhhkk37r5", "text": "8 4\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to understand.\n- The implementation correctly solves the problem using dynamic programming.\n- The comments are clear and concise, explaining the logic and functionality of the code.\n\nWeaknesses:\n- There are no comments explaining the initialization of the 2D array 'm'.\n- The code could be more efficient by not initializing the 2D array with all zeros and then updating it.\n\nSuggestions for improvement:\n- Add comments to explain the initialization of the 2D array 'm'.\n- Consider initializing the 2D array 'm' with the appropriate values instead of all zeros.\n\nAssistant 2:\nStrengths:\n- The code is structured and readable.\n- The implementation attempts to solve the problem using dynamic programming.\n\nWeaknesses:\n- The code does not correctly implement the given problem statement, as it returns the minimum edit distance instead of the longest common subsequence.\n- The comments are not as clear and concise as they could be, making it harder to understand the code's logic and functionality.\n- The code initializes the 2D array with `float('inf')`, which is unnecessary and inefficient.\n\nSuggestions for improvement:\n- Modify the code to correctly implement the longest common subsequence problem.\n- Improve the comments to better explain the code's logic and functionality.\n- Consider initializing the 2D array with more appropriate values instead of `float('inf')`.", "score": [8.0, 4.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "nD8SHmwjdEoLfodK7i8a9f", "question_id": 63, "answer1_id": "mx9G7gfKTCXCmNRaiMZQr9", "answer2_id": "jPGHEfp7FxhDyvRA6qEN3j", "text": "8 8\n\nBoth Assistant 1 and Assistant 2 provided a working solution for validating email addresses using regular expressions in Python. Their code is accurate, efficient, and easy to understand. Both solutions are very similar, with only minor differences in variable names and print statements.\n\nStrengths:\n- Both solutions correctly implement the given problem statement.\n- The code is accurate and efficient in both cases.\n- Both solutions include clear and concise comments that explain the code's logic and functionality.\n\nWeaknesses:\n- Both solutions use a simple regular expression that may not cover all edge cases for email validation. However, this is acceptable for a basic email validation implementation.\n\nSuggestions for improvement:\n- Both solutions could benefit from a more comprehensive regular expression that covers more edge cases for email validation. However, this is not a requirement for the given problem statement.\n- Assistant 2's solution could be improved by wrapping the email validation code in a function, similar to Assistant 1's solution. This would make the code more modular and reusable.", "score": [8.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "H9nS4Gsoe6nK5q2ebuhRk7", "question_id": 64, "answer1_id": "8LjpXk6Va5L2FBEwTU9YJ6", "answer2_id": "Ra9eTcAuNZPkPRnaHiQrak", "text": "8 4\n\nAssistant 1:\nStrengths:\n- The code is simple and easy to understand.\n- The dynamic programming approach is implemented correctly using a list to store Fibonacci numbers.\n- The code is efficient and will work well for large values of n.\n- The example usage is helpful for understanding the function's usage.\n\nWeaknesses:\n- The code could benefit from more comments explaining the logic and functionality.\n\nSuggestions for improvement:\n- Add comments to explain the logic and functionality of the code.\n\nAssistant 2:\nStrengths:\n- The code attempts to use dynamic programming with a memoization table.\n- The base case for n = 0 and n = 1 is handled correctly.\n\nWeaknesses:\n- The code does not actually use the memoization table, making it inefficient for large values of n.\n- The recursive calls to the function do not use the memoization table, leading to redundant calculations.\n- The code structure is more complex than necessary.\n\nSuggestions for improvement:\n- Use the memoization table to store and retrieve previously calculated Fibonacci numbers.\n- Simplify the code structure by removing the unnecessary memoization table initialization and using a list to store Fibonacci numbers, similar to Assistant 1's approach.", "score": [8.0, 4.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "AeohqWsGLDJmqX83xZmNPF", "question_id": 65, "answer1_id": "BLC8fhAUeahFfrjBRiL9tx", "answer2_id": "5mbEFLEnEx8Cm2wEF8WAbM", "text": "9 8.5\n\nBoth Assistant 1 and Assistant 2 provided correct and efficient implementations of the binary search algorithm. Their code is readable and well-structured. However, there are a few differences in their submissions.\n\nAssistant 1:\n- The code is clear and easy to understand.\n- The example usage provided is helpful for understanding how to use the function.\n- The variable names \"left\" and \"right\" are more descriptive than \"start\" and \"end\" used by Assistant 2.\n\nAssistant 2:\n- The code is also clear and easy to understand.\n- The explanation of the algorithm is more detailed, which is helpful for understanding how the algorithm works.\n- The variable names \"start\" and \"end\" are less descriptive than \"left\" and \"right\" used by Assistant 1.\n\nSuggestions for improvement:\n- Assistant 1 could provide a more detailed explanation of the algorithm, similar to Assistant 2.\n- Assistant 2 could use more descriptive variable names, such as \"left\" and \"right\", as used by Assistant 1.\n- Both assistants could add comments within the code to explain the logic and functionality of each step.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "PhJVYMN4t8GbM7hsxJm47V", "question_id": 66, "answer1_id": "NAH245JGvMWF24FWZJagYp", "answer2_id": "KLbBzjbuwq4agAESQ9Wfpq", "text": "8 5\n\nAssistant 1:\nStrengths:\n- The code is well-structured and easy to read.\n- The implementation is correct and efficient.\n- The comments are helpful and explain the code's logic and functionality.\n- The code adheres to proper coding standards and best practices.\n\nWeaknesses:\n- The code could benefit from a few more comments, especially for the `enqueue` and `dequeue` methods.\n\nSuggestions for improvement:\n- Add more comments to explain the logic behind the `enqueue` and `dequeue` methods.\n\nAssistant 2:\nStrengths:\n- The code is readable and has a clear structure.\n- The `peek` and `is_empty` methods are implemented correctly.\n\nWeaknesses:\n- The implementation of the `enqueue` and `dequeue` methods is incorrect, as it does not use two stacks to implement the queue.\n- The code does not adhere to proper coding standards and best practices, as it uses a mix of Python and C++ syntax.\n- The comments are not as helpful as they could be, and there are no comments for the `peek` and `is_empty` methods.\n\nSuggestions for improvement:\n- Correct the implementation of the `enqueue` and `dequeue` methods to use two stacks.\n- Ensure that the code uses consistent syntax, preferably Python syntax.\n- Add more comments to explain the logic behind the `peek` and `is_empty` methods.", "score": [8.0, 5.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "gJz8gj8qVa2Co9HSotcUUQ", "question_id": 67, "answer1_id": "NKXTwB3WtfFcmQZbTxBP5o", "answer2_id": "ganAwetd9VTzEAiTtsskzX", "text": "7 5\n\nAssistant 1:\nStrengths:\n- The code is easy to read and understand.\n- The function works correctly for the given problem statement.\n- The example provided is helpful for understanding the code's functionality.\n\nWeaknesses:\n- The code uses an extra data structure (the 'common' list) which is against the problem statement's requirement.\n- The nested loops can be inefficient for large arrays.\n\nSuggestions for improvement:\n- To avoid using an extra data structure, you can print the common elements directly instead of appending them to a list.\n- Consider using a more efficient algorithm, such as sorting the arrays and using two pointers to find common elements.\n\nAssistant 2:\nStrengths:\n- The code is easy to read and understand.\n- The example provided is helpful for understanding the code's functionality.\n\nWeaknesses:\n- The code uses an extra data structure (the 'result' list) which is against the problem statement's requirement.\n- The nested loops can be inefficient for large arrays.\n- The code does not handle duplicate elements in the arrays, resulting in duplicate common elements in the output.\n\nSuggestions for improvement:\n- To avoid using an extra data structure, you can print the common elements directly instead of appending them to a list.\n- Consider using a more efficient algorithm, such as sorting the arrays and using two pointers to find common elements.\n- Add a check to ensure that duplicate common elements are not printed.", "score": [7.0, 5.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "HnmQ6NkCSS8sumGzBnwkgo", "question_id": 68, "answer1_id": "5P7wPyrNwBrHNdJNrETS5h", "answer2_id": "LG6szQ3cqNRzUjXtZtXG94", "text": "First, I will solve the problem independently:\n\nf(x) = 5x^3 - 2x + 3\n\nf(2) = 5(2)^3 - 2(2) + 3\nf(2) = 5(8) - 4 + 3\nf(2) = 40 - 4 + 3\nf(2) = 39\n\nNow, I will evaluate the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nf(2) = 5(2)^3 - 2(2) + 3\nf(2) = 5(8) - 4 + 3\nf(2) = 40 - 1\nf(2) = 39\n\nAssistant 2:\nf(2) = 5(2)^3 - 2(2) + 3\nf(2) = 5 * 8 - 2 + 3\nf(2) = 41 - 2 + 3\nf(2) = 40\n\nAssistant 1 has the correct answer and followed the correct steps. Assistant 2 made a mistake in the second step, where they incorrectly calculated 5 * 8 - 2 as 41 instead of 40. This led to an incorrect final answer of 40.\n\nScores:\nAssistant 1: 10\nAssistant 2: 6\n\nAssistant 1 performed better in this case.", "score": [10, 6], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "c5XdU8rAprsY8V5j7CGAe2", "question_id": 69, "answer1_id": "UmnL4WoucBduFpX4jptkNU", "answer2_id": "LHMnXGbSZubeZJcr5fgrte", "text": "First, I will solve the problem independently:\n\n3x + 10 = 5(x - 2)\n\n3x + 10 = 5x - 10  (use the distributive property)\n\n-2x = -20 (subtract 3x from both sides)\n\nx = 10 (divide both sides by -2)\n\nTherefore, x = 10.\n\nNow, I will evaluate the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe steps provided by Assistant 1 are correct, and the final answer is x = 10.\n\nAssistant 2:\nThe steps provided by Assistant 2 are incorrect. The assistant made an error in step 2 by subtracting 5x from both sides, which resulted in an incorrect equation. The rest of the steps are also incorrect due to this initial error.\n\nScores:\nAssistant 1: 10\nAssistant 2: 1\n\nAssistant 1 performed better and provided the correct solution to the problem.", "score": [10, 1], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "3T6zyEyabtCSmED9WgtDSm", "question_id": 70, "answer1_id": "Hymm673GkY5tXNaTFeNUBZ", "answer2_id": "TGENVWL5iAYi4NCf5jAzKy", "text": "First, I will solve the problem independently:\n\nThe distance formula is given by:\n\ndistance = sqrt((x2 - x1)^2 + (y2 - y1)^2)\n\nPlugging in the coordinates of the given endpoints, we get:\n\ndistance = sqrt((10 - 2)^2 + (4 - (-2))^2)\ndistance = sqrt(8^2 + 6^2)\ndistance = sqrt(64 + 36)\ndistance = sqrt(100)\ndistance = 10\n\nNow, I will examine the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe steps followed by Assistant 1 are correct, and the final answer is also correct.\n\nAssistant 2:\nThe steps followed by Assistant 2 are incorrect. The mistake occurs in the calculation of the differences between the coordinates:\n\nIncorrect: sqrt((10 - 2)^2 + (4 - (-2))^2) = sqrt(18^2 + 4^2)\nCorrect: sqrt((10 - 2)^2 + (4 - (-2))^2) = sqrt(8^2 + 6^2)\n\nThe final answer provided by Assistant 2 is incorrect.\n\nScores:\nAssistant 1: 10\nAssistant 2: 4\n\nAssistant 1 performed better in this case, as they followed the correct steps and provided the correct answer. Assistant 2 made a calculation mistake in the distance formula, leading to an incorrect answer.", "score": [10, 4], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "2FNQ97Hnop7zu525REHpPh", "question_id": 71, "answer1_id": "PUzddJ35E3WsM7BDQ9E59i", "answer2_id": "kc9oytarvqyNVVfGZz93EL", "text": "9 9.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's request for a formal email proposing a joint venture. Both responses included appropriate greetings, introductions, and explanations of the proposed partnership. Assistant 2's response was slightly more detailed, including the proposal of establishing a new jointly-owned company and suggesting a meeting to discuss the proposal further. However, both responses were of high quality and would be useful to the user.", "score": [9.0, 9.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "jbBS3mJXgYxGnnjhamALro", "question_id": 72, "answer1_id": "6Q72hZCtDkgq379yhdmN5N", "answer2_id": "NxstpvVEnGMny3jECtvrw2", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate resignation letter samples. They both addressed the user's request to leave on good terms and express gratitude for the opportunities provided. The level of detail in both responses is appropriate, and both letters are well-structured and professionally written. It is difficult to differentiate between the two responses in terms of quality, as both are excellent examples of resignation letters.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "TzDXKHsob9xTKFdvCMvyVn", "question_id": 73, "answer1_id": "ReXnHy9C8SwcYPAep6gvJg", "answer2_id": "XN9NBpeQJiQqGT5UYkys7N", "text": "9 9\n\nBoth Assistant 1 and Assistant 2 provided well-structured, detailed, and relevant formal letters of recommendation for a student applying to a prestigious graduate program in computer science. Both letters included essential elements such as the sender's and recipient's information, a clear introduction, a thorough explanation of the student's academic achievements, and a strong conclusion. Both letters were accurate, relevant, and helpful in addressing the user's request. Therefore, I believe both assistants deserve a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "X2tMhW8rQLckXYViCjz5ZM", "question_id": 74, "answer1_id": "cKk5zZe8yYY4JH3kr5pGXG", "answer2_id": "3qMqAZcfHUVUTEJnMXT8Pw", "text": "8 9\nBoth assistants provided relevant and detailed responses to the user's request. Assistant 1's email was well-structured and included benefits and features of the software solution, as well as a special introductory offer. However, Assistant 2's response was slightly more precise, as it mentioned the target audience and market, and provided a clearer list of key features. Additionally, Assistant 2's email was more personalized by mentioning the company name and offering a demo. Both emails were helpful and accurate, but Assistant 2's response was more comprehensive and tailored to the user's request.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "EcpwAYWnZD8YF8kvKNAwBy", "question_id": 75, "answer1_id": "c5rwA3cPjytSGcn7H8dZ6Q", "answer2_id": "hMXjd228Mc8pnc7zgd2frG", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate responses to the user's question. Assistant 1's response was slightly more detailed, including information about the cause of the delay, the steps taken to resolve the issue, and measures to prevent future delays. Assistant 2's response was also well-written and addressed the issue, but it lacked the specific details about the cause and resolution that Assistant 1 provided. Overall, both responses were of high quality, but Assistant 1's response was more comprehensive and informative.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LQhfYPFfLcwFPAb5KCJzmS", "question_id": 76, "answer1_id": "XZGPtBo86KfF9REZ36s2X5", "answer2_id": "gUEd9nNepqy8nsfbypscHx", "text": "9 8\nAssistant 1 provided a well-structured script for a YouTube video, including visual cues and narration, which made it more engaging and informative. The response covered the origins of jazz, its evolution, and its cultural significance, as well as mentioning jazz festivals and clubs. Assistant 2 also provided a good response, discussing the history and cultural significance of jazz, its influence on other genres, and its role in the civil rights movement. However, Assistant 2's response was less engaging and lacked the visual cues and structure of a video script, which is why Assistant 1 received a higher score. Both responses were relevant, accurate, and detailed, but Assistant 1's response was better suited for a YouTube video script.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "M8dSLebM2csXbgXd5r59X2", "question_id": 77, "answer1_id": "DRncHCsdGji756efDhacUT", "answer2_id": "nZZyn3SAvU8T2BVR73LMHC", "text": "9 9\nBoth Assistant 1 and Assistant 2 provided engaging and well-written travel blog posts about a recent trip to Hawaii. They both highlighted cultural experiences, such as attending a luau and watching hula performances, and must-see attractions like Waikiki Beach and Haleakala National Park. The level of detail and accuracy in both responses was excellent, and both assistants effectively conveyed the excitement and beauty of a trip to Hawaii. It is difficult to choose one over the other, as both responses were equally helpful, relevant, and engaging. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "APtBcAMY9tVW7nyE4g2ZAA", "question_id": 78, "answer1_id": "Y5rCQHHDA6WNfhRcB6QboG", "answer2_id": "YM4qjUM5VfmWnzM3HXRB4G", "text": "9 8.5\nBoth Assistant 1 and Assistant 2 provided captivating movie reviews for a recently released science fiction film. Assistant 1's response was slightly more detailed and engaging, with a more in-depth discussion of the plot, characters, and special effects. The review also mentioned the character development and growth throughout the film. Assistant 2's response was also well-written and engaging, but it was slightly less detailed in comparison. Both reviews were relevant, accurate, and helpful, but Assistant 1's response had a slight edge in terms of depth and detail.", "score": [9.0, 8.5], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Z5Rhru7as3Yhk8ZXjzfPe4", "question_id": 79, "answer1_id": "Lea4wh5n6GsEkBnKsntN64", "answer2_id": "WcbzNkzu5q66QkK92NxmGB", "text": "9 8\nAssistant 1 provided a well-structured podcast script with clear sections, covering the music industry before streaming, the rise of streaming platforms, their impact on artists and the industry, and the future of music streaming. Assistant 2 also provided a good podcast script, but it was less detailed and structured compared to Assistant 1. Assistant 1's response was more comprehensive and organized, making it easier to follow and understand the flow of the podcast.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "kw3p4X9zBGtVDfPDRoJwRE", "question_id": 80, "answer1_id": "gdLxzcypTeuD6ToC6HWnXh", "answer2_id": "Tfm4f2mbpKJsa8MT6exs8D", "text": "9 9\n\nBoth Assistant 1 and Assistant 2 provided well-written and detailed symphony concert reviews that discussed the orchestra's performance and the overall audience experience. Both reviews were engaging and captured the essence of a symphony concert, making it difficult to differentiate between the two in terms of helpfulness, relevance, accuracy, and level of detail. Therefore, both assistants receive a score of 9.", "score": [9.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
diff --git a/llava/eval/table/review/review_llama-13b_vicuna-13b.jsonl b/llava/eval/table/review/review_llama-13b_vicuna-13b.jsonl
deleted file mode 100644
index fc29a0b6..00000000
--- a/llava/eval/table/review/review_llama-13b_vicuna-13b.jsonl
+++ /dev/null
@@ -1,80 +0,0 @@
-{"review_id": "WFp5i5yjjFethrgugKTDmX", "question_id": 1, "answer1_id": "J3UA6eGXGyFeUGqGpP3g34", "answer2_id": "cV4zXygaNP6CXEsgdHMEqz", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about improving time management skills. Assistant 1 offered a more detailed list of strategies, with 16 points, while Assistant 2 provided a slightly shorter list with 7 points. Both assistants covered similar topics, but Assistant 1 included additional suggestions such as managing stress, getting enough sleep, and striking a balance. Assistant 2's answer was well-structured and concise, but Assistant 1's response was more comprehensive, which is why Assistant 1 receives a 9 and Assistant 2 receives an 8.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "VFr7STcg4Yss7qHrgKZtJ2", "question_id": 2, "answer1_id": "jZVDNTtFnNNd6vgkGjzAGa", "answer2_id": "3zpPUeoVsPWXtKMV7cAhZ6", "text": "9 8\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate answers to the question about effective ways to deal with stress. Assistant 1 provided a more detailed response with 10 suggestions, while Assistant 2 provided 8 suggestions. Both assistants covered similar points, such as exercise, relaxation techniques, social support, and seeking professional help. Assistant 1, however, included additional suggestions like improving time management and developing a support network, which added more value to their response. Therefore, Assistant 1 receives a 9, and Assistant 2 receives an 8.", "score": [9.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "4cn29wYPaXYEyypZC5UBFh", "question_id": 3, "answer1_id": "NbSxRXf8Yuvjr7HC6AvrPw", "answer2_id": "6xpiZJE4vxxGqjRogPfBk7", "text": "4 8\nAssistant 1's response is repetitive and contains several incorrect statements, making it less helpful and accurate. Assistant 2's response is more concise, accurate, and relevant, providing a clear comparison between Python and JavaScript, which makes it more helpful and informative.", "score": [4.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "QfbSxZhq2a3unPfDqPXWzm", "question_id": 4, "answer1_id": "E9zrPDdncYvAebEaxiPXfx", "answer2_id": "abitTVu2Dz8rdMxyThvFyJ", "text": "8 9\nBoth assistants provided helpful and relevant tips for increasing productivity while working from home. Assistant 1's response was well-organized and concise, covering important aspects such as setting up a workspace, blocking distractions, taking breaks, avoiding multitasking, having an organized schedule, and setting goals. Assistant 2's response was more detailed and covered additional points such as getting dressed for work, scheduling meetings, and staying connected with colleagues and loved ones. While both responses were accurate and useful, Assistant 2's answer was more comprehensive and provided a slightly better level of detail, which is why it received a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ZhokYAotAEkh6QQMkjJeDi", "question_id": 5, "answer1_id": "36TYpADDHAfRvtvKitfSXR", "answer2_id": "UMZod8JaWia9KB2EVXdYrF", "text": "7 9\nAssistant 1 provided a brief and simple explanation of quantum computing, mentioning the use of qubits and the advantage of quantum computers in solving certain problems more efficiently. However, the response lacked depth and details. Assistant 2, on the other hand, provided a more comprehensive explanation, covering the basics of quantum computing, the use of qubits, and the different approaches to building quantum computers. The response also mentioned potential applications of quantum computing, making it more informative and helpful overall.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RWRpNF3RZiurzyAKHRggx4", "question_id": 6, "answer1_id": "8vtLLYRM352mZ8PXXbwzBT", "answer2_id": "WXRCqF34f4UUJzd3xbgAqT", "text": "8 10\nAssistant 1 provided a brief and accurate response, but it lacked details and only mentioned the difference in amino acid profiles. Assistant 2, on the other hand, provided a more comprehensive answer, discussing nutritional value, absorption, sustainability, health effects, ethical considerations, and cost. This made Assistant 2's response more helpful and informative, earning it a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ngDhYdQ3bPkbvctvpoJoxJ", "question_id": 7, "answer1_id": "mrCniyWYvAtxWhgMdXq2BG", "answer2_id": "JREJbG5ZKXCNMoWoiJQxbC", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information on how to develop critical thinking skills. Assistant 1 offered four suggestions, while Assistant 2 provided a more detailed list of ten tips. Both answers were accurate and relevant to the question. However, Assistant 2's response was more comprehensive and provided a wider range of strategies, which is why it receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "efxZ8mmetiunsqv5AVCcKD", "question_id": 8, "answer1_id": "S8fmdbvpvbgTUyDU9V3M3N", "answer2_id": "mmVwmX6TGJ2Y72gCNac4EQ", "text": "7 9\nAssistant 1 provided a relevant and accurate response, but it was limited in detail and only focused on one major challenge, which is the lack of skilled teachers. On the other hand, Assistant 2 provided a more comprehensive and detailed answer, covering a wide range of challenges faced by the education sector today, such as access and affordability, quality and standards, technology integration, curriculum development, teacher training and development, accountability and assessment, equity and inclusion, and sustainability. This makes Assistant 2's response more helpful and informative for the user.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "PmxhqvF7E6Z6YmQBHW8SB6", "question_id": 9, "answer1_id": "KmuNjvNKRyaFwaBZTLArcG", "answer2_id": "DMTZyzd4rRAFV43xtBJ9ns", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 listed eight factors influencing consumer behavior, while Assistant 2 listed six factors. Both answers covered psychological, social, economic, and marketing factors. However, Assistant 2's response was more concise and better organized, making it easier to understand. Assistant 1's answer had some redundancy, such as mentioning reference groups and family twice in different categories. Overall, Assistant 2 provided a slightly better response due to its clarity and organization.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "9iqa3D5izqdMoruHKiihWk", "question_id": 10, "answer1_id": "HNNT9k5htiMeRwNSD6dcz4", "answer2_id": "dETAsj4xHnUCSTkZezz8aM", "text": "7 9\nAssistant 1 provided a list of conflict resolution strategies but did not elaborate on them, making the answer less detailed and informative. Assistant 2, on the other hand, provided a more comprehensive list of strategies with clear explanations, making it more helpful, relevant, and accurate. Therefore, Assistant 2 receives a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7e22Cc8mBiHN9uszBKvh8A", "question_id": 11, "answer1_id": "ChXjhDDikxU9FV3CADs6Ym", "answer2_id": "C8ksZxg3LshMUWiLxPanbt", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information regarding the implications of using single-use plastic bottles versus reusable bottles on the environment and human health. Assistant 1 focused on the environmental problems caused by single-use plastic bottles and briefly mentioned the health risks associated with the chemicals used in their manufacturing. Assistant 2 provided a more comprehensive response, covering both environmental and health impacts in greater detail, and also mentioned the benefits of using reusable bottles. Assistant 2's response was more structured and provided a clearer overview of the topic, which is why it received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oJVZyBDNFhHuRPkUqf3qEC", "question_id": 12, "answer1_id": "5wsPnN3VmmSkahgugFNo7u", "answer2_id": "NeHhRc5P5uAU8eWSJBRkhG", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information in their responses. Assistant 1 focused on the needs of people with disabilities, mentioning accessible stops, routes, vehicles, ramps, elevators, signage, facilities, and information. However, Assistant 2 provided a more comprehensive response, addressing not only accessibility for people with disabilities but also considering seniors and those without personal vehicles. Assistant 2 also mentioned route design, scheduling, customer service, information and communication, safety, and affordability, which makes their response more detailed and well-rounded. Therefore, Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dKQtFqgZw9Uk7GsD9oCpJF", "question_id": 13, "answer1_id": "NRGZGnU2sPN3ShMe9C3fMn", "answer2_id": "KAJ7UVwu8oCKyxZj9j82pm", "text": "7 9\nAssistant 1 provided a response that focused solely on fiscal policy and did not address monetary policy, which is an important aspect of the question. The response also incorrectly categorized interest rate changes as fiscal policy, when they are actually part of monetary policy. Assistant 2 provided a more comprehensive answer, covering both fiscal and monetary policies and their respective examples. Assistant 2 also mentioned the potential consequences of using these policies, which adds to the quality of the answer. Therefore, Assistant 2 receives a higher score for a more accurate and complete response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "nMw34PnYdGMNbLEiNwJ8mb", "question_id": 14, "answer1_id": "inKimHkWsXShQBTRmxr5Yg", "answer2_id": "NnWfaeRe8PmitgmV4u5fY8", "text": "8 10\nAssistant 1 provided a relevant and accurate response, but it was quite brief and lacked specific examples. Assistant 2, on the other hand, offered a more detailed and comprehensive answer, including specific ways in which language and cultural barriers can affect communication and relationships in multicultural societies. This made Assistant 2's response more helpful and informative, earning it a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "iDS9f8GfKKavmsTwE4qMLp", "question_id": 15, "answer1_id": "H8aKtWwf8m6Lgxc2YyR2yf", "answer2_id": "WiGpqKRRwWonwgU95piNNc", "text": "7 9\nAssistant 1 provided a list of applications of AI in healthcare, but did not specifically describe a scenario as requested. The answer was relevant and accurate, but not as detailed as it could have been. Assistant 2, on the other hand, provided a more comprehensive response, describing a scenario where AI is used to analyze patient data, automate routine tasks, monitor patients remotely, develop personalized treatment plans, and analyze medical research. The answer was more detailed, relevant, and accurate, making it a better response to the question.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7QUNx3aR6g6LHSuNoEmcbn", "question_id": 16, "answer1_id": "PafVwxMsjSkYUETiVBgxTU", "answer2_id": "iangiZeex5ndxAXnWMrxBW", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the CRISPR-Cas9 gene editing technology, its potential applications, and ethical implications. Assistant 1 gave a brief overview of the process, while Assistant 2 provided a more detailed explanation, including the two repair mechanisms, HDR and NHR. Assistant 2 also discussed a wider range of potential applications and ethical concerns, making their response more comprehensive and informative. Therefore, Assistant 2 receives a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "gTqC8kz9Kbnz2LkELTzSHx", "question_id": 17, "answer1_id": "dmDUAfTP4aERJqqSeDBybu", "answer2_id": "XnMRLphzYQX4QRNht7tbui", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about vaccinations and herd immunity. Assistant 1 gave a brief explanation of how vaccinations work and what herd immunity is, which is useful for a quick understanding. However, Assistant 2 provided a more detailed response, explaining the concept of herd immunity in greater depth, including the critical threshold needed for herd immunity to be effective. Assistant 2 also emphasized the importance of vaccinations for both individuals and the community, which adds value to the response. Overall, Assistant 2's answer was more comprehensive and informative, earning a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bdRgXZzZVxLiweFqFnJipG", "question_id": 18, "answer1_id": "8KGSSqbLqVdSZMEN9oCv5R", "answer2_id": "HZc37bwy646mRzbqSsDAob", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful, relevant, and accurate information about the influence of social media platforms on news consumption and the spread of misinformation. Assistant 1 focused on the speed of news access, the sharing of news within communities, the incentive for sensationalist content, and the sharing of controversial news. Assistant 2, on the other hand, provided a more detailed response, discussing personalization, virality, amplification, filter bubbles, confirmation bias, and the lack of fact-checking. Assistant 2's response was more comprehensive and covered a wider range of factors, which is why it received a slightly higher score. Both assistants, however, provided valuable insights into the topic.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "AAvjdtgEkKA35V7AWttJXh", "question_id": 19, "answer1_id": "HbnJXJpPfaM2iX3ek4Epvy", "answer2_id": "iJrMatLrMdJyyqMx9uJ45a", "text": "6 9\nAssistant 1's response started by listing a large number of interventions but failed to address the main question about how cultural, social, and economic factors influence food choices. Assistant 2, on the other hand, provided a more relevant and detailed response, discussing the impact of cultural, social, and economic factors on food choices and how this knowledge can be used to promote healthier diets. Assistant 2's response was more helpful, accurate, and relevant to the question, which is why it received a higher score.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "8h5XnzGfNZSfqcnRBb5ioB", "question_id": 20, "answer1_id": "mx8Abfz5PtDcn6jgCA8zhM", "answer2_id": "oVEHqDnDTEADZSFfKgFTZd", "text": "6 9\nAssistant 1's response started off well by explaining the basic concept of natural selection but then veered off into discussing population size and genetic drift, which are not directly related to the process of natural selection. The response was not as focused and detailed as it should have been. Assistant 2 provided a much clearer and more comprehensive explanation of natural selection, detailing the steps involved in the process and how it contributes to the evolution and adaptation of species. The response was relevant, accurate, and detailed, making it more helpful overall.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "CAksX9yGXbLd2gHv3Pg97a", "question_id": 21, "answer1_id": "NuS9PUGkJG2pHscArvfyeF", "answer2_id": "WLAj4u59bj2oEXzahF79ek", "text": "7 9\nAssistant 1 provided a brief and relevant response, but it lacked the detail and formality expected in a medieval knight's introduction. Assistant 2, on the other hand, offered a more comprehensive and well-structured response, including a respectful speech that showcased the knight's humility, loyalty, and dedication to the kingdom. This response was more fitting for the context of a royal banquet and demonstrated a better understanding of the medieval knight's role and etiquette.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "SKHF8XUbwstcDNeHXoGHFk", "question_id": 22, "answer1_id": "SPjzirzbzo3UJ8BHXSgY9y", "answer2_id": "fJPnM2XcRveW2zR4DDaeTb", "text": "8 9\nAssistant 1 provided a relevant and helpful response, suggesting that the crew would be motivated by the promise of treasure and the potential to become the best pirate ship in the world. However, the answer could have been more detailed and engaging. Assistant 2, on the other hand, provided a more immersive and detailed response, with a well-crafted motivational speech that touched on the crew's sense of adventure, determination, and camaraderie. The speech was engaging and relevant to the question, and the disclaimer at the beginning was a responsible touch.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oKLigaxgtBdQkCUrYuNwNt", "question_id": 23, "answer1_id": "JkeeC4MZVwybPU8DSbusnT", "answer2_id": "fY4Xed6veWpcKfj5krZz8m", "text": "8 10\nAssistant 1 provided a relevant and accurate explanation of a soliloquy and gave an example from Romeo and Juliet, but did not create a new soliloquy as requested. Assistant 2, on the other hand, understood the request and crafted an original soliloquy in a Shakespearean style, demonstrating creativity and relevance to the question. Both assistants were helpful and provided accurate information, but Assistant 2 went above and beyond in addressing the user's request, which is why it receives a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "emz8g7aZzAyeWf6GJmVcbQ", "question_id": 24, "answer1_id": "UcfKSXuzBPkZzLHsH4EwQz", "answer2_id": "LqFJA2JQbBXP77nkYjtrZK", "text": "4 9\nAssistant 1's response was not relevant to the question, as it focused on the AI's own origin story rather than providing an explanation of a superhero's origin story for a curious child. Assistant 2, on the other hand, provided a relevant and engaging response that explained the origin story of a superhero in a manner that a child could understand. The response was detailed, accurate, and demonstrated empathy and compassion, which are important qualities for a superhero.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "CY8hhNosMQqsLCJi8PwE8J", "question_id": 25, "answer1_id": "cEGdo69FwL2Y3teYkMk9Gp", "answer2_id": "WxCKcRstJPXTXnqEzSX3Hg", "text": "8 9\nBoth assistants provided helpful, relevant, and accurate information about the technological advancements from the year 3000. Assistant 1 focused on five advancements, while Assistant 2 provided a more detailed response with six advancements. Assistant 2's response was more comprehensive, covering a wider range of technologies and providing more context for each advancement. This is why Assistant 2 received a slightly higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "idohdDHu3UbYHVem9vpHzG", "question_id": 26, "answer1_id": "W28UtVDZTcWwa8TmeruRn4", "answer2_id": "dgPJ7DvtFHD7mxtRwzM3vW", "text": "6 9\nAssistant 1's response was confusing and contained several inconsistencies, making it difficult to understand the winning play. Assistant 2, on the other hand, provided a clear and engaging description of the winning play, capturing the excitement and drama of the final moments of a championship game. Assistant 2's response was more helpful, relevant, accurate, and detailed, earning a higher score.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "BDya8NbCTo9jFshoiP5yuF", "question_id": 27, "answer1_id": "j65UyAfRdAcWBAEinXJzBK", "answer2_id": "ADX83sWvjJkewJX6JmYUzQ", "text": "7 10\nAssistant 1 provided a brief and general description of a chicken fried rice dish, but it lacked the depth and detail expected from a world-famous chef describing their signature dish. Assistant 2, on the other hand, delivered a more engaging, detailed, and eloquent response that showcased the chef's passion, creativity, and technique. The description of flavors, textures, and presentation in Assistant 2's response was more immersive and convincing, making it a better overall performance.", "score": [7.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FmKtMUW7zJovkwFfoqbD5T", "question_id": 28, "answer1_id": "VWuywPvFPK42ZxmHpmQ27y", "answer2_id": "ihNG3rwsrt95NDhCAFeSDR", "text": "6 9\nAssistant 1's response was somewhat helpful but lacked accuracy and detail in describing the view from the summit of Mount Everest. The description of green trees and bushes is not accurate, as the summit is covered in snow and ice. Assistant 2 provided a more accurate and detailed response, describing the emotions a climber might feel and the breathtaking view from the summit. Additionally, Assistant 2 mentioned the importance of proper training, experience, and equipment, which adds value to the response.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "TeDK6wCoeXdgfQt8y4GSv4", "question_id": 29, "answer1_id": "AUWSLTAvGV5sP9qLsVyzjT", "answer2_id": "Gmhqf3z4LvVfwPNFJ89BKd", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided relevant and detailed responses to the user's question. Assistant 1 focused more on the environmental challenges of living on Mars, such as temperature, gravity, and the thin atmosphere. They also discussed the challenges of isolation, boredom, and stress. Assistant 2 provided a more structured response, outlining a daily routine and addressing the challenges faced in each aspect of the day. Both assistants mentioned the importance of food and communication with Earth. Assistant 2's response was slightly more helpful as it provided a clearer picture of daily life on Mars and addressed the mental health aspect of living on another planet.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "4PncefRDDBxughK6btkF8X", "question_id": 30, "answer1_id": "GZtEdKLxmUUbenH4XRyLkE", "answer2_id": "gSwkKJCn6qDnNZond2xVJ3", "text": "4 9\nAssistant 1's response was not helpful or relevant to the question, as it did not provide any description of a character in a post-apocalyptic world, their survival, or the allies they encounter. Instead, it provided instructions on how to answer the question, which was not what the user asked for. Assistant 2, on the other hand, provided a detailed and relevant response that described a character's survival in a post-apocalyptic world, their alliances, and their encounters with other survivors. The response was engaging and painted a vivid picture of the character's experiences, making it a much better answer to the user's question.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "XyNjnkLJfqYKP6oa98qJ3t", "question_id": 31, "answer1_id": "kba2Xpb75rUfnskZSzrBas", "answer2_id": "8RaBeMjxx2bCp2GKWv7YiP", "text": "6 9\nAssistant 1's response was less detailed and less accurate, as it only mentioned the crowd as an indicator of a restaurant's popularity among locals or tourists. Assistant 2, on the other hand, provided a more comprehensive list of factors to consider, such as crowd composition, reservations, parking, pricing, and menu offerings. Additionally, Assistant 2 explained the usefulness of this information in terms of personal preferences, time of year, recommendations, and price range. Overall, Assistant 2's response was more helpful, relevant, accurate, and detailed than Assistant 1's response.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "TsVnQN38oyCHbFPm54rGsq", "question_id": 32, "answer1_id": "RCaptsMBYXseVJgjJyyvUh", "answer2_id": "C65PZkmAfFfWRs4bPhyKqg", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 listed six subtle clues, while Assistant 2 provided six as well but with more detailed explanations. Assistant 2's answer also included more diverse clues, such as interrupting or talking over others and aggressively defending their position. Assistant 1's answer had some redundancy, such as points 3 and 6, which could have been combined. Overall, Assistant 2's response was more comprehensive and detailed, which is why it receives a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RWooMSFE499SFJvkcdhvtG", "question_id": 33, "answer1_id": "2CeidEr7Cjj6YgcuaiPgnN", "answer2_id": "4so4HTEjgDZKTqNAgkHHQX", "text": "7 9\nAssistant 1 provided a relevant answer, but it was not as detailed and comprehensive as Assistant 2's response. Assistant 2 listed several reasons why someone might choose to use a paper map or ask for directions instead of relying on a GPS device or smartphone app, covering aspects such as preference, planning, reliability, safety, and privacy. This made Assistant 2's answer more informative and helpful compared to Assistant 1's response, which only mentioned the general idea of seeing the big picture.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "94Qr8aVXJeB5mG2o9reTVa", "question_id": 34, "answer1_id": "kpStLLNWNkGHyRFdKsmDds", "answer2_id": "FCYaiexEzdoLFPAwvTgDDm", "text": "8 10\nAssistant 1 provided a relevant answer, but it was less detailed and focused mainly on the person's actions and tone of voice. Assistant 2, on the other hand, provided a more comprehensive response, listing five specific clues to look for when determining if someone is genuinely interested in a conversation or simply being polite. This answer was more helpful, accurate, and detailed, which is why Assistant 2 receives a higher score.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bf6Gu6BoDbhVVYH7BerRFC", "question_id": 35, "answer1_id": "PFtMQp4X4MrMYkEa8c8CKG", "answer2_id": "76EPQDh4ZNxBMGqED9LEFi", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant answers to the question. Assistant 1 focused on supporting the local community, flexibility, and eco-friendliness, while Assistant 2 provided a more detailed list of reasons, including supporting the local community, personal connection, unique products, sustainability, customer service, and prestige. Assistant 2's answer was more comprehensive and covered a wider range of reasons, which is why it received a slightly higher score. However, both answers were accurate and relevant to the question.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "jFqudZJFnj8DsYtWepyr8w", "question_id": 36, "answer1_id": "4JwjhR5QVsdYgQupSZrfUp", "answer2_id": "cvBg3gyCyDuyESof3YXhTE", "text": "8 9\nBoth Assistant 1 and Assistant 2 provided helpful and relevant information on assessing the credibility of a source of information. Assistant 1 focused on checking the publisher's reliability, the author's credentials and background, and the credibility of the sources cited in the article. Assistant 2 provided a more detailed and structured response, listing seven specific steps to assess credibility, including checking the author's credentials, publication's reputation, objectivity, supporting evidence, date of publication, corroboration, and maintaining skepticism. Assistant 2's response is more comprehensive and actionable, which is why it receives a higher score.", "score": [8.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "WVQdm2vKM58JfqENPtDoZV", "question_id": 37, "answer1_id": "ednPMy4dvW9CgKg2PJsBqW", "answer2_id": "kRgfUJ7qqkyZUnLd2fnnaX", "text": "7 9\nAssistant 1 provided a brief and general answer, mentioning that people enjoy being scared because it can be exciting and exhilarating, while others avoid it because it can be unpleasant and frightening. However, Assistant 2 provided a more detailed and comprehensive response, discussing various factors such as brain chemistry, life experiences, personality traits, cultural factors, and learning. Assistant 2's answer is more helpful and informative, which is why it receives a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7iXRhpSdjNTnDCokDUiQkv", "question_id": 38, "answer1_id": "ehPApSqCniyGN7hd332ToW", "answer2_id": "J3YuizKcHQ74ydNyCcwgwu", "text": "7 9\nAssistant 1 provided a relevant and helpful response, but it was quite brief and only offered a single example. Assistant 2, on the other hand, provided a more detailed and comprehensive answer, explaining how culture is learned and transmitted through social interactions, and offering examples of different cultural norms. Assistant 2 also mentioned the importance of observing reactions and responses to different situations, which adds more depth to the answer. Therefore, Assistant 2 receives a higher score for providing a more thorough and informative response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "AqhznsybzBuLPTHNsjY8Aw", "question_id": 39, "answer1_id": "bLvdcqSwgSCrUPZ8eQLLqb", "answer2_id": "abWLpFojLpNPfDGHpuRSUG", "text": "8 10\nAssistant 1 provided a brief but relevant response, touching on some benefits of space exploration. However, the answer lacked depth and did not address the moral obligation aspect of the question. Assistant 2, on the other hand, provided a more comprehensive and balanced response, discussing both sides of the argument and offering points to consider when forming an opinion. The answer also acknowledged the complexity and nuance of the issue, making it more helpful and informative.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RyhZiSzP5iu3JZQZ6kp5GZ", "question_id": 40, "answer1_id": "8tx5GEHjSRKZRaZwHvLoM8", "answer2_id": "Ki4fkJvsoSxuQeSoj2AcBG", "text": "7 9\nAssistant 1 provided a relevant and accurate response, but it lacked the level of detail that Assistant 2 provided. Assistant 1 mentioned the importance of balancing job creation and technological progress but did not provide specific examples or suggestions on how to achieve this balance. Assistant 2, on the other hand, provided a more comprehensive answer, discussing the potential benefits of technological progress and the importance of addressing the social and economic impacts of automation. Assistant 2 also suggested policies and programs that could help strike a balance between job creation and technological progress. Overall, Assistant 2's response was more helpful and detailed, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "YCoUoinB2iMTNbyyURn8b6", "question_id": 41, "answer1_id": "J6TzFQAyiHs6Arijqwxtrm", "answer2_id": "GRQjYgAapNAVqzuZR4KQpf", "text": "7 8\nAssistant 1 provided a good estimate of the number of blinks in a lifetime but made an error in the calculation. The correct calculation should be 20,000 blinks/day * 29,415 days = 588,300,000 blinks (for the lower estimate) and 30,000 blinks/day * 29,415 days = 882,450,000 blinks (for the higher estimate). Assistant 2 provided a more detailed step-by-step explanation, considering factors like sleep and varying blink rates. However, Assistant 2's calculation of 4,996,800 blinks in a lifetime seems to be incorrect, as it should be 20,000 blinks/day * 29,200 days = 584,000,000 blinks (assuming 80 years of age). Both assistants provided helpful information, but Assistant 2's response was more detailed and considered more factors, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Xe2MUtxdQ47DQKstUXbbV7", "question_id": 42, "answer1_id": "f7xAtd3wwc4vP7WSnHffAN", "answer2_id": "SB5a2AiK224hB927vkqvYs", "text": "6 9\nAssistant 1 provided an answer with a wide range of atoms (10^22 to 10^23) without explaining the reasoning behind the numbers. Assistant 2, on the other hand, gave a detailed explanation of the calculation process, including the assumptions made, the formulas used, and the intermediate steps. Assistant 2's response was more helpful, relevant, accurate, and detailed, which is why it receives a higher score.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RdK3FHY7HRvju6zxNdX97d", "question_id": 43, "answer1_id": "eRMyp4AbfgBqtbVZzHupFN", "answer2_id": "4XYycBrMiJgYKPof2P6xoe", "text": "7 8\nAssistant 1 provided an answer with specific numbers, but the numbers given were incorrect, stating 50 million strikes per day instead of the correct estimate of 100 million. Assistant 2, on the other hand, provided a more accurate estimate of 100 million lightning strikes per day and also explained the factors that contribute to the number of lightning strikes, such as electrical charge, weather conditions, and location. Assistant 2's response was more detailed and accurate, which is why it received a higher score.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7wcEec9TX2aNeG8qC53m2V", "question_id": 44, "answer1_id": "kjKb6YG86FfAh22qCePV4V", "answer2_id": "fpoXWHHdpv7XNBTS2NRY6d", "text": "6 8\nAssistant 1's response had some errors in calculations and assumptions, which led to an incorrect conclusion. The response assumed that 12L of helium weighs 2 pounds, which is incorrect. Assistant 2's response was more accurate and detailed, explaining that lifting a house with balloons is not feasible in real life and providing a hypothetical calculation based on the movie's technology. Although the lifting power of 100 pounds per balloon was an assumption, the response was more relevant and helpful in explaining the impracticality of the scenario.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2uaVnAXLgg4vmPVBd64p3c", "question_id": 45, "answer1_id": "ULtkGu6m6tB4tawFStVdJM", "answer2_id": "GRXWdnzLLzmeZRU67PMTb7", "text": "7 8\nAssistant 1 provided an answer with specific numbers, but the data is from 2016, which may not be accurate for the current time. However, the response does give a general idea of the scale of text messages sent globally. Assistant 2, on the other hand, explained why it is difficult to determine the exact number of text messages sent globally in a minute and provided a more detailed explanation of the factors that make it challenging to track this information. While Assistant 2 didn't provide a specific number, the response was more informative and relevant to the question, explaining the complexity of the issue.", "score": [7.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "FuWYeqPkW7do6ws6oVKDx2", "question_id": 46, "answer1_id": "C6hkCnJZmfCGvnr5an2jFh", "answer2_id": "RTxk2jYsjYiiWMwcRjmfwu", "text": "4 8\nAssistant 1's response was confusing and contained incorrect calculations, leading to an inaccurate estimate of words spoken daily on Earth. The response also focused solely on the English language, which is not representative of the entire world population. Assistant 2, on the other hand, provided a more logical and coherent explanation, considering the world population and a range of words spoken per person per day. Although the estimate provided by Assistant 2 is still rough, it is more reasonable and relevant to the question asked.", "score": [4.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "njRKU8Jcd7KMDzhZcJ6PuG", "question_id": 47, "answer1_id": "DApBTLuiJwSsfQHTioH9PY", "answer2_id": "2J2Et6W8u2kXLTUgfYrPxe", "text": "4 7\nAssistant 1 provided an incorrect and misleading answer, stating that only 200 snowflakes fall during a typical winter, which is far from accurate. Additionally, the focus on snowflake size was not relevant to the question. Assistant 2, on the other hand, provided a more detailed and relevant response, explaining the factors that influence the number of snowflakes and the difficulty in estimating an exact number. While Assistant 2 did not provide a specific number, the explanation was more helpful, relevant, and accurate, thus receiving a higher score.", "score": [4.0, 7.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ji9DtHHwETNsxf69BH4ScL", "question_id": 48, "answer1_id": "gmy58biNVuneSpFZTcpXtT", "answer2_id": "nSGoG9Z5g5VuCXgmQJJKrq", "text": "6 8\nAssistant 1 provided an estimate of the number of published books but did not attempt to estimate the total number of pages, which was the main focus of the question. The response also mentioned that the number of books is \"peanuts\" compared to the number of books written over time, but did not provide any reasoning or evidence for this claim. Assistant 2, on the other hand, provided a step-by-step explanation of how to estimate the total number of pages in all the books ever published, using reasonable assumptions and calculations. While both assistants acknowledged the difficulty in providing an exact number, Assistant 2's response was more helpful, relevant, and detailed in addressing the user's question.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "VTBkt2MJZEpv7imWL7Sztk", "question_id": 49, "answer1_id": "Cpi6LBB86W73APEA8naNuj", "answer2_id": "ScqJdUq9n5bp9qPv5WPqG5", "text": "5 8\nAssistant 1's response was confusing and contained unnecessary calculations, making it difficult to understand the reasoning. The final answer was also incorrect. Assistant 2 provided a clearer explanation and a more accurate estimate, taking into account the age of the Earth and the average distance between the Earth and the sun. However, the response could have been more precise by mentioning that life is estimated to have begun around 3.5 to 4 billion years ago, rather than using the age of the Earth itself.", "score": [5.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bRdzrs2qxLr8oFKJXmrPoZ", "question_id": 50, "answer1_id": "3g83CoAFWrDD6V6LyEpoMV", "answer2_id": "GdLFr88pCwsGpfRBRQQkyh", "text": "4 8\nAssistant 1's response is inaccurate and seems to be based on arbitrary assumptions, resulting in an implausible estimate of 36,000 songs. Assistant 2, on the other hand, provides a more reasonable and detailed explanation, acknowledging the difficulty of estimating the exact number of songs recorded throughout history and considering various factors that contribute to the vastness and diversity of the music collection. Assistant 2's response is more helpful, relevant, and accurate, which is why it receives a higher score.", "score": [4.0, 8.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "3TAok7dw9s3F2aCWyp3bXc", "question_id": 51, "answer1_id": "8p5A4gouBgCmbQj5gMZapU", "answer2_id": "Sa7uNEbFGRFuXBAwau8sHS", "text": "7 9\nAssistant 1 provided a brief and somewhat relevant answer, but it lacked depth and detail. Assistant 2, on the other hand, provided a more comprehensive response, discussing the technological and cultural context of the Renaissance period and how it might have influenced the development of the Internet. Assistant 2 also considered the limitations of the time and how they would have affected the potential of the Internet. Overall, Assistant 2's answer was more helpful, relevant, accurate, and detailed than Assistant 1's response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "RELThBxYkgYHs4LCozKhtx", "question_id": 52, "answer1_id": "LVWqSxH3Uq6675w7LXH6zP", "answer2_id": "T6Ar3cAEw3AmVVQTcCPp8N", "text": "7 9\nAssistant 1 provided a brief and somewhat speculative response, while Assistant 2 offered a more detailed and balanced answer. Assistant 2 discussed the potential outcomes for the Aztec civilization, such as maintaining their independence, resisting European diseases, and forming alliances with other indigenous groups. Additionally, Assistant 2 considered the possible impact on the spread of Christianity and the economy of Mexico. Overall, Assistant 2's response was more comprehensive and informative, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LhyR6znMTKSq4wfco9D5Be", "question_id": 53, "answer1_id": "ffXksqRU3k8CNHqYfshYhj", "answer2_id": "KiZQev5JEk2h6JYeQnFmtM", "text": "7 9\nAssistant 1 provided a brief response that focused on the current state of the bacterium and how it would affect the modern world, which was not relevant to the question. Assistant 2, on the other hand, provided a more detailed and relevant response, discussing the potential demographic, economic, and social changes that could have occurred in Europe if the Black Death had not happened in the 14th century. Assistant 2's answer was more helpful, accurate, and relevant to the user's question, which is why it received a higher score.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "fLx8FwVXuCooBXpjuTsuwb", "question_id": 54, "answer1_id": "4L3dWuqvWwg5kELt7p8SVZ", "answer2_id": "cYiyYKKXM3GXkrZHAbX83S", "text": "6 9\nAssistant 1 provided a brief and speculative answer, stating that Newton wouldn't have discovered the law of gravity if he had focused on biology. While this is true, it doesn't provide any insight into the potential impact of Newton's work on biology. Assistant 2, on the other hand, gave a more detailed and thoughtful response, discussing the possible contributions Newton could have made to biology while acknowledging the uncertainty of such a scenario. Assistant 2 also provided context on Newton's work in other fields, making their answer more informative and comprehensive.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "DCg6g39EWq7TfYh7GNc6Ti", "question_id": 55, "answer1_id": "XfpqViGW5dvauNZPDSF8yv", "answer2_id": "PQmMUdAAcBsAWmWaTvdHSU", "text": "7 9\nAssistant 1 provided a brief and general answer, stating that the world would be missing the music of one of the most successful bands in history. While this is true, it doesn't provide much detail or insight into the potential impact of the Beatles not forming. Assistant 2, on the other hand, provided a more detailed and thoughtful response, exploring various possibilities of what might have happened if the Beatles had never formed. This answer was more helpful, relevant, and accurate, as it considered different aspects of the music industry, culture, and the individual members' careers.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "EQpmLs3VURbLnTVdC2DajG", "question_id": 56, "answer1_id": "WsuMf9XBGs3dFjfSGKjEzg", "answer2_id": "PorExChQ9VeYsPJptdgtsB", "text": "7 9\nAssistant 1 provided a brief and relevant answer, but it lacked detail and depth. Assistant 2, on the other hand, gave a more comprehensive response, discussing the potential consequences of not cracking the Enigma code, the possible development of alternative strategies, and acknowledging the difficulty in predicting the exact outcome of the war without Turing's contributions. This made Assistant 2's response more helpful and informative.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "ey4ZU2o9V6S8Wsoe2e5veJ", "question_id": 57, "answer1_id": "5qo3HudLrwrzEV2Px7gYRf", "answer2_id": "249f6dSMwZRZVMmtxv6yDm", "text": "7 9\nAssistant 1 provided a brief and accurate response, mentioning the longer travel time and more dangerous waters if the Suez Canal had never been constructed. However, Assistant 2 offered a more comprehensive answer, discussing the impact on international trade, economic development, European colonization, and the significance of the canal's construction in engineering and technology. Assistant 2 also touched on the political and strategic interests surrounding the canal. Therefore, Assistant 2 receives a higher score for providing a more detailed and well-rounded response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "bggwDqWGq27n76ytXx3shS", "question_id": 58, "answer1_id": "SRxwJkNCfUaVe4Gb8LPvSK", "answer2_id": "nxa3m6kiAZwKgcMUBY8KYz", "text": "7 9\nAssistant 1 provided a brief and general response, while Assistant 2 offered a more detailed and comprehensive answer. Assistant 2 explored the potential impact of the Maya civilization on the development of other civilizations, their possible advancements in various fields, and the potential changes in the political and cultural landscape of Mesoamerica. This makes Assistant 2's response more helpful, relevant, and informative compared to Assistant 1's response.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "eYsKC4P5j99ZiJQBKSAUfo", "question_id": 59, "answer1_id": "S54Mmyim3QT5owdhwnNYmB", "answer2_id": "DXFvhjCaKqQiBsFDCkXGMT", "text": "7 9\nAssistant 1 provided a brief background on Christopher Columbus and his discovery of the Americas, but did not directly address the question of what would have happened if he had not discovered the Americas. Assistant 2, on the other hand, provided a more detailed and relevant response, discussing the potential impacts on the indigenous peoples and the possibility of other European explorers discovering the Americas eventually. Assistant 2's response was more helpful, relevant, and accurate in addressing the user's question.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "LJtZNbRQtU9PKwHPFr3isf", "question_id": 60, "answer1_id": "8DJpwEFb5jqt3vMj5oNqVX", "answer2_id": "BCUK5VcpZK26jMJ2UMgR3E", "text": "6 9\nAssistant 1's response provided some background information on Van Gogh's life, but it did not address the hypothetical scenario of him being successful during his lifetime. Assistant 2, on the other hand, directly addressed the question and provided a thoughtful analysis of how Van Gogh's life and career might have been different if he had been successful. The response was relevant, detailed, and explored various possibilities, making it more helpful and informative than Assistant 1's response.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "YJChAPeEgz5eSKT2ZKqBya", "question_id": 61, "answer1_id": "KMtqqYkDmyyThkvxXJ4kJd", "answer2_id": "JHTKBrLVTp7bv5zW2uwYVW", "text": "6 9\n\nAssistant 1:\nStrengths:\n- The answer provides a step-by-step approach to solving the problem, which can be helpful for someone who wants to understand the process.\n\nWeaknesses:\n- The answer does not provide actual code, only a list of steps.\n- The suggested use of the strstr function is not the best choice for this problem, as it can lead to incorrect results if the word is a substring of another word.\n\nSuggestions for improvement:\n- Provide actual code instead of just a list of steps.\n- Use the find function instead of strstr to search for the word in the line, and make sure to account for word boundaries.\n\nAssistant 2:\nStrengths:\n- The answer provides a complete and working code solution.\n- The code is well-structured and easy to understand.\n- The code includes helpful comments that explain the logic and functionality.\n\nWeaknesses:\n- The current implementation only counts lines containing the search string, not the actual number of occurrences of the word in the file.\n\nSuggestions for improvement:\n- Modify the code to count the actual number of occurrences of the word in the file, not just the number of lines containing the word. This can be done by using a loop to search for the word multiple times within the same line, updating the search position after each successful find.", "score": [6.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "ixZgcMexvedv7CXnCbo3hS", "question_id": 62, "answer1_id": "MkbgwTpP5b79NWdPEnvHyq", "answer2_id": "jyzD2deb9JAffRhhkk37r5", "text": "4 7\n\nAssistant 1 provided a brief explanation of the longest common subsequence problem but did not provide any code implementation. The explanation was clear, but it would have been better to include a code example.\n\nAssistant 2 provided a code implementation for the Longest Common Subsequence (LCS) problem using dynamic programming in Python. The code is structured and includes comments explaining the logic. However, the implementation is incorrect, as it doesn't return the actual longest common subsequence but rather the length of it. The initialization of the `dp` table with `float('inf')` is also unnecessary, as it should be initialized with zeros. Here's a corrected version of the code:\n\n```python\ndef lcs(str1, str2):\n    m, n = len(str1), len(str2)\n    dp = [[0 for j in range(n+1)] for i in range(m+1)]\n\n    for i in range(1, m+1):\n        for j in range(1, n+1):\n            if str1[i-1] == str2[j-1]:\n                dp[i][j] = dp[i-1][j-1] + 1\n            else:\n                dp[i][j] = max(dp[i-1][j], dp[i][j-1])\n\n    # Reconstruct the LCS\n    lcs_str = \"\"\n    i, j = m, n\n    while i > 0 and j > 0:\n        if str1[i-1] == str2[j-1]:\n            lcs_str = str1[i-1] + lcs_str\n            i -= 1\n            j -= 1\n        elif dp[i-1][j] > dp[i][j-1]:\n            i -= 1\n        else:\n            j -= 1\n\n    return lcs_str\n\n# Example usage\nstr1 = \"banana\"\nstr2 = \"apple\"\nprint(lcs(str1, str2))  # prints \"ana\"\n```\n\nThis corrected version initializes the `dp` table with zeros and includes a section to reconstruct the LCS from the `dp` table. The example usage has also been updated to match the problem statement.", "score": [4.0, 7.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "gQHT6XKZst42mzwqKYuMNf", "question_id": 63, "answer1_id": "PSb6r567shkRasPkJagvWi", "answer2_id": "jPGHEfp7FxhDyvRA6qEN3j", "text": "6 8\n\nAssistant 1:\nStrengths:\n- The regular expression provided is mostly correct and covers a good range of valid email addresses.\n- The code is simple and easy to understand.\n\nWeaknesses:\n- The code is incomplete and missing the import statement for the 're' module.\n- The code does not handle uppercase letters in the email address.\n- The regular expression does not account for some special characters that are allowed in email addresses.\n- The explanation of the two methods (re.match and re.search) is not necessary, as both can be used to achieve the same result in this case.\n\nSuggestions for improvement:\n- Import the 're' module at the beginning of the code.\n- Update the regular expression to handle uppercase letters and additional special characters.\n- Complete the code by adding the missing parts, such as the 'else' statement and the 'print' function.\n\nAssistant 2:\nStrengths:\n- The code is complete and functional.\n- The regular expression provided is more accurate and covers a wider range of valid email addresses.\n- The code is well-structured and easy to read.\n- The comments are clear and concise, explaining the logic and functionality of the code.\n\nWeaknesses:\n- The regular expression does not account for some special characters that are allowed in email addresses.\n\nSuggestions for improvement:\n- Update the regular expression to handle additional special characters that are allowed in email addresses.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "HWHXDi6bn42H5W4yCeM78C", "question_id": 64, "answer1_id": "QWpBq9gM4iYmsTLQ3NNnBe", "answer2_id": "Ra9eTcAuNZPkPRnaHiQrak", "text": "4 6\n\nAssistant 1:\nStrengths:\n- The problem statement is well explained.\n\nWeaknesses:\n- There is no actual code provided to solve the problem.\n- The input format and test cases are not addressed.\n\nSuggestions for improvement:\n- Provide a working code solution that implements dynamic programming.\n- Address the input format and test cases in the code.\n\nAssistant 2:\nStrengths:\n- A Python code solution is provided.\n- The code is structured and includes comments.\n\nWeaknesses:\n- The code does not use dynamic programming as requested.\n- The code does not handle the input format and test cases as specified in the problem statement.\n- The code is not efficient for large values of n.\n\nSuggestions for improvement:\n- Implement dynamic programming in the code to improve efficiency.\n- Address the input format and test cases in the code.\n- Consider using an iterative approach instead of recursion to save memory and time.", "score": [4.0, 6.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "7t8xTNbmkJUJv4XkbzPCSR", "question_id": 65, "answer1_id": "U3QxoC4DxXoggnLRbgJh8b", "answer2_id": "5mbEFLEnEx8Cm2wEF8WAbM", "text": "4 9\n\nAssistant 1 provided a response that acknowledged the task but did not provide any code or solution. They only restated the problem, which is not helpful in solving the problem.\n\nAssistant 2 provided a clear and concise implementation of the binary search algorithm in Python. The code is well-structured, efficient, and easy to understand. The explanation of the algorithm is thorough, and the time complexity is mentioned. The only minor issue is that the output comment in the example is incorrect (it should be \"output: 3\" instead of \"output: 4\"). Overall, Assistant 2's response is excellent, with just a small mistake in the example output comment.\n\nFor Assistant 1, I would recommend providing an actual implementation of the algorithm instead of just restating the problem. For Assistant 2, I would recommend double-checking the example output to ensure it is accurate.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "j4M8nZhTEZ2AbYippnJwDP", "question_id": 66, "answer1_id": "f3TXCreQPJ5Erd6BGt4SFE", "answer2_id": "KLbBzjbuwq4agAESQ9Wfpq", "text": "4 8\n\nAssistant 1:\nStrengths:\n- The explanation of the approach is clear and easy to understand.\n\nWeaknesses:\n- The answer does not provide any code implementation.\n- The explanation does not mention the correct approach of using two stacks for implementing a queue.\n\nSuggestions for improvement:\n- Provide a code implementation for the given problem statement.\n- Explain the correct approach of using two stacks to implement a queue, which involves transferring elements between the stacks when needed.\n\nAssistant 2:\nStrengths:\n- The code implementation is provided.\n- The code is clear and easy to understand.\n- The code includes comments explaining the functionality of each method.\n\nWeaknesses:\n- The dequeue method is not implemented correctly. It should transfer elements from the in_stack to the out_stack when the out_stack is empty and then pop the top element from the out_stack.\n- The peek method should return the top element of the out_stack if it's not empty, otherwise, it should transfer elements from the in_stack to the out_stack and return the top element.\n\nSuggestions for improvement:\n- Correct the dequeue and peek methods to properly implement the queue using two stacks.\n- Add comments to explain the logic behind transferring elements between the stacks when needed.", "score": [4.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "RivhKLYyBmjyVjKvc5UnKd", "question_id": 67, "answer1_id": "SG7YcT8s4sJNBRqidKk7Z7", "answer2_id": "ganAwetd9VTzEAiTtsskzX", "text": "6 8\n\nAssistant 1:\nStrengths:\n- The general approach is described well, and the steps are clear.\n\nWeaknesses:\n- The actual code implementation is missing, making it difficult to evaluate the code structure, readability, and comments.\n- The helper function is mentioned but not defined or explained.\n\nSuggestions for improvement:\n- Provide a complete code implementation, including the helper function.\n- Add comments to the code to explain the logic and functionality.\n\nAssistant 2:\nStrengths:\n- The code is provided, and it correctly implements the given problem statement.\n- The code is accurate and efficient.\n- The example usage is helpful for understanding how the function works.\n\nWeaknesses:\n- The code lacks comments to explain the logic and functionality.\n\nSuggestions for improvement:\n- Add comments to the code to explain the logic and functionality.\n- Consider edge cases and add error handling if necessary.", "score": [6.0, 8.0], "reviewer_id": "gpt-4-0328-coding", "metadata": {}}
-{"review_id": "WjYY86fiq6muEGUJ8Q4Ayp", "question_id": 68, "answer1_id": "ENsG3zoeUY7ArFo2kNaQbz", "answer2_id": "LG6szQ3cqNRzUjXtZtXG94", "text": "First, I will solve the problem independently:\n\nGiven f(x) = 5x^3 - 2x + 3, we need to find the value of f(2).\n\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 4 + 3 = 40 - 4 + 3 = 39\n\nNow, I will evaluate the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nf(2) = 26\n\nAssistant 1 provided an incorrect answer without showing any steps.\n\nAssistant 2:\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 2 + 3 = 41 - 2 + 3 = 40\n\nAssistant 2 provided a step-by-step solution but made a mistake in the calculation. The correct calculation should be:\n\nf(2) = 5(2)^3 - 2(2) + 3 = 5 * 8 - 4 + 3 = 40 - 4 + 3 = 39\n\nScores:\nAssistant 1: 1 (Incorrect answer and no steps provided)\nAssistant 2: 7 (Correct steps but made a calculation mistake)\n\nAssistant 2 performed better as they provided the correct steps but made a calculation mistake. The correct answer is f(2) = 39.\n\nOutput: (1, 7)", "score": [1, 7], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "WXsw2Xuc8fUawFxh6neKj9", "question_id": 69, "answer1_id": "C7CN5AYJhGC7iDgS7rciDQ", "answer2_id": "LHMnXGbSZubeZJcr5fgrte", "text": "First, I will solve the problem independently:\n\nGiven equation: 3x + 10 = 5(x - 2)\n\n1. Distribute the 5 on the right-hand side: 3x + 10 = 5x - 10\n2. Move the x terms to one side by subtracting 3x from both sides: 10 = 2x - 10\n3. Move the constant term to the other side by adding 10 to both sides: 20 = 2x\n4. Divide both sides by 2 to solve for x: x = 10\n\nNow, I will evaluate the problem-solving process of Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe steps provided by Assistant 1 are incorrect and confusing. The answer is also incorrect. \n\nAssistant 2:\n1. The first step is correct.\n2. The second step is incorrect. The correct step should be subtracting 3x from both sides, not subtracting 5x.\n3-7. All the subsequent steps are incorrect as they are based on the incorrect second step.\n\nScores:\nAssistant 1: 1/10\nAssistant 2: 2/10\n\nBoth assistants performed poorly, but Assistant 2 performed slightly better as the first step was correct.", "score": [1, 2], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "ZgNXGB9wziYPnGFjqLzqVH", "question_id": 70, "answer1_id": "QP9CmZKwhu2BrUmzhy8tH4", "answer2_id": "TGENVWL5iAYi4NCf5jAzKy", "text": "First, I will solve the problem independently:\n\nThe length of a line segment between two points (x1, y1) and (x2, y2) can be found using the distance formula:\n\ndistance = sqrt((x2 - x1)^2 + (y2 - y1)^2)\n\nGiven the points (2, -2) and (10, 4), we can plug in the values:\n\ndistance = sqrt((10 - 2)^2 + (4 - (-2))^2)\ndistance = sqrt(8^2 + 6^2)\ndistance = sqrt(64 + 36)\ndistance = sqrt(100)\ndistance = 10\n\nNow, I will evaluate the answers provided by Assistant 1 and Assistant 2:\n\nAssistant 1:\nThe length of the line segment is 10 units.\n\nThis answer is correct.\n\nAssistant 2:\nThe length of the line segment is 17.973.\n\nThis answer is incorrect. The mistake is in the calculation of the differences between the x and y coordinates. The correct differences should be 8 and 6, not 18 and 4.\n\nScores:\nAssistant 1: 10\nAssistant 2: 4\n\nAssistant 1 performed better because they provided the correct answer and used the correct method to solve the problem. Assistant 2 made a mistake in the calculation of the differences between the x and y coordinates, which led to an incorrect answer.", "score": [10, 2], "reviewer_id": "gpt-4-0328-math", "metadata": {}}
-{"review_id": "YCbKCPhn4DqT56ASDfFhnz", "question_id": 71, "answer1_id": "2KtpJeRqEfbPLwdUxAXWrj", "answer2_id": "kc9oytarvqyNVVfGZz93EL", "text": "7 10\nAssistant 1 provided general tips for writing a formal email, which were helpful but did not directly address the user's request for help in writing the email. Assistant 2, on the other hand, provided a complete and well-written example of a formal email proposing a joint venture, which directly addressed the user's request. Assistant 2's response was more relevant, accurate, and detailed, which is why it receives a higher score.", "score": [7.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "oYP3NAG57ufZ4vfnvfsSty", "question_id": 72, "answer1_id": "gsL3R2grTXUiHdeDSk8f9Q", "answer2_id": "NxstpvVEnGMny3jECtvrw2", "text": "8 10\nAssistant 1 provided a helpful and relevant response, but it was not as detailed and complete as Assistant 2's response. Assistant 2 provided a full sample resignation letter that the user can use as a guide, which is more helpful and detailed than Assistant 1's response. Both assistants were accurate in addressing the user's request, but Assistant 2 provided a more comprehensive and well-structured answer.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "kQCXTX4kvYL3ujbvNFfCfm", "question_id": 73, "answer1_id": "HprPMoUk9JY5TtToRbuMwy", "answer2_id": "XN9NBpeQJiQqGT5UYkys7N", "text": "8 10\nAssistant 1 provided a brief and general outline of a recommendation letter, which is helpful but lacks the level of detail and specificity required for the user's request. Assistant 2, on the other hand, provided a comprehensive and well-structured example of a formal letter of recommendation tailored to a student applying to a prestigious graduate program in computer science. This response is more relevant, accurate, and detailed, making it the better answer for the user's needs.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "Jfo5EVsy9oemR8iJ6B5gyU", "question_id": 74, "answer1_id": "YU7gDhmo4LDVMTEZZMRdBC", "answer2_id": "3qMqAZcfHUVUTEJnMXT8Pw", "text": "0 9\n\nAssistant 1 did not provide any response, so it receives a score of 0. Assistant 2 provided a well-structured and compelling product launch announcement email, including a subject line, introduction, key features, and a call to action. The response was relevant, accurate, and detailed, earning a score of 9. The only improvement could be the addition of more specific details about the software, but this would require more information from the user.", "score": [0.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "2LY8t4Hq9j47SrN5vs4Tui", "question_id": 75, "answer1_id": "2gNqU2XpsL6yqFn6DMRgHq", "answer2_id": "hMXjd228Mc8pnc7zgd2frG", "text": "4 10\nAssistant 1 only asked for the customer's name and did not provide any actual content for the apology email, making their response less helpful and relevant. On the other hand, Assistant 2 provided a well-written and detailed apology email that addressed the customer's concerns and reassured them that the issue has been resolved. This response was helpful, relevant, accurate, and contained a good level of detail, which is why Assistant 2 receives a perfect score.", "score": [4.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "4HSThcxvTM4TjQ9a9vMbds", "question_id": 76, "answer1_id": "a4L6JNPuomkPLdWxJRQpod", "answer2_id": "gUEd9nNepqy8nsfbypscHx", "text": "7 9\nAssistant 1 provided a brief overview of jazz, its origins, and its evolution, but the response lacked the structure and detail of a script for a YouTube video. Assistant 2, on the other hand, provided a more comprehensive and well-structured script, discussing key figures, the influence of jazz on other genres, and its role in the civil rights movement. Assistant 2's response was more engaging and informative, making it a better choice for a YouTube video script.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "HfKLQTn2DvPDUdgPNoY6MG", "question_id": 77, "answer1_id": "eN9V2Wf2BNzWoJosqdFw6K", "answer2_id": "nZZyn3SAvU8T2BVR73LMHC", "text": "1 9\n\nAssistant 1 failed to provide any response to the user's request, while Assistant 2 composed an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. Assistant 2's response was helpful, relevant, accurate, and detailed, making it deserving of a high score.", "score": [1.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "6pzkZvS4nNDwacSTqzvw7z", "question_id": 78, "answer1_id": "iCzkbWTatKfuC6QVv59XgA", "answer2_id": "YM4qjUM5VfmWnzM3HXRB4G", "text": "7 9\nAssistant 1 provided a detailed response, but the plot summary seemed to be a mix of \"Ready Player One\" and an original story, which led to confusion. The review also lacked the captivating tone that was requested. Assistant 2, on the other hand, delivered a captivating review with a clear focus on the plot, characters, and special effects. The tone was engaging and enthusiastic, making it more appealing to the reader.", "score": [7.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "7uM72Nb4Sn5eo6TrYw2bpR", "question_id": 79, "answer1_id": "GQcFyY8ayFT48DAP5NKNZf", "answer2_id": "WcbzNkzu5q66QkK92NxmGB", "text": "4 9\nAssistant 1 provided a long list of points without any structure or organization, making it difficult to use as a podcast script. Assistant 2, on the other hand, provided a well-structured podcast script with clear segments and topics, making it much more suitable for the user's request. Assistant 2's response was helpful, relevant, accurate, and detailed, while Assistant 1's response lacked organization and coherence.", "score": [4.0, 9.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
-{"review_id": "dXWWCggLzLD4SBZH2JSAZH", "question_id": 80, "answer1_id": "A6pauLMckn34otnfpeQRFi", "answer2_id": "Tfm4f2mbpKJsa8MT6exs8D", "text": "8 10\nAssistant 1 provided a brief and general overview of the concert experience, mentioning the composers and the audience's reaction. However, Assistant 2 offered a more detailed and engaging review, discussing specific pieces, the conductor's skill, and the emotions evoked by the performance. Assistant 2's response also painted a vivid picture of the concert experience, making it more helpful and informative for someone interested in a symphony concert review.", "score": [8.0, 10.0], "reviewer_id": "gpt-4-0328-generic", "metadata": {}}
diff --git a/llava/eval/table/reviewer.jsonl b/llava/eval/table/reviewer.jsonl
deleted file mode 100644
index e96127cc..00000000
--- a/llava/eval/table/reviewer.jsonl
+++ /dev/null
@@ -1,4 +0,0 @@
-{"reviewer_id": "gpt-4-0328-default", "prompt_id": 1, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for general questions"}
-{"reviewer_id": "gpt-4-0328-coding", "prompt_id": 2, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for coding questions"}
-{"reviewer_id": "gpt-4-0328-math", "prompt_id": 3, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"}
-{"reviewer_id": "gpt-4-0417-visual", "prompt_id": 4, "metadata": {"temperature": 0.2, "max_tokens": 1024}, "description": "GPT-4 for math questions"}
diff --git a/llava/eval/video/eval_benchmark_1_correctness.py b/llava/eval/video/eval_benchmark_1_correctness.py
old mode 100644
new mode 100755
diff --git a/llava/eval/video/eval_benchmark_2_detailed_orientation.py b/llava/eval/video/eval_benchmark_2_detailed_orientation.py
old mode 100644
new mode 100755
diff --git a/llava/eval/video/eval_benchmark_3_context.py b/llava/eval/video/eval_benchmark_3_context.py
old mode 100644
new mode 100755
diff --git a/llava/eval/video/eval_benchmark_4_temporal.py b/llava/eval/video/eval_benchmark_4_temporal.py
old mode 100644
new mode 100755
diff --git a/llava/eval/video/eval_benchmark_5_consistency.py b/llava/eval/video/eval_benchmark_5_consistency.py
old mode 100644
new mode 100755
diff --git a/llava/eval/video/eval_video_nextqa.py b/llava/eval/video/eval_video_nextqa.py
new file mode 100644
index 00000000..3b0908bf
--- /dev/null
+++ b/llava/eval/video/eval_video_nextqa.py
@@ -0,0 +1,163 @@
+import os
+import argparse
+import json
+from nltk.tokenize import word_tokenize
+from nltk.corpus import wordnet
+import nltk
+nltk.download('averaged_perceptron_tagger')
+nltk.download('stopwords')
+nltk.download('wordnet')    # if you see error complaining "Resource wordnet not found.", even if you have already downloaded it, try unzip it by yourself. ref: https://github.com/nltk/nltk/issues/3028
+nltk.download('punkt')
+from pywsd.utils import lemmatize_sentence
+
+
+import numpy as np
+from tqdm import tqdm
+
+def load_stopwords_file(filename):
+    with open(filename, 'r') as fp:
+        cont = fp.readlines()
+        cont = [c.rstrip('\n') for c in cont]
+    return cont
+
+def remove_stop(sentence, stopwords):
+
+    words = lemmatize_sentence(sentence)
+    words = [w for w in words if not w in stopwords]
+    return ' '.join(words)
+
+def wup(word1, word2, alpha):
+    """
+    calculate the wup similarity
+    :param word1:
+    :param word2:
+    :param alpha:
+    :return:
+    """
+    # print(word1, word2)
+    if word1 == word2:
+        return 1.0
+
+    w1 = wordnet.synsets(word1)
+    w1_len = len(w1)
+    if w1_len == 0: return 0.0
+    w2 = wordnet.synsets(word2)
+    w2_len = len(w2)
+    if w2_len == 0: return 0.0
+
+    #match the first
+    word_sim = w1[0].wup_similarity(w2[0])
+    if word_sim is None:
+        word_sim = 0.0
+
+    if word_sim < alpha:
+        word_sim = 0.1*word_sim
+    return word_sim
+
+
+def wups(words1, words2, alpha):
+    """
+
+    :param pred:
+    :param truth:
+    :param alpha:
+    :return:
+    """
+    sim = 1.0
+    flag = False
+    for w1 in words1:
+        max_sim = 0
+        for w2 in words2:
+            word_sim = wup(w1, w2, alpha)
+            if word_sim > max_sim:
+                max_sim = word_sim
+        if max_sim == 0: continue
+        sim *= max_sim
+        flag = True
+    if not flag:
+        sim = 0.0
+    return sim
+
+
+def get_wups(pred, truth, alpha):
+    """
+    calculate the wups score
+    :param pred:
+    :param truth:
+    :return:
+    """
+    pred = word_tokenize(pred)
+    truth = word_tokenize(truth)
+    item1 = wups(pred, truth, alpha)
+    item2 = wups(truth, pred, alpha)
+    value = min(item1, item2)
+    return value
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", default=r'', help="The path to file containing prediction.")
+    parser.add_argument("--output_json", default=r'', help="The path to save annotation final combined json file.")
+    parser.add_argument("--gt_file", default=r"", help="The path to save the ground truth file.")
+    parser.add_argument("--stopwords_file", default="stopwords.txt", help="The path to save the stop words file.")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+    file = open(args.pred_path)
+    new_pred_contents = [eval(i.strip()) for i in file.readlines()]
+    file.close()
+    stopwords = load_stopwords_file(args.stopwords_file)
+
+    # Read the ground truth csv file
+    gt_file = os.path.expanduser(args.gt_file)
+    with open(gt_file, 'r') as f:
+        gt_qa_data = f.readlines()
+    qtype_dict = {}
+    for line in gt_qa_data[1:]:
+        line = line.strip()
+        line = line.split(',')
+        video_name = line[1]
+        question_id = line[7]
+        question_type = line[8]
+        if video_name not in qtype_dict:
+            qtype_dict[video_name] = {}
+            qtype_dict[video_name][question_id] = question_type
+        else:
+            qtype_dict[video_name][question_id] = question_type
+
+    # Preparing dictionary of question-answer sets
+    prediction_set = {}
+    sum_score = 0.0
+    for sample in new_pred_contents:
+        video_name = sample['video_name']
+        id = sample['id']
+        question = sample['question']
+        answer = sample['answer']
+        pred = sample['pred']
+        answer_processed = remove_stop(answer, stopwords)
+        pred_processed = remove_stop(pred, stopwords)
+        # if id in qtype_dict[video_name]:
+        qtype = qtype_dict[video_name][id]
+        if qtype == 'DC' or qtype == 'DB':
+            score = 1.0 if answer_processed == pred_processed else 0.0
+        else:
+            score = get_wups(pred_processed, answer_processed, 0.0)
+        sum_score += score
+        qa_set = {"q": question, "a": answer, "pred": pred, "score": score}
+        prediction_set[id] = qa_set
+    
+    sum_score = sum_score / len(new_pred_contents)
+    print(f"Average WUPS score: {sum_score}")
+
+    with open(args.output_json, 'w') as f:
+        json.dump(prediction_set, f, indent=4)
+    print(f"Saved the prediction file at {args.output_json}")
+
+if __name__ == "__main__":
+    main()
diff --git a/llava/eval/video/eval_video_perception.py b/llava/eval/video/eval_video_perception.py
new file mode 100644
index 00000000..f2a2c471
--- /dev/null
+++ b/llava/eval/video/eval_video_perception.py
@@ -0,0 +1,37 @@
+import os
+import argparse
+import json
+import numpy as np
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="question-answer-generation-using-gpt-3")
+    parser.add_argument("--pred_path", default=r'', help="The path to file containing prediction.")
+    parser.add_argument("--output_json", default=r'', help="The path to save annotation final combined json file.")
+    args = parser.parse_args()
+    return args
+
+def main():
+    """
+    Main function to control the flow of the program.
+    """
+    # Parse arguments.
+    args = parse_args()
+    # Read the prediction file
+    with open(args.pred_path, 'r') as f:
+        pred = f.readlines()
+    # Convert each line of pred to a list of json objects
+    new_pred_contents = [json.loads(i.strip()) for i in pred]
+    total = 0
+    correct = 0
+    for pred in new_pred_contents:
+        if pred["correct"]:
+            correct += 1
+        total += 1
+    print("Total: ", total)
+    print("Correct: ", correct)
+    print("Accuracy: ", correct/total)
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/llava/eval/video/eval_video_qa.py b/llava/eval/video/eval_video_qa.py
old mode 100644
new mode 100755
index f0be2ac1..1ed134e6
--- a/llava/eval/video/eval_video_qa.py
+++ b/llava/eval/video/eval_video_qa.py
@@ -14,13 +14,14 @@ def parse_args():
     parser.add_argument("--output_dir", default=r'', help="The path to save annotation json files.")
     parser.add_argument("--output_json", default=r'', help="The path to save annotation final combined json file.")
     parser.add_argument("--num_tasks", default=1, type=int, help="Number of splits.")
+    parser.add_argument("--gpt_model", default="gpt-4-0125-preview", help="The path to save annotation final combined json file.")
     args = parser.parse_args()
     return args
 
 
 def annotate(prediction_set, caption_files, output_dir, args):
     """
-    Evaluates question and answer pairs using GPT-3
+    Evaluates question and answer pairs using GPT-3.5/GPT-4
     Returns a score for correctness.
     """
     # Set the OpenAI API key.
@@ -34,7 +35,7 @@ def annotate(prediction_set, caption_files, output_dir, args):
         try:
             # Compute the correctness score
             completion = openai.chat.completions.create(
-                model="gpt-3.5-turbo",
+                model=args.gpt_model,
                 messages=[
                     {
                         "role": "system",
@@ -124,6 +125,7 @@ def main():
     num_tasks = args.num_tasks
 
     # While loop to ensure that all captions are processed.
+    num_incomplete_files_list = []
     while True:
         try:
             # Files that have not been processed yet.
@@ -132,7 +134,12 @@ def main():
 
             # Files that have not been processed yet.
             incomplete_files = [f for f in caption_files if f not in completed_files]
+
             print(f"incomplete_files: {len(incomplete_files)}")
+            num_incomplete_files_list.append(len(incomplete_files))
+
+            if len(num_incomplete_files_list) >= 10 and len(set(num_incomplete_files_list[-10:])) == 1:
+                break
 
             # Break the loop when there are no incomplete files
             if len(incomplete_files) == 0:
diff --git a/llava/mm_utils.py b/llava/mm_utils.py
index ab18720f..dc8ba56d 100644
--- a/llava/mm_utils.py
+++ b/llava/mm_utils.py
@@ -1,18 +1,141 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
 from PIL import Image
 from io import BytesIO
 import base64
+import numpy as np
+import os
 
 import torch
 from transformers import StoppingCriteria
 from llava.constants import IMAGE_TOKEN_INDEX
 
+import tempfile
+from io import BytesIO
+
+
+def get_frame_from_vcap(vidcap, num_frames=10, fps=None, frame_count=None):
+    import cv2
+
+    if fps == None or frame_count == None:
+        # if one of fps or frame_count is None, still recompute
+        fps = vidcap.get(cv2.CAP_PROP_FPS)
+        frame_count = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if fps == 0 or frame_count == 0:
+        print("Video file not found. return empty images.")
+        return [
+            Image.new("RGB", (720, 720)),
+        ] * num_frames
+    
+    duration = frame_count / fps
+    frame_interval = frame_count // num_frames
+    if frame_interval == 0 and frame_count <= 1:
+        print("frame_interval is equal to 0. return empty image.")
+        return [
+            Image.new("RGB", (720, 720)),
+        ] * num_frames
+    # print("duration:", duration, "frames:", frame_count, "intervals:", frame_interval)
+
+    images = []
+    count = 0
+    success = True
+    frame_indices = np.linspace(0, frame_count - 2, num_frames, dtype=int)
+
+    while success:
+        # print("frame_count:", frame_count, "count:", count, "num_frames:", num_frames, "frame_interval:", frame_interval)
+        if frame_count >= num_frames:
+            success, frame = vidcap.read()
+            if count in frame_indices:
+                img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                im_pil = Image.fromarray(img)
+                images.append(im_pil)
+                if len(images) >= num_frames:
+                    return images
+            count += 1
+        else:
+            # Left padding frames if the video is not long enough
+            success, frame = vidcap.read()
+            if success:
+                img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                im_pil = Image.fromarray(img)
+                images.append(im_pil)
+                count += 1
+            elif count >= 1:
+                width, height = images[-1].size
+                images = [Image.new("RGB", (width, height))] * (num_frames - len(images)) + images
+                print("padding frames:", (num_frames - len(images)))
+                return images
+            else: 
+                break
+    raise ValueError("Did not find enough frames in the video. return empty image.")
+
+
+def opencv_extract_frames(vpath_or_bytesio, frames=6, fps=None, frame_count=None):
+    """
+    Extract frames from a video using OpenCV.
+
+    Args:
+        vpath_or_bytesio (str or BytesIO): Path to the video file or BytesIO object containing the video.
+        frames (int): Number of frames to extract from the video.
+
+    Returns:
+        list: List of PIL Images extracted from the video.
+
+    Raises:
+        NotImplementedError: If the type of `vpath_or_bytesio` is not supported.
+    """
+    import cv2
+
+    if isinstance(vpath_or_bytesio, str):
+        vidcap = cv2.VideoCapture(vpath_or_bytesio)
+        return get_frame_from_vcap(vidcap, frames, fps=fps, frame_count=frame_count)
+    elif isinstance(vpath_or_bytesio, (BytesIO,)):
+        # assuming mp4
+        with tempfile.NamedTemporaryFile(delete=True, suffix=".mp4") as temp_video:
+            temp_video.write(vpath_or_bytesio.read())
+            temp_video_name = temp_video.name
+            vidcap = cv2.VideoCapture(temp_video_name)
+            return get_frame_from_vcap(vidcap, frames, fps=fps, frame_count=frame_count)
+    else:
+        raise NotImplementedError(type(vpath_or_bytesio))
+
 
 def load_image_from_base64(image):
     return Image.open(BytesIO(base64.b64decode(image)))
 
 
 def expand2square(pil_img, background_color):
+    """
+    Expand the given PIL image to a square shape by adding padding.
+
+    Parameters:
+    - pil_img: The PIL image to be expanded.
+    - background_color: The color of the padding to be added.
+
+    Returns:
+    - The expanded PIL image.
+
+    If the image is already square, it is returned as is.
+    If the image is wider than it is tall, padding is added to the top and bottom.
+    If the image is taller than it is wide, padding is added to the left and right.
+    """
     width, height = pil_img.size
+    if pil_img.mode == 'L':
+        background_color = background_color[0]
     if width == height:
         return pil_img
     elif width > height:
@@ -24,31 +147,76 @@ def expand2square(pil_img, background_color):
         result.paste(pil_img, ((height - width) // 2, 0))
         return result
 
+def process_image(image_file, data_args, image_folder):
+    processor = data_args.image_processor
+    if isinstance(image_file, str):
+        if image_folder is not None:
+            image = Image.open(os.path.join(image_folder, image_file)).convert("RGB")
+        else:
+            image = Image.open(image_file).convert("RGB")
+    else:
+        # image is stored in bytearray
+        image = image_file
+    if data_args.image_aspect_ratio == "resize":
+        if hasattr(data_args.image_processor, "crop_size"):
+            # CLIP vision tower
+            crop_size = data_args.image_processor.crop_size
+        else:
+            # SIGLIP vision tower
+            assert hasattr(data_args.image_processor, "size")
+            crop_size = data_args.image_processor.size
+        image = image.resize((crop_size["height"], crop_size["width"]))
+    if data_args.image_aspect_ratio == "pad":
+
+        def expand2square(pil_img, background_color):
+            width, height = pil_img.size
+            if width == height:
+                return pil_img
+            elif width > height:
+                result = Image.new(pil_img.mode, (width, width), background_color)
+                result.paste(pil_img, (0, (width - height) // 2))
+                return result
+            else:
+                result = Image.new(pil_img.mode, (height, height), background_color)
+                result.paste(pil_img, ((height - width) // 2, 0))
+                return result
 
-def process_images(images, image_processor, model_cfg):
-    image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
-    new_images = []
-    if image_aspect_ratio == 'pad':
-        for image in images:
-            image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
-            image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            new_images.append(image)
+        image = expand2square(image, tuple(int(x * 255) for x in processor.image_mean))
+        image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
     else:
-        return image_processor(images, return_tensors='pt')['pixel_values']
+        # Using default behavior of the vision encoder
+        # For CLIP, default is central crop
+        # For Radio, default is central crop
+        # For Siglip, default is resize
+        # For InternVIT, default is resize
+        image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
+    return image
+
+def process_images(images, image_processor, model_cfg):
+
+    model_cfg.image_processor = image_processor
+    new_images = [process_image(image, model_cfg, None) for image in images]
+
     if all(x.shape == new_images[0].shape for x in new_images):
         new_images = torch.stack(new_images, dim=0)
     return new_images
 
 
-def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
-    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
+def tokenizer_image_token(
+    prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None
+):
+    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("<image>")]
 
     def insert_separator(X, sep):
-        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
+        return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1]
 
     input_ids = []
     offset = 0
-    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
+    if (
+        len(prompt_chunks) > 0
+        and len(prompt_chunks[0]) > 0
+        and prompt_chunks[0][0] == tokenizer.bos_token_id
+    ):
         offset = 1
         input_ids.append(prompt_chunks[0][0])
 
@@ -56,9 +224,9 @@ def insert_separator(X, sep):
         input_ids.extend(x[offset:])
 
     if return_tensors is not None:
-        if return_tensors == 'pt':
+        if return_tensors == "pt":
             return torch.tensor(input_ids, dtype=torch.long)
-        raise ValueError(f'Unsupported tensor type: {return_tensors}')
+        raise ValueError(f"Unsupported tensor type: {return_tensors}")
     return input_ids
 
 
@@ -69,11 +237,12 @@ def is_gemma_tokenizer(tokenizer):
 def get_model_name_from_path(model_path):
     model_path = model_path.strip("/")
     model_paths = model_path.split("/")
-    if model_paths[-1].startswith('checkpoint-'):
+    if model_paths[-1].startswith("checkpoint-"):
         return model_paths[-2] + "_" + model_paths[-1]
     else:
         return model_paths[-1]
 
+
 class KeywordsStoppingCriteria(StoppingCriteria):
     def __init__(self, keywords, tokenizer, input_ids):
         self.keywords = keywords
@@ -81,27 +250,38 @@ def __init__(self, keywords, tokenizer, input_ids):
         self.max_keyword_len = 0
         for keyword in keywords:
             cur_keyword_ids = tokenizer(keyword).input_ids
-            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
+            if (
+                len(cur_keyword_ids) > 1
+                and cur_keyword_ids[0] == tokenizer.bos_token_id
+            ):
                 cur_keyword_ids = cur_keyword_ids[1:]
             if len(cur_keyword_ids) > self.max_keyword_len:
                 self.max_keyword_len = len(cur_keyword_ids)
             self.keyword_ids.append(torch.tensor(cur_keyword_ids))
         self.tokenizer = tokenizer
         self.start_len = input_ids.shape[1]
-    
-    def call_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+
+    def call_for_batch(
+        self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+    ) -> bool:
         offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
-        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
+        self.keyword_ids = [
+            keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids
+        ]
         for keyword_id in self.keyword_ids:
-            if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
+            if (output_ids[0, -keyword_id.shape[0] :] == keyword_id).all():
                 return True
-        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
+        outputs = self.tokenizer.batch_decode(
+            output_ids[:, -offset:], skip_special_tokens=True
+        )[0]
         for keyword in self.keywords:
             if keyword in outputs:
                 return True
         return False
-    
-    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+
+    def __call__(
+        self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+    ) -> bool:
         outputs = []
         for i in range(output_ids.shape[0]):
             outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores))
diff --git a/llava/model/__init__.py b/llava/model/__init__.py
index cdc87708..6fd2e0df 100644
--- a/llava/model/__init__.py
+++ b/llava/model/__init__.py
@@ -1,4 +1,6 @@
-from .language_model.llava_llama import LlavaLlamaForCausalLM, LlavaConfig
+from .language_model.llava_llama import LlavaLlamaModel, LlavaLlamaConfig
 # from .language_model.llava_mpt import LlavaMPTForCausalLM, LlavaMPTConfig
 from .language_model.llava_mixtral import LlavaMixtralForCausalLM, LlavaMixtralConfig
 from .language_model.llava_mistral import LlavaMistralForCausalLM, LlavaMistralConfig
+# from .language_model.llava_gemma import LlavaGemmaForCausalLM, LlavaGemmaConfig
+
diff --git a/llava/model/apply_delta.py b/llava/model/apply_delta.py
index f351f011..01ede651 100644
--- a/llava/model/apply_delta.py
+++ b/llava/model/apply_delta.py
@@ -15,24 +15,32 @@
 def apply_delta(base_model_path, target_model_path, delta_path):
     print("Loading base model")
     base = AutoModelForCausalLM.from_pretrained(
-        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
+    )
 
     print("Loading delta")
-    delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    delta = LlavaLlamaForCausalLM.from_pretrained(
+        delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
+    )
     delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
 
     print("Applying delta")
     for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
         if name not in base.state_dict():
-            assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
+            assert name in [
+                "model.mm_projector.weight",
+                "model.mm_projector.bias",
+            ], f"{name} not in base model"
             continue
         if param.data.shape == base.state_dict()[name].shape:
             param.data += base.state_dict()[name]
         else:
-            assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
-                f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
+            assert name in [
+                "model.embed_tokens.weight",
+                "lm_head.weight",
+            ], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}"
             bparam = base.state_dict()[name]
-            param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
+            param.data[: bparam.shape[0], : bparam.shape[1]] += bparam
 
     print("Saving target model")
     delta.save_pretrained(target_model_path)
diff --git a/llava/model/builder.py b/llava/model/builder.py
index 6f53013d..c8b7110f 100644
--- a/llava/model/builder.py
+++ b/llava/model/builder.py
@@ -18,146 +18,243 @@
 import warnings
 import shutil
 
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig, LlamaConfig
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+)
 import torch
 from llava.model import *
 from llava.model.utils import is_mm_model
-from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from llava.model.language_model.llava_llama import LlavaConfig
+from llava.constants import (
+    DEFAULT_IMAGE_PATCH_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IM_END_TOKEN,
+)
 
-
-def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", **kwargs):
+def load_pretrained_model(
+    model_path,
+    model_name,
+    model_base=None,
+    load_8bit=False,
+    load_4bit=False,
+    device_map="auto",
+    device="cuda",
+    **kwargs,
+):
     kwargs = {"device_map": device_map, **kwargs}
 
     if device != "cuda":
-        kwargs['device_map'] = {"": device}
+        kwargs["device_map"] = {"": device}
 
     if load_8bit:
-        kwargs['load_in_8bit'] = True
+        kwargs["load_in_8bit"] = True
     elif load_4bit:
-        kwargs['load_in_4bit'] = True
-        kwargs['quantization_config'] = BitsAndBytesConfig(
+        kwargs["load_in_4bit"] = True
+        kwargs["quantization_config"] = BitsAndBytesConfig(
             load_in_4bit=True,
             bnb_4bit_compute_dtype=torch.float16,
             bnb_4bit_use_double_quant=True,
-            bnb_4bit_quant_type='nf4'
+            bnb_4bit_quant_type="nf4",
         )
     else:
-        kwargs['torch_dtype'] = torch.float16
+        kwargs["torch_dtype"] = torch.float16
 
     if is_mm_model(model_path):
         # Load LLaVA model
-        if 'lora' in model_name.lower() and model_base is None:
-            warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
-        if 'lora' in model_name.lower() and model_base is not None:
+        ## TODO @yunhao: mind fixing lora
+        if "lora" in model_name.lower() and model_base is None:
+            warnings.warn(
+                "There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged."
+            )
+        if "lora" in model_name.lower() and model_base is not None:
             lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False, legacy=False)
-            print('Loading LLaVA from base model...')
-            model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
+            tokenizer = AutoTokenizer.from_pretrained(
+                model_base, use_fast=False, legacy=False
+            )
+            print("Loading LLaVA from base model...")
+            model = LlavaLlamaForCausalLM.from_pretrained(
+                model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs
+            )
             token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
             if model.lm_head.weight.shape[0] != token_num:
-                model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
-                model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
+                model.lm_head.weight = torch.nn.Parameter(
+                    torch.empty(
+                        token_num, tokem_dim, device=model.device, dtype=model.dtype
+                    )
+                )
+                model.model.embed_tokens.weight = torch.nn.Parameter(
+                    torch.empty(
+                        token_num, tokem_dim, device=model.device, dtype=model.dtype
+                    )
+                )
 
-            print('Loading additional LLaVA weights...')
-            if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
-                non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
+            print("Loading additional LLaVA weights...")
+            if os.path.exists(os.path.join(model_path, "non_lora_trainables.bin")):
+                non_lora_trainables = torch.load(
+                    os.path.join(model_path, "non_lora_trainables.bin"),
+                    map_location="cpu",
+                )
             else:
                 # this is probably from HF Hub
                 from huggingface_hub import hf_hub_download
+
                 def load_from_hf(repo_id, filename, subfolder=None):
                     cache_file = hf_hub_download(
-                        repo_id=repo_id,
-                        filename=filename,
-                        subfolder=subfolder)
-                    return torch.load(cache_file, map_location='cpu')
-                non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
-            non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
-            if any(k.startswith('model.model.') for k in non_lora_trainables):
-                non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
+                        repo_id=repo_id, filename=filename, subfolder=subfolder
+                    )
+                    return torch.load(cache_file, map_location="cpu")
+
+                non_lora_trainables = load_from_hf(
+                    model_path, "non_lora_trainables.bin"
+                )
+            non_lora_trainables = {
+                (k[11:] if k.startswith("base_model.") else k): v
+                for k, v in non_lora_trainables.items()
+            }
+            if any(k.startswith("model.model.") for k in non_lora_trainables):
+                non_lora_trainables = {
+                    (k[6:] if k.startswith("model.") else k): v
+                    for k, v in non_lora_trainables.items()
+                }
             model.load_state_dict(non_lora_trainables, strict=False)
 
             from peft import PeftModel
-            print('Loading LoRA weights...')
+
+            print("Loading LoRA weights...")
             model = PeftModel.from_pretrained(model, model_path)
-            print('Merging LoRA weights...')
+            print("Merging LoRA weights...")
             model = model.merge_and_unload()
-            print('Model is loaded...')
+            print("Model is loaded...")
+        ## TODO @yunhao: mind fixing this
         elif model_base is not None:
             # this may be mm projector only
-            print('Loading LLaVA from base model...')
-            if 'mpt' in model_name.lower():
-                if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
-                    shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
+            print("Loading LLaVA from base model...")
+            cfg_pretrained = AutoConfig.from_pretrained(
+                model_path, trust_remote_code=True
+            )
+            mm_config_wrapper(config, kwargs)
+            if "mpt" in model_name.lower():
+                if not os.path.isfile(os.path.join(model_path, "configuration_mpt.py")):
+                    shutil.copyfile(
+                        os.path.join(model_base, "configuration_mpt.py"),
+                        os.path.join(model_path, "configuration_mpt.py"),
+                    )
                 tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-                cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
-                model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
+                model = LlavaMPTForCausalLM.from_pretrained(
+                    model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs
+                )
             else:
-                tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False, legacy=False)
-                cfg_pretrained = AutoConfig.from_pretrained(model_path)
-                model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
-
-            mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
-            mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
-            model.load_state_dict(mm_projector_weights, strict=False)
+                tokenizer = AutoTokenizer.from_pretrained(
+                    model_base, use_fast=False, legacy=False
+                )
+                model = LlavaLlamaForCausalLM.from_pretrained(
+                    model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs
+                )
         else:
-            if 'mpt' in model_name.lower():
-                config = AutoConfig.from_pretrained(model_path)
-                # config._attn_implementation = "flash_attention_2"
-                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-                model = LlavaMPTForCausalLM.from_pretrained(model_path, config=config, low_cpu_mem_usage=True, **kwargs)
-            elif 'mistral' in model_name.lower() or 'mixtral' in model_name.lower():
-                config = AutoConfig.from_pretrained(model_path)
-                # config._attn_implementation = "flash_attention_2"
-                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, legacy=False)
-                model = LlavaMistralForCausalLM.from_pretrained(model_path, config=config, low_cpu_mem_usage=True, **kwargs)
+            config = AutoConfig.from_pretrained(model_path)
+            config.resume_path = model_path
+            prepare_config_for_eval(config, kwargs)
+            if "mpt" in model_name.lower():
+                model = LlavaMPTForCausalLM.from_pretrained(
+                    model_path, config=config, low_cpu_mem_usage=True, **kwargs
+                )
+            elif "mistral" in model_name.lower() or "mixtral" in model_name.lower():
+                model = LlavaMistralForCausalLM.from_pretrained(
+                    model_path, config=config, low_cpu_mem_usage=True, **kwargs
+                )
+            elif "gemma" in model_name.lower():
+                model = LlavaGemmaForCausalLM.from_pretrained(
+                    model_path, config=config, low_cpu_mem_usage=True, **kwargs
+                )
             else:
                 # kentang-mit@: llama-2 model
-                config = LlamaConfig.from_pretrained(model_path)
                 # config._attn_implementation = "flash_attention_2"
-                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, legacy=False)
-                model = LlavaLlamaForCausalLM.from_pretrained(model_path, config=config, low_cpu_mem_usage=True, **kwargs)
+                model = LlavaLlamaModel(
+                    config=config,
+                    low_cpu_mem_usage=True,
+                    **kwargs
+                )
+            tokenizer = model.tokenizer
     else:
         # Load language model
         if model_base is not None:
             # PEFT model
             from peft import PeftModel
+
             tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
-            model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
+            model = AutoModelForCausalLM.from_pretrained(
+                model_base, low_cpu_mem_usage=True, **kwargs
+            )
             print(f"Loading LoRA weights from {model_path}")
             model = PeftModel.from_pretrained(model, model_path)
             print(f"Merging weights")
             model = model.merge_and_unload()
-            print('Convert to FP16...')
+            print("Convert to FP16...")
             model.to(torch.float16)
         else:
-            use_fast = False
-            if 'mpt' in model_name.lower():
+            if "mpt" in model_name.lower():
                 tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
+                model = AutoModelForCausalLM.from_pretrained(
+                    model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs
+                )
             else:
-                tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, legacy=False)
-                model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-
+                tokenizer = AutoTokenizer.from_pretrained(
+                    model_path, use_fast=False, legacy=False
+                )
+                model = AutoModelForCausalLM.from_pretrained(
+                    model_path, low_cpu_mem_usage=True, **kwargs
+                )
+    model.eval()
     image_processor = None
-
     if is_mm_model(model_path):
         mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
         mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
         if mm_use_im_patch_token:
             tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
         if mm_use_im_start_end:
-            tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+            tokenizer.add_tokens(
+                [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True
+            )
         model.resize_token_embeddings(len(tokenizer))
-
         vision_tower = model.get_vision_tower()
-        if not vision_tower.is_loaded:
-            vision_tower.load_model()
         vision_tower.to(device=device, dtype=torch.float16)
+        mm_projector = model.get_mm_projector()
+        mm_projector.to(device=device, dtype=torch.float16)
         image_processor = vision_tower.image_processor
 
-    if hasattr(model.config, "max_sequence_length"):
+    if hasattr(model.llm.config, "max_sequence_length"):
         context_len = model.config.max_sequence_length
     else:
         context_len = 2048
 
     return tokenizer, model, image_processor, context_len
+
+def parse_model_name_or_path(config: PretrainedConfig, model_name="llm", suffix="_cfg"):
+    target_model = f"{model_name}{suffix}"
+    target_cfg = getattr(config, target_model, None)
+    
+    if isinstance(target_cfg, str):
+        return target_cfg
+    elif isinstance(target_cfg, dict):
+        return target_cfg["architectures"][0]
+    else:
+        raise ValueError(f"Invalid {target_model} configuration!")
+
+def prepare_config_for_eval(config: PretrainedConfig, kwargs: dict):
+    try:
+        # compatible with deprecated config convention
+        if getattr(config, "vision_tower_cfg", None) is None:
+            config.vision_tower_cfg = config.mm_vision_tower
+    except AttributeError:
+        raise ValueError(f"Invalid configuration! Cannot find vision_tower in config:\n{config}")
+    
+    config.model_dtype = kwargs.pop("torch_dtype").__str__()
+    # siglip does not support device_map = "auto"
+    vision_tower_name = parse_model_name_or_path(config, "vision_tower")
+    if "siglip" in vision_tower_name.lower():
+        kwargs["device_map"] = "cuda"
\ No newline at end of file
diff --git a/llava/model/configuration_llava.py b/llava/model/configuration_llava.py
new file mode 100644
index 00000000..16240f69
--- /dev/null
+++ b/llava/model/configuration_llava.py
@@ -0,0 +1,51 @@
+from transformers import PretrainedConfig
+
+
+class LlavaConfig(PretrainedConfig):
+    model_type = "llava"
+
+    def __init__(
+        self,
+        llm_cfg=None,
+        vision_tower_cfg=None,
+        mm_projector_cfg=None,
+        architectures=None,
+        resume_path=None,
+        hidden_size=None,
+        mm_hidden_size=None,
+        image_aspect_ratio=None,
+        num_video_frames=None,
+        mm_vision_select_layer=None,
+        mm_vision_select_feature=None,
+        mm_use_im_start_end=False,
+        mm_use_im_patch_token=True,
+        mm_projector_lr=None,
+        vision_resolution=None,
+        interpolate_mode=None,
+        s2=None,
+        s2_scales=None,
+        s2_max_split_size=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.architectures = architectures
+        self.llm_cfg = llm_cfg
+        self.vision_tower_cfg = vision_tower_cfg
+        self.mm_projector_cfg = mm_projector_cfg
+        self.resume_path = resume_path
+        
+        self.hidden_size = hidden_size
+        self.mm_hidden_size = mm_hidden_size
+        self.image_aspect_ratio = image_aspect_ratio
+        self.num_video_frames = num_video_frames
+        self.mm_vision_select_layer = mm_vision_select_layer
+        self.mm_vision_select_feature = mm_vision_select_feature
+        self.mm_use_im_start_end = mm_use_im_start_end
+        self.mm_use_im_start_end = mm_use_im_start_end
+        self.mm_use_im_patch_token = mm_use_im_patch_token
+        self.mm_projector_lr = mm_projector_lr
+        self.vision_resolution = vision_resolution
+        self.interpolate_mode = interpolate_mode
+        self.s2 = s2
+        self.s2_scales = s2_scales
+        self.s2_max_split_size = s2_max_split_size
diff --git a/llava/model/language_model/builder.py b/llava/model/language_model/builder.py
new file mode 100644
index 00000000..25a2549e
--- /dev/null
+++ b/llava/model/language_model/builder.py
@@ -0,0 +1,111 @@
+import math
+import warnings
+import os, os.path as osp
+import torch
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoConfig,
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+)
+
+
+def has_tokenizer(path):
+    if (
+        osp.exists(osp.join(path, "special_tokens_map.json"))
+        and osp.exists(osp.join(path, "tokenizer_config.json"))
+        and (osp.exists(osp.join(path, "tokenizer.model")) or osp.exists(osp.join(path, "tokenizer.json")))
+    ):
+        # print("[has_tokenizer]", path, True)
+        return True
+    from huggingface_hub import HfApi, file_exists
+    from huggingface_hub.utils import validate_repo_id, HFValidationError
+    api = HfApi()
+    try:
+        valid_hf_repo = api.repo_exists(path)
+    except HFValidationError as e:
+        valid_hf_repo = False
+    if (
+        valid_hf_repo
+        and file_exists(path, "special_tokens_map.json")
+        and file_exists(path, "tokenizer_config.json")
+        and (file_exists(path, "tokenizer.model") or file_exists(path, "tokenizer.json"))
+    ):
+        # print("[has_tokenizer]", path, True)
+        return True
+    # print("[has_tokenizer]", path, False)
+    return False
+
+
+def context_length_extension(config):
+    orig_ctx_len = getattr(config, "max_position_embeddings", None)
+    model_max_length = getattr(config, "model_max_length", None)
+    if orig_ctx_len and model_max_length > orig_ctx_len:
+        print(f"Scaling RoPE from {orig_ctx_len} to {model_max_length}")
+        scaling_factor = float(math.ceil(model_max_length / orig_ctx_len))
+        config.rope_scaling = {"type": "linear", "factor": scaling_factor}
+    return config
+
+
+def build_llm_and_tokenizer(
+    model_name_or_path: str,
+    config: PretrainedConfig,
+    # config_cls: PretrainedConfig = None,
+    # llm_cls: PreTrainedModel = None,
+    attn_implementation=None,
+    model_max_length=None,
+    *args,
+    **kwargs,
+) -> PreTrainedModel:
+    # if config_cls is None:
+    #     config_cls = AutoConfig
+    # if llm_cls is None:
+    #     llm_cls = AutoModelForCausalLM
+    # config_cls = AutoConfig
+    # llm_cls = AutoModelForCausalLM
+    ## extra configuration for llm
+    # print("build_llm_and_tokenizer():", model_name_or_path); input("DEBUG")
+    llm_cfg = AutoConfig.from_pretrained(model_name_or_path)
+    llm_cfg._attn_implementation = attn_implementation
+    llm_cfg.model_max_length = model_max_length
+    if model_max_length is not None:
+        context_length_extension(llm_cfg)
+
+    llm = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path, config=llm_cfg, torch_dtype=eval(config.model_dtype), *args, **kwargs
+    )
+    
+    llm_path = model_name_or_path
+    if not has_tokenizer(llm_path):
+        warnings.warn("tokenizer found in VLM root folder. Move to ./{VILA}/llm in the future.")
+        llm_path = osp.join(llm_path, "llm")
+    
+    # TODO(ligeng): use LLM class to judge to better compability.
+    if "mpt" in model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            llm_path, 
+            model_max_length=llm_cfg.model_max_length,
+            padding_side="right",
+        )
+    elif "yi" in model_name_or_path.lower():
+        tokenizer = AutoTokenizer.from_pretrained(
+            llm_path,
+            model_max_length=llm_cfg.model_max_length,
+            padding_side="right",
+            use_fast=False,
+        )
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(
+            llm_path, 
+            model_max_length=llm_cfg.model_max_length,
+            padding_side="right",
+            use_fast=False,
+            legacy=False,
+        )
+        
+    # TODO(ligeng): is this necessary for llava?
+    config.hidden_size = llm.config.hidden_size
+    return llm, tokenizer
\ No newline at end of file
diff --git a/llava/model/language_model/llava_mixtral.py b/llava/model/language_model/llava_gemma.py
similarity index 56%
rename from llava/model/language_model/llava_mixtral.py
rename to llava/model/language_model/llava_gemma.py
index fd01107c..6b9bf118 100644
--- a/llava/model/language_model/llava_mixtral.py
+++ b/llava/model/language_model/llava_gemma.py
@@ -1,53 +1,51 @@
-# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#    Copyright 2023 Haotian Liu
 #
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
 #
-#      http://www.apache.org/licenses/LICENSE-2.0
+#        http://www.apache.org/licenses/LICENSE-2.0
 #
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# SPDX-License-Identifier: Apache-2.0
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
 
-# This file is modified from https://github.com/haotian-liu/LLaVA/
+PAD_TOKEN_ID = 0
 
 from typing import List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
 
-from transformers import AutoConfig, AutoModelForCausalLM, \
-                         MixtralConfig, MixtralModel, MixtralForCausalLM
+from transformers import AutoConfig, AutoModelForCausalLM
+from transformers.models.gemma import GemmaConfig, GemmaModel, GemmaForCausalLM
 
 from transformers.modeling_outputs import CausalLMOutputWithPast
-
+from llava.constants import IGNORE_INDEX
 from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
+# import time
 
 
-class LlavaMixtralConfig(MixtralConfig):
-    model_type = "llava_mixtral"
-    pretraining_tp = 1
+class LlavaGemmaConfig(GemmaConfig):
+    model_type = "llava_gemma"
 
 
-class LlavaMixtralModel(LlavaMetaModel, MixtralModel):
-    config_class = LlavaMixtralConfig
+class LlavaGemmaModel(GemmaModel, LlavaMetaModel):
+    config_class = LlavaGemmaConfig
 
-    def __init__(self, config: MixtralConfig):
-        super(LlavaMixtralModel, self).__init__(config)
+    def __init__(self, config: GemmaConfig):
+        super(LlavaGemmaModel, self).__init__(config)
 
 
-class LlavaMixtralForCausalLM(MixtralForCausalLM, LlavaMetaForCausalLM):
-    config_class = LlavaMixtralConfig
+class LlavaGemmaForCausalLM(GemmaForCausalLM, LlavaMetaForCausalLM):
+    config_class = LlavaGemmaConfig
 
     def __init__(self, config):
-        super(MixtralForCausalLM, self).__init__(config)
-        self.model = LlavaMixtralModel(config)
-        self.pretraining_tp = config.pretraining_tp
+        super(LlavaGemmaForCausalLM, self).__init__(config)
+        self.model = LlavaGemmaModel(config)
+        self.pretraining_tp = 1
         self.vocab_size = config.vocab_size
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
@@ -56,6 +54,9 @@ def __init__(self, config):
 
     def get_model(self):
         return self.model
+    
+    def get_lm_head(self):
+        return self.lm_head
 
     def forward(
         self,
@@ -66,6 +67,7 @@ def forward(
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
         use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         images: Optional[torch.FloatTensor] = None,
@@ -87,6 +89,8 @@ def forward(
                 labels,
                 images
             )
+        # TODO (kentang-mit@): fuse this function into the previous one.
+        # current design makes unit-test easier.
         if self.training:
             (
                 _,
@@ -106,13 +110,27 @@ def forward(
             )
             new_input_ids = None
             past_key_values = None
+            new_cache_position = None
         else:
             new_attention_mask = attention_mask
             new_position_ids = position_ids
             new_inputs_embeds = inputs_embeds
             new_labels = labels
-            sorted_seqlens_in_batch = attention_mask.sum(-1).int()
+            if attention_mask is not None:
+                sorted_seqlens_in_batch = attention_mask.sum(-1).int()
+            else:
+                sorted_seqlens_in_batch = None
             new_input_ids = input_ids
+            # kentang-mit@: This only works for batch=1 currently
+            # model.generate of gemma does not correctly handle decoding stage currently
+            # need to manually adjust decoding stage input = 1 token
+            if past_key_values is not None:
+                if new_inputs_embeds is not None:
+                    new_inputs_embeds = new_inputs_embeds[:, [-1]]
+                # kentang-mit@: seems to be a problem unique to gemma
+                if new_position_ids is not None:
+                    new_position_ids = new_position_ids[:, [-1]]
+            new_cache_position = new_position_ids[0]
 
         outputs = super().forward(
             input_ids=new_input_ids,
@@ -122,6 +140,7 @@ def forward(
             inputs_embeds=new_inputs_embeds,
             labels=new_labels,
             use_cache=use_cache,
+            cache_position=new_cache_position,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
@@ -138,5 +157,5 @@ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_
             _inputs['images'] = images
         return _inputs
 
-AutoConfig.register("llava_mixtral", LlavaMixtralConfig)
-AutoModelForCausalLM.register(LlavaMixtralConfig, LlavaMixtralForCausalLM)
+AutoConfig.register("llava_gemma", LlavaGemmaConfig)
+AutoModelForCausalLM.register(LlavaGemmaConfig, LlavaGemmaForCausalLM)
diff --git a/llava/model/language_model/llava_llama.py b/llava/model/language_model/llava_llama.py
index bf359d9e..b2db1cb2 100644
--- a/llava/model/language_model/llava_llama.py
+++ b/llava/model/language_model/llava_llama.py
@@ -15,55 +15,71 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
 
 
-PAD_TOKEN_ID = 0
-
 from typing import List, Optional, Tuple, Union
-
+import os, os.path as osp
 import torch
-import torch.nn as nn
 
-from transformers import AutoConfig, AutoModelForCausalLM, \
-                         LlamaConfig, LlamaModel, LlamaForCausalLM
+from transformers import (
+    LlamaForCausalLM,
+    LlamaConfig,
+    PreTrainedModel,
+    AutoConfig,
+    AutoModel,
+    GenerationConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+)
 
 from transformers.modeling_outputs import CausalLMOutputWithPast
-from llava.constants import IGNORE_INDEX
 from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
-# import time
+from ..multimodal_encoder.builder import build_vision_tower
+from ..multimodal_projector.builder import build_mm_projector
+from ..configuration_llava import LlavaConfig
+from ..utils import get_model_config
+from .builder import build_llm_and_tokenizer
 
 
-class LlavaConfig(LlamaConfig):
+class LlavaLlamaConfig(LlavaConfig):
     model_type = "llava_llama"
 
-
-class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
-    config_class = LlavaConfig
-
-    def __init__(self, config: LlamaConfig):
-        super(LlavaLlamaModel, self).__init__(config)
-
-
-class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
-    """This class is originally implemented by the LLaVA team and
-    modified by Jason Lu and Haotian Tang based on Ji Lin's implementation
-    to support flash attention with input packing."""
-    config_class = LlavaConfig
-
-    def __init__(self, config):
-        super(LlamaForCausalLM, self).__init__(config)
-        self.model = LlavaLlamaModel(config)
-        self.pretraining_tp = config.pretraining_tp
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_model(self):
-        return self.model
+## FIXME we will follow the convention to add a new class for CausalLM in the future
+class LlavaLlamaModel(LlavaMetaModel, LlavaMetaForCausalLM, PreTrainedModel):
+    config_class = LlavaLlamaConfig
+    main_input_name = "input_embeds"
+    supports_gradient_checkpointing = True
+    
+    def __init__(self, config: LlavaLlamaConfig = None, *args, **kwargs) -> None:
+        super().__init__(config)
+        return self.init_vlm(config=config, *args, **kwargs)
+        
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ):
+        if hasattr(cls, "load_pretrained"):
+            return cls.load_pretrained(pretrained_model_name_or_path, 
+                *model_args, config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, force_download=force_download, local_files_only=local_files_only, token=token, 
+                revision=revision, use_safetensors=use_safetensors, **kwargs
+            )
+        return super(LlavaLlamaModel).from_pretrained(pretrained_model_name_or_path, 
+            *model_args, config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, force_download=force_download, local_files_only=local_files_only, token=token, 
+            revision=revision, use_safetensors=use_safetensors, **kwargs)    
 
     def forward(
         self,
         input_ids: torch.LongTensor = None,
+        images: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[List[torch.FloatTensor]] = None,
@@ -72,9 +88,9 @@ def forward(
         use_cache: Optional[bool] = None,
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
-        images: Optional[torch.FloatTensor] = None,
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
+        self.freezed_module_patch()
         if inputs_embeds is None:
             (
                 input_ids,
@@ -82,14 +98,9 @@ def forward(
                 attention_mask,
                 past_key_values,
                 inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
                 labels,
-                images
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids, position_ids, attention_mask, past_key_values, labels, images
             )
         # Note (kentang-mit@): we have a unit test for this function.
         if self.training:
@@ -100,14 +111,14 @@ def forward(
                 _,
                 new_inputs_embeds,
                 new_labels,
-                sorted_seqlens_in_batch
+                sorted_seqlens_in_batch,
             ) = self.repack_multimodal_data(
                 input_ids,
                 position_ids,
                 attention_mask,
                 past_key_values,
                 inputs_embeds,
-                labels
+                labels,
             )
             new_input_ids = None
             past_key_values = None
@@ -118,11 +129,8 @@ def forward(
             new_labels = labels
             sorted_seqlens_in_batch = attention_mask.sum(-1).int()
             new_input_ids = input_ids
-        # ed = time.time()
-        # print(inputs_embeds.device, "preparation time:", ed - st, "s.")
 
-        #st = time.time()
-        outputs = super().forward(
+        outputs = self.llm.forward(
             input_ids=new_input_ids,
             attention_mask=new_attention_mask,
             position_ids=new_position_ids,
@@ -136,15 +144,37 @@ def forward(
             seqlens_in_batch=sorted_seqlens_in_batch,
         )
         return outputs
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
-        images = kwargs.pop("images", None)
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
-        )
+    
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: Optional[torch.FloatTensor] = None,
+        images: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,
+        **generation_kwargs,
+    ):
         if images is not None:
-            _inputs['images'] = images
-        return _inputs
+            (
+                _,
+                _,
+                attention_mask,
+                _,
+                inputs_embeds,
+                _,
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids, None, attention_mask, None, None, images
+            )
+        else:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+        inputs_embeds = inputs_embeds.to(self.dtype)
+        
+        outputs = self.llm.generate(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            **generation_kwargs
+        )
+        return outputs
+        
 
-AutoConfig.register("llava_llama", LlavaConfig)
-AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)
+AutoConfig.register("llava_llama", LlavaLlamaConfig)
+AutoModel.register(LlavaLlamaConfig, LlavaLlamaModel)
diff --git a/llava/model/language_model/llava_mistral.py b/llava/model/language_model/llava_mistral.py
index 077e3cd3..cac461d2 100644
--- a/llava/model/language_model/llava_mistral.py
+++ b/llava/model/language_model/llava_mistral.py
@@ -24,6 +24,8 @@
 from transformers import AutoConfig, AutoModelForCausalLM, \
                          MistralConfig, MistralModel, MistralForCausalLM
 
+from .modeling_mixtral_long_context import MixtralModel, MixtralForCausalLM
+
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
@@ -34,7 +36,7 @@ class LlavaMistralConfig(MistralConfig):
     pretraining_tp = 1
 
 
-class LlavaMistralModel(LlavaMetaModel, MistralModel):
+class LlavaMistralModel(MistralModel, LlavaMetaModel):
     config_class = LlavaMistralConfig
 
     def __init__(self, config: MistralConfig):
@@ -56,6 +58,9 @@ def __init__(self, config):
 
     def get_model(self):
         return self.model
+    
+    def get_lm_head(self):
+        return self.lm_head
 
     def forward(
         self,
diff --git a/llava/model/language_model/llava_mpt.py b/llava/model/language_model/llava_mpt.py
index 504261ef..71f70360 100644
--- a/llava/model/language_model/llava_mpt.py
+++ b/llava/model/language_model/llava_mpt.py
@@ -33,7 +33,7 @@ class LlavaMPTConfig(MPTConfig):
     model_type = "llava_mpt"
 
 
-class LlavaMPTModel(LlavaMetaModel, MPTModel):
+class LlavaMPTModel(MPTModel, LlavaMetaModel):
     config_class = LlavaMPTConfig
 
     def __init__(self, config: MPTConfig):
diff --git a/llava/model/llava_arch.py b/llava/model/llava_arch.py
index 2bb58582..dfc070eb 100644
--- a/llava/model/llava_arch.py
+++ b/llava/model/llava_arch.py
@@ -12,124 +12,291 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 
-
-from abc import ABC, abstractmethod
-
-import torch
-import torch.nn as nn
-
+import os, sys, os.path as osp
 import warnings
-from .multimodal_encoder.builder import build_vision_tower
-from .multimodal_projector.builder import build_vision_projector
-
-from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from abc import ABC, abstractmethod
 
-class LlavaMetaModel:
+import torch, logging
+
+from transformers import (
+    AutoTokenizer,
+    AutoModel,
+    AutoModelForCausalLM,
+    AutoConfig,
+    BitsAndBytesConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+)
+
+from llava.constants import (
+    DEFAULT_IM_END_TOKEN,
+    DEFAULT_IM_START_TOKEN,
+    DEFAULT_IMAGE_PATCH_TOKEN,
+    IGNORE_INDEX,
+    IMAGE_TOKEN_INDEX,
+)
+
+from llava.train.utils import (
+    get_checkpoint_path,
+    prepare_config_for_training,
+    vision_resolution_elevation,
+    unit_test_rope_scaling,
+)
+
+from collections import OrderedDict
+from llava.model.utils import get_model_config
+from llava.model.language_model.builder import build_llm_and_tokenizer
+from llava.model.multimodal_encoder.builder import build_vision_tower
+from llava.model.multimodal_projector.builder import build_mm_projector
+from llava.model.configuration_llava import LlavaConfig
+
+from transformers.modeling_utils import ContextManagers, no_init_weights
+
+## TODO decide whether should we use metaclass
+class LlavaMetaModel(ABC):
+    def init_vlm(self, config: PreTrainedModel = None, *args, **kwargs):
+        # TODO(ligeng): figure out how from_config and from_pretrained works in HF implementation.
+        if hasattr(self, "llm") or hasattr(self, "vision_tower")  or hasattr(self, "mm_projector"):
+            # already initialized, skipped
+            return 
+        
+        model_dtype = getattr(config, "model_dtype", "torch.float16")
+        if not hasattr(config, "model_dtype"):
+            warnings.warn("model_dtype not found in config, defaulting to torch.float16.")
+            config.model_dtype = model_dtype
+        
+        # print("init_vlm(): config", config); input("DEBUG init_vlm")
+        cfgs = get_model_config(config)
+        if len(cfgs) == 3:
+            llm_cfg, vision_tower_cfg, mm_projector_cfg = cfgs
+        else:
+            raise ValueError("`llm_cfg` `mm_projector_cfg` `vision_tower_cfg` not found in the config.")
+        # print("init_vlm():", cfgs); input("DEBUG init_vlm")
+        # print(llm_cfg, vision_tower_cfg, mm_projector_cfg); input("DEBUG init_vlm")
+        self.llm, self.tokenizer = build_llm_and_tokenizer(llm_cfg, config, *args, **kwargs)
+        self.vision_tower = build_vision_tower(vision_tower_cfg, config)
+        self.mm_projector = build_mm_projector(mm_projector_cfg, config)
+
+        self.post_config()
+        self.is_loaded = True
+
+        assert (
+            self.llm is not None or self.vision_tower is not None or self.mm_projector is not None
+        ), "At least one of the components must be instantiated."
+    
+    @classmethod
+    def load_from_config(cls, model_path_or_config, *args, **kwargs):
+        pass
+    
+    ## FIXME we will use this function to load model in the future
+    @classmethod
+    def load_pretrained(cls, model_path_or_config, *args, **kwargs):
+        kwargs.pop("config", None)
+
+        if isinstance(model_path_or_config, str):
+            config = AutoConfig.from_pretrained(model_path_or_config)
+        elif isinstance(model_path_or_config, LlavaConfig):
+            config = model_path_or_config
+        else:
+            raise NotImplementedError(f"wrong type, {type(model_path_or_config)} \
+                                      {isinstance(model_path_or_config, LlavaConfig)}")
 
-    def __init__(self, config):
-        super(LlavaMetaModel, self).__init__(config)
+        model_dtype = getattr(config, "model_dtype", "torch.float16")
+        if not hasattr(config, "model_dtype"):
+            warnings.warn("model_dtype not found in config, defaulting to torch.float16.")
+            config.model_dtype = model_dtype
+        
+        cfgs = get_model_config(config)
+        if len(cfgs) == 3:
+            llm_cfg, vision_tower_cfg, mm_projector_cfg = cfgs
+        else:
+            raise ValueError("`llm_cfg` `mm_projector_cfg` `vision_tower_cfg` not found in the config.")
 
-        if hasattr(config, "mm_vision_tower"):
-            self.vision_tower = build_vision_tower(config, delay_load=True)
-            self.mm_projector = build_vision_projector(config)
+        # print(llm_cfg, vision_tower_cfg, mm_projector_cfg); input("DEBUG load_pretrained")
+        with ContextManagers([no_init_weights(_enable=True),]):
+            vlm = cls(config, *args, **kwargs)
+        # print(llm_cfg, vision_tower_cfg, mm_projector_cfg); input("DEBUG load_pretrained finish")
+        
+        if hasattr(vlm, "llm") or hasattr(vlm, "vision_tower")  or hasattr(vlm, "mm_projector"):
+            if vlm.is_loaded:
+                return vlm
+        
+        vlm.llm, vlm.tokenizer = build_llm_and_tokenizer(llm_cfg, config, *args, **kwargs)
+        vlm.vision_tower = build_vision_tower(vision_tower_cfg, config)
+        vlm.mm_projector = build_mm_projector(mm_projector_cfg, config)
+
+        self.post_config()
+        self.is_loaded = True
+
+        # FIXME(ligeng, yunhao): llm should never be none here.
+        assert (
+            vlm.llm is not None or vlm.vision_tower is not None or vlm.mm_projector is not None
+        ), "At least one of the components must be instantiated."
+        return vlm
+    
+    ## FIXME we will use this function to save the model in the future
+    def save_pretrained(self, output_dir, state_dict=None):
+        if state_dict is None:
+            # other wise fetch from deepspeed
+            # state_dict = accelerator.get_state_dict(is_deepspeed_enabled)
+            state_dict = self.state_dict()
+        
+        if getattr(self, "tokenizer", None):
+            self.tokenizer.save_pretrained(osp.join(output_dir, "llm"))
+
+        if self.get_llm():
+            print(f"saving llm to {osp.join(output_dir, 'llm')}")
+            self.llm.config._name_or_path = osp.join(output_dir, "llm")
+            llm_state_dict = OrderedDict({k.split("llm.")[-1]: v for k, v in state_dict.items() if "llm" in k})
+            self.llm.save_pretrained(os.path.join(output_dir, "llm"), state_dict=llm_state_dict)
+            self.config.llm_cfg = self.llm.config
+
+        if self.get_vision_tower() and "radio" not in self.get_vision_tower().__class__.__name__.lower():
+            print(f"saving vision_tower to {osp.join(output_dir, 'vision_tower')}")
+            self.vision_tower.config._name_or_path = osp.join(output_dir, "vision_tower")
+            vision_tower_state_dict = OrderedDict(
+                {k.split("vision_tower.vision_tower.")[-1]: v for k, v in state_dict.items() if "vision_tower" in k}
+            )
+            self.vision_tower.vision_tower.save_pretrained(
+                os.path.join(output_dir, "vision_tower"),
+                state_dict=vision_tower_state_dict,
+            )
+            self.vision_tower.image_processor.save_pretrained(os.path.join(output_dir, "vision_tower"))
+            self.config.vision_tower_cfg = self.vision_tower.config
+            if hasattr(self.config.vision_tower_cfg, 'auto_map'):
+                delattr(self.config.vision_tower_cfg, 'auto_map')
+
+        if self.get_mm_projector():
+            print(f"saving mm_projector to {osp.join(output_dir, 'mm_projector')}")
+            self.mm_projector.config._name_or_path = osp.join(output_dir, "mm_projector")
+            mm_projector_state_dict = OrderedDict(
+                {k.split("mm_projector.")[-1]: v for k, v in state_dict.items() if "mm_projector" in k}
+            )
+            self.mm_projector.save_pretrained(
+                os.path.join(output_dir, "mm_projector"),
+                state_dict=mm_projector_state_dict,
+            )
+            self.config.mm_projector_cfg = self.mm_projector.config
+        ## update and save top-level config
+        self.config._name_or_path = output_dir
+        self.config.architectures = [self.__class__.__name__]
+        self.config.save_pretrained(output_dir)
+   
+
+    def get_llm(self):
+        llm = getattr(self, "llm", None)
+        if type(llm) is list:
+            llm = llm[0]
+        return llm
+
+    def get_lm_head(self):
+        lm_head = getattr(self.get_llm(), "lm_head", None)
+        return lm_head
 
     def get_vision_tower(self):
-        vision_tower = getattr(self, 'vision_tower', None)
+        vision_tower = getattr(self, "vision_tower", None)
         if type(vision_tower) is list:
             vision_tower = vision_tower[0]
         return vision_tower
 
-    def initialize_vision_modules(self, model_args, fsdp=None):
-        vision_tower = model_args.vision_tower
-        mm_vision_select_layer = model_args.mm_vision_select_layer
-        mm_vision_select_feature = model_args.mm_vision_select_feature
-        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
-
-        self.config.mm_vision_tower = vision_tower
-
-        if self.get_vision_tower() is None:
-            vision_tower = build_vision_tower(model_args)
-
-            if fsdp is not None and len(fsdp) > 0:
-                self.vision_tower = [vision_tower]
-            else:
-                self.vision_tower = vision_tower
-        else:
-            if fsdp is not None and len(fsdp) > 0:
-                vision_tower = self.vision_tower[0]
-            else:
-                vision_tower = self.vision_tower
-            vision_tower.load_model()
-
-        self.config.use_mm_proj = True
-        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
-        self.config.mm_hidden_size = vision_tower.hidden_size
-        self.config.mm_vision_select_layer = mm_vision_select_layer
-        self.config.mm_vision_select_feature = mm_vision_select_feature
+    def get_mm_projector(self):
+        mm_projector = getattr(self, "mm_projector", None)
+        if type(mm_projector) is list:
+            mm_projector = mm_projector[0]
+        return mm_projector
+
+    def post_config(self):
+        self.training = self.get_llm().training
+        ## configuration
+        if getattr(self.config, "llm_cfg", None) is None:
+            self.config.llm_cfg = self.llm.config
+        if getattr(self.config, "vision_tower_cfg", None) is None:
+            self.config.vision_tower_cfg = self.vision_tower.config
+        if getattr(self.config, "mm_projector_cfg", None) is None:
+            self.config.mm_projector_cfg = self.mm_projector.config
+
+    def freezed_module_patch(self):
+        '''
+        Huggingface will call model.train() at each training_step. To ensure the expected behaviors for modules like dropout, batchnorm, etc., we need to call model.eval() for the freezed modules.
+        '''
+        if self.training:
+            if self.get_llm() and not getattr(self.config, "tune_language_model", False):
+                logging.warning("Caution: Your LLM is currently in training mode, ensuring accurate gradient computation. Please be vigilant, particularly regarding BatchNorm and Dropout operations.")
+            if self.get_vision_tower() and not getattr(self.config, "tune_vision_tower", False):
+                self.get_vision_tower().eval()
+            if self.get_mm_projector() and not getattr(self.config, "tune_mm_projector", False):
+                self.get_mm_projector().eval()
+    
+    def encode_images(self, images):
+        image_features = self.get_vision_tower()(images)
+        image_features = self.get_mm_projector()(image_features)
+        return image_features
+    
+    ## @yunhao: is there a better way to handle function call and attributes for llm?
+    ## support beam search
+    def _temporary_reorder_cache(self, past_key_values, sorted_idx):
+        return self.get_llm()._temporary_reorder_cache(past_key_values, sorted_idx)
 
-        if getattr(self, 'mm_projector', None) is None:
-            self.mm_projector = build_vision_projector(self.config)
-        else:
-            # In case it is frozen by LoRA
-            for p in self.mm_projector.parameters():
-                p.requires_grad = True
+    def get_input_embeddings(self):
+        return self.get_llm().get_input_embeddings()
 
-        if pretrain_mm_mlp_adapter is not None:
-            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
-            def get_w(weights, keyword):
-                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
+    def get_output_embeddings(self):
+        return self.get_llm().get_output_embeddings()
 
-            try:
-                self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
-            except:
-                # Haotian: workaround
-                # The shape of mm_projector's weights can be all [0]s
-                pass
+    def resize_token_embeddings(self, embed_size):
+        self.get_llm().resize_token_embeddings(embed_size)
 
+    
 
 class LlavaMetaForCausalLM(ABC):
     """This class is originally implemented by the LLaVA team and
     modified by Haotian Tang and Jason Lu based on Ji Lin's implementation
     to support multiple images and input packing."""
 
-    @abstractmethod
-    def get_model(self):
-        pass
-
-    def get_vision_tower(self):
-        return self.get_model().get_vision_tower()
-
-    def encode_images(self, images):
-        image_features = self.get_model().get_vision_tower()(images)
-        image_features = self.get_model().mm_projector(image_features)
-        return image_features
-
+    ## TODO move the forward function here if there is no need to override it
     def prepare_inputs_labels_for_multimodal(
         self, input_ids, position_ids, attention_mask, past_key_values, labels, images
     ):
         vision_tower = self.get_vision_tower()
         if vision_tower is None or images is None or input_ids.shape[1] == 1:
-            if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
+            if (
+                past_key_values is not None
+                and vision_tower is not None
+                and images is not None
+                and input_ids.shape[1] == 1
+            ):
                 target_shape = past_key_values[-1][-1].shape[-2] + 1
-                attention_mask = torch.cat((attention_mask, torch.ones(
-                    (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
-                    dtype=attention_mask.dtype,
-                    device=attention_mask.device
-                )), dim=1)
+                attention_mask = torch.cat(
+                    (
+                        attention_mask,
+                        torch.ones(
+                            (
+                                attention_mask.shape[0],
+                                target_shape - attention_mask.shape[1],
+                            ),
+                            dtype=attention_mask.dtype,
+                            device=attention_mask.device,
+                        ),
+                    ),
+                    dim=1,
+                )
                 position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
-            return input_ids, position_ids, attention_mask, past_key_values, None, labels
-
-        if type(images) is list or images.ndim == 5:
-            concat_images = torch.cat([image for image in images], dim=0)
-            image_features = self.encode_images(concat_images)
-            split_sizes = [image.shape[0] for image in images]
-            image_features = torch.split(image_features, split_sizes, dim=0)
-            image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
-        else:
-            image_features = self.encode_images(images).to(self.device)
-        
+            return (
+                input_ids,
+                position_ids,
+                attention_mask,
+                past_key_values,
+                None,
+                labels,
+            )
+        # handle different image dtypes for packing
+        if type(images) is list:
+            images = torch.cat(images, dim=0)
+        elif images.ndim == 5:  # batch_size x seq_len x image_channels
+            images = images.flatten(0, 1)
+        image_features = self.encode_images(images).to(self.device)
         # Note (kentang-mit@): image start / end is not implemented here to support pretraining.
-        if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
+        if getattr(self.config, "turn_mm_projector", False) and getattr(self.config, "mm_use_im_start_end", False):
             raise NotImplementedError
 
         # Let's just add dummy tensors if they do not exist,
@@ -152,10 +319,15 @@ def prepare_inputs_labels_for_multimodal(
         input_ids_copy = input_ids.clone()
         # kentang-mit@: Otherwise tokenizer out of bounds. Embeddings of image tokens will not be used.
         input_ids_copy[input_ids_copy == IMAGE_TOKEN_INDEX] = 0
-        input_embeds = self.get_model().embed_tokens(input_ids_copy)
-
-        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
-        input_embeds_1 = [cur_input_embeds[cur_attention_mask] for cur_input_embeds, cur_attention_mask in zip(input_embeds, attention_mask)]
+        input_embeds = self.llm.model.embed_tokens(input_ids_copy)
+
+        input_ids = [
+            cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)
+        ]
+        input_embeds_1 = [
+            cur_input_embeds[cur_attention_mask]
+            for cur_input_embeds, cur_attention_mask in zip(input_embeds, attention_mask)
+        ]
         labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
 
         new_input_embeds = []
@@ -170,7 +342,7 @@ def prepare_inputs_labels_for_multimodal(
             num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
             if num_images == 0:
                 cur_image_features = image_features[0]
-                # cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
+                # cur_input_embeds_1 = self.get_llm().embed_tokens(cur_input_ids)
                 cur_input_embeds_1 = input_embeds_1[batch_idx]
                 cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
                 new_input_embeds.append(cur_input_embeds)
@@ -178,23 +350,24 @@ def prepare_inputs_labels_for_multimodal(
                 # kenang-mit@: we do not have placeholdr image for text-only data now.
                 # cur_image_idx += 1
                 continue
-            
+
             cur_input_embeds = input_embeds_1[batch_idx]
-            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
+            image_token_indices = (
+                [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
+            )
             cur_input_ids_noim = []
             cur_labels = labels[batch_idx]
             cur_labels_noim = []
             cur_input_embeds_no_im = []
             for i in range(len(image_token_indices) - 1):
-                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
-                cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
-                cur_input_embeds_no_im.append(cur_input_embeds[image_token_indices[i]+1:image_token_indices[i+1]])
+                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
+                cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
+                cur_input_embeds_no_im.append(cur_input_embeds[image_token_indices[i] + 1 : image_token_indices[i + 1]])
             split_sizes = [x.shape[0] for x in cur_labels_noim]
-            # cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
+            # cur_input_embeds = self.get_llm().embed_tokens(torch.cat(cur_input_ids_noim))
             # cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
             cur_new_input_embeds = []
             cur_new_labels = []
-
             for i in range(num_images + 1):
                 cur_new_input_embeds.append(cur_input_embeds_no_im[i])
                 cur_new_labels.append(cur_labels_noim[i])
@@ -202,16 +375,23 @@ def prepare_inputs_labels_for_multimodal(
                     cur_image_features = image_features[cur_image_idx]
                     cur_image_idx += 1
                     cur_new_input_embeds.append(cur_image_features)
-                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
+                    cur_new_labels.append(
+                        torch.full(
+                            (cur_image_features.shape[0],),
+                            IGNORE_INDEX,
+                            device=cur_labels.device,
+                            dtype=cur_labels.dtype,
+                        )
+                    )
 
             cur_new_input_embeds = torch.cat(cur_new_input_embeds)
             cur_new_labels = torch.cat(cur_new_labels)
 
             new_input_embeds.append(cur_new_input_embeds)
             new_labels.append(cur_new_labels)
-        
+
         # Truncate sequences to max length as image embeddings can make the sequence longer
-        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
+        tokenizer_model_max_length = getattr(self.llm.config, "tokenizer_model_max_length", None)
         if tokenizer_model_max_length is not None:
             if any(len(x) > tokenizer_model_max_length for x in new_input_embeds):
                 warnings.warn("Inputs truncated!")
@@ -222,30 +402,61 @@ def prepare_inputs_labels_for_multimodal(
         batch_size = len(new_input_embeds)
 
         new_input_embeds_padded = []
-        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
-        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
+        new_labels_padded = torch.full(
+            (batch_size, max_len),
+            IGNORE_INDEX,
+            dtype=new_labels[0].dtype,
+            device=new_labels[0].device,
+        )
+        attention_mask = torch.zeros(
+            (batch_size, max_len),
+            dtype=attention_mask.dtype,
+            device=attention_mask.device,
+        )
         position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
 
         for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
             cur_len = cur_new_embed.shape[0]
-            if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
-                new_input_embeds_padded.append(torch.cat((
-                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
-                    cur_new_embed
-                ), dim=0))
+            if getattr(self.llm.config, "tokenizer_padding_side", "right") == "left":
+                new_input_embeds_padded.append(
+                    torch.cat(
+                        (
+                            torch.zeros(
+                                (max_len - cur_len, cur_new_embed.shape[1]),
+                                dtype=cur_new_embed.dtype,
+                                device=cur_new_embed.device,
+                            ),
+                            cur_new_embed,
+                        ),
+                        dim=0,
+                    )
+                )
                 if cur_len > 0:
                     new_labels_padded[i, -cur_len:] = cur_new_labels
                     attention_mask[i, -cur_len:] = True
-                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+                    position_ids[i, -cur_len:] = torch.arange(
+                        0, cur_len, dtype=position_ids.dtype, device=position_ids.device
+                    )
             else:
-                new_input_embeds_padded.append(torch.cat((
-                    cur_new_embed,
-                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
-                ), dim=0))
+                new_input_embeds_padded.append(
+                    torch.cat(
+                        (
+                            cur_new_embed,
+                            torch.zeros(
+                                (max_len - cur_len, cur_new_embed.shape[1]),
+                                dtype=cur_new_embed.dtype,
+                                device=cur_new_embed.device,
+                            ),
+                        ),
+                        dim=0,
+                    )
+                )
                 if cur_len > 0:
                     new_labels_padded[i, :cur_len] = cur_new_labels
                     attention_mask[i, :cur_len] = True
-                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+                    position_ids[i, :cur_len] = torch.arange(
+                        0, cur_len, dtype=position_ids.dtype, device=position_ids.device
+                    )
 
         new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
 
@@ -261,11 +472,24 @@ def prepare_inputs_labels_for_multimodal(
 
         if _position_ids is None:
             position_ids = None
-        
-        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels 
+
+        return (
+            None,
+            position_ids,
+            attention_mask,
+            past_key_values,
+            new_input_embeds,
+            new_labels,
+        )
 
     def repack_multimodal_data(
-        self, input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels
+        self,
+        input_ids,
+        position_ids,
+        attention_mask,
+        past_key_values,
+        inputs_embeds,
+        labels,
     ):
         # kentang-mit@: reorder and repack (reduce computation overhead)
         # requires transformers replacement.
@@ -290,7 +514,12 @@ def repack_multimodal_data(
                 # remove padding on-the-fly
                 cur_inputs_embeds.append(inputs_embeds[sorted_idx[i]][attention_mask[sorted_idx[i]]])
                 # each item: num_tokens
-                cur_position_ids.append(torch.arange(cur_inputs_embeds[-1].shape[0], device=cur_inputs_embeds[-1].device))
+                cur_position_ids.append(
+                    torch.arange(
+                        cur_inputs_embeds[-1].shape[0],
+                        device=cur_inputs_embeds[-1].device,
+                    )
+                )
                 # each item: num_tokens
                 # remove padding on-the-fly
                 cur_labels.append(labels[sorted_idx[i]][attention_mask[sorted_idx[i]]])
@@ -301,40 +530,45 @@ def repack_multimodal_data(
                 # The current batch is too long. We will start a new batch.
                 cur_batch_len = cur_seqlen
                 cur_inputs_embeds = [inputs_embeds[sorted_idx[i]][attention_mask[sorted_idx[i]]]]
-                cur_position_ids = [torch.arange(cur_inputs_embeds[-1].shape[0], device=cur_inputs_embeds[-1].device)]
+                cur_position_ids = [
+                    torch.arange(
+                        cur_inputs_embeds[-1].shape[0],
+                        device=cur_inputs_embeds[-1].device,
+                    )
+                ]
                 cur_labels = [labels[sorted_idx[i]][attention_mask[sorted_idx[i]]]]
-        
+
         if len(cur_inputs_embeds):
             new_inputs_embeds.append(torch.cat(cur_inputs_embeds, 0))
             new_position_ids.append(torch.cat(cur_position_ids, 0))
             new_labels.append(torch.cat(cur_labels, 0))
-        
+
         # print(new_position_ids[0].device, [x.shape for x in new_inputs_embeds], [x.shape for x in new_labels], [x.shape for x in new_position_ids])
         # assert 0
         new_inputs_embeds = torch.nn.utils.rnn.pad_sequence(
-            new_inputs_embeds,
-            batch_first=True,
-            padding_value=self.pad_token_id)
-        
-        new_position_ids = torch.nn.utils.rnn.pad_sequence(
-            new_position_ids,
-            batch_first=True,
-            padding_value=IGNORE_INDEX)
-        
-        new_labels = torch.nn.utils.rnn.pad_sequence(
-            new_labels,
-            batch_first=True,
-            padding_value=IGNORE_INDEX)
-        
-        new_attention_mask = new_position_ids.ne(IGNORE_INDEX)
+            new_inputs_embeds, batch_first=True, padding_value=self.llm.pad_token_id
+        )
+
+        new_position_ids = torch.nn.utils.rnn.pad_sequence(new_position_ids, batch_first=True, padding_value=-1)
+
+        new_labels = torch.nn.utils.rnn.pad_sequence(new_labels, batch_first=True, padding_value=IGNORE_INDEX)
+        ## yunhao: it's currently a workaround to avoid errors for seq_len < 100
+        new_attention_mask = new_position_ids.ne(-1)
         # sanity check
         assert new_attention_mask.sum() == attention_mask.sum()
         # print(new_inputs_embeds.shape, (new_attention_mask.sum(1)))
         # print(sorted_seqlens_in_batch.device, sorted_seqlens_in_batch, new_attention_mask.sum(1))
 
         # return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
-        return None, new_position_ids, new_attention_mask, past_key_values, new_inputs_embeds, new_labels, sorted_seqlens_in_batch
-
+        return (
+            None,
+            new_position_ids,
+            new_attention_mask,
+            past_key_values,
+            new_inputs_embeds,
+            new_labels,
+            sorted_seqlens_in_batch,
+        )
 
     def initialize_vision_tokenizer(self, model_args, tokenizer):
         if model_args.mm_use_im_patch_token:
@@ -349,32 +583,26 @@ def initialize_vision_tokenizer(self, model_args, tokenizer):
                 input_embeddings = self.get_input_embeddings().weight.data
                 output_embeddings = self.get_output_embeddings().weight.data
 
-                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
-                    dim=0, keepdim=True)
-                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
-                    dim=0, keepdim=True)
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
 
                 input_embeddings[-num_new_tokens:] = input_embeddings_avg
                 output_embeddings[-num_new_tokens:] = output_embeddings_avg
-
-            if model_args.tune_mm_mlp_adapter:
-                for p in self.get_input_embeddings().parameters():
-                    p.requires_grad = True
-                for p in self.get_output_embeddings().parameters():
-                    p.requires_grad = False
-
+            ## TODO yunhao: handle cases for <im_st> <im_end>
             if model_args.pretrain_mm_mlp_adapter:
-                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
-                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location="cpu")
+                embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"]
                 assert num_new_tokens == 2
                 if input_embeddings.shape == embed_tokens_weight.shape:
                     input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
                 elif embed_tokens_weight.shape[0] == num_new_tokens:
                     input_embeddings[-num_new_tokens:] = embed_tokens_weight
                 else:
-                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+                    raise ValueError(
+                        f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}."
+                    )
         elif model_args.mm_use_im_patch_token:
-            if model_args.tune_mm_mlp_adapter:
+            if model_args.mm_projector:
                 for p in self.get_input_embeddings().parameters():
                     p.requires_grad = False
                 for p in self.get_output_embeddings().parameters():
diff --git a/llava/model/make_delta.py b/llava/model/make_delta.py
index 89ff5db2..b10b4a86 100644
--- a/llava/model/make_delta.py
+++ b/llava/model/make_delta.py
@@ -15,23 +15,32 @@
 def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id):
     print("Loading base model")
     base = AutoModelForCausalLM.from_pretrained(
-        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+        base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
+    )
 
     print("Loading target model")
     auto_upgrade(target_model_path)
-    target = AutoModelForCausalLM.from_pretrained(target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
+    target = AutoModelForCausalLM.from_pretrained(
+        target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True
+    )
 
     print("Calculating delta")
     for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"):
         if name not in base.state_dict():
-            assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
+            assert name in [
+                "model.mm_projector.weight",
+                "model.mm_projector.bias",
+            ], f"{name} not in base model"
             continue
         if param.data.shape == base.state_dict()[name].shape:
             param.data -= base.state_dict()[name]
         else:
-            assert name in ['model.embed_tokens.weight', 'lm_head.weight'], f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
+            assert name in [
+                "model.embed_tokens.weight",
+                "lm_head.weight",
+            ], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}"
             bparam = base.state_dict()[name]
-            param.data[:bparam.shape[0], :bparam.shape[1]] -= bparam
+            param.data[: bparam.shape[0], : bparam.shape[1]] -= bparam
 
     print("Saving delta")
     if hub_repo_id:
@@ -51,4 +60,6 @@ def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id):
     parser.add_argument("--hub-repo-id", type=str, default=None)
     args = parser.parse_args()
 
-    make_delta(args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id)
+    make_delta(
+        args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id
+    )
diff --git a/llava/model/multimodal_encoder/builder.py b/llava/model/multimodal_encoder/builder.py
index 24766968..488748d8 100644
--- a/llava/model/multimodal_encoder/builder.py
+++ b/llava/model/multimodal_encoder/builder.py
@@ -1,13 +1,53 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
 
 import os
-from .clip_encoder import CLIPVisionTower
+from transformers import AutoConfig, PretrainedConfig, PreTrainedModel
+from .clip_encoder import CLIPVisionTower, CLIPVisionTowerS2
+from .siglip_encoder import SiglipVisionTower, SiglipVisionTowerS2
+from .intern_encoder import InternVisionTower
+from .radio_encoder import RADIOVisionTower
 
+def build_vision_tower(
+    model_name_or_path: str, config: PretrainedConfig
+) -> PreTrainedModel:
+    ## skip vision tower instantiation
+    if model_name_or_path is None:
+        return None
 
-def build_vision_tower(vision_tower_cfg, **kwargs):
-    vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
-    is_absolute_path_exists = os.path.exists(vision_tower)
-    if is_absolute_path_exists or vision_tower.startswith("openai"):
-        return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
+    vision_tower_arch = None
+    if config.resume_path and "radio" not in model_name_or_path:
+        assert os.path.exists(
+            model_name_or_path
+        ), f"Resume vision tower path {model_name_or_path} does not exist!"
+        vision_tower_cfg = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
+        vision_tower_arch = vision_tower_cfg.architectures[0].lower()
+    vision_tower_name = (
+        vision_tower_arch if vision_tower_arch is not None else model_name_or_path
+    )
 
-    raise ValueError(f'Unknown vision tower: {vision_tower}')
+    use_s2 = getattr(config, 's2', False)
+
+    if "intern" in vision_tower_name.lower():
+        if hasattr(config, 'drop_path_rate'):
+            vision_tower = InternVisionTower(
+                model_name_or_path, config=config, drop_path_rate=config.drop_path_rate)
+        else:
+            vision_tower = InternVisionTower(
+                model_name_or_path, config=config, drop_path_rate=0.0)
+    elif "radio" in vision_tower_name:
+        vision_tower = RADIOVisionTower(model_name_or_path, config)
+    elif "clip" in vision_tower_name:
+        if use_s2:
+            vision_tower = CLIPVisionTowerS2(model_name_or_path, config)
+        else:
+            vision_tower = CLIPVisionTower(model_name_or_path, config)
+    elif "siglip" in vision_tower_name:
+        if use_s2:
+            vision_tower = SiglipVisionTowerS2(model_name_or_path, config)
+        else:
+            vision_tower = SiglipVisionTower(model_name_or_path, config)
+    else:
+        raise ValueError(f"Unknown vision tower: {model_name_or_path}")
+
+    config.mm_hidden_size = vision_tower.config.hidden_size if not use_s2 else vision_tower.hidden_size
+    return vision_tower
diff --git a/llava/model/multimodal_encoder/clip_encoder.py b/llava/model/multimodal_encoder/clip_encoder.py
index 24b36c8e..54bc33dc 100644
--- a/llava/model/multimodal_encoder/clip_encoder.py
+++ b/llava/model/multimodal_encoder/clip_encoder.py
@@ -1,23 +1,34 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
+import torch
 
-from llava.model.multimodal_encoder.vision_encoder import VisionTower
-from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
+from llava.model.multimodal_encoder.vision_encoder import VisionTower, VisionTowerS2
+from transformers import (
+    PretrainedConfig,
+    CLIPVisionModel,
+    CLIPImageProcessor,
+)
 
 
 class CLIPVisionTower(VisionTower):
-    def __init__(self, vision_tower, args, delay_load=False):
-        super().__init__(vision_tower, args, delay_load)
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = CLIPImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = CLIPVisionModel.from_pretrained(
+            model_name_or_path, torch_dtype=eval(config.model_dtype)
+        )
+        self.is_loaded = True
 
-        if not delay_load:
-            self.load_model()
-        else:
-            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
 
-    def load_model(self):
-        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
-        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
-        self.vision_tower.requires_grad_(False)
+class CLIPVisionTowerS2(VisionTowerS2):
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = CLIPImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = CLIPVisionModel.from_pretrained(
+            model_name_or_path, torch_dtype=eval(config.model_dtype)
+        )
 
-        self.is_loaded = True
+        # Make sure it crops/resizes the image to the largest scale in self.scales to maintain high-res information
+        self.image_processor.size['shortest_edge'] = self.scales[-1]
+        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.scales[-1]
 
-    
\ No newline at end of file
+        self.is_loaded = True
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/image_processor.py b/llava/model/multimodal_encoder/image_processor.py
new file mode 100644
index 00000000..5fa62259
--- /dev/null
+++ b/llava/model/multimodal_encoder/image_processor.py
@@ -0,0 +1,546 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Image processor class for RADIO."""
+import math
+from copy import deepcopy
+from itertools import product
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+
+import PIL
+from PIL.Image import Image
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from transformers.image_transforms import convert_to_rgb, pad, resize, to_channel_dimension_format
+from transformers.image_utils import (
+    IMAGENET_DEFAULT_MEAN,
+    IMAGENET_DEFAULT_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_scaled_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+)
+from transformers.utils import (
+    TensorType,
+    is_tf_available,
+    is_torch_available,
+    is_torchvision_available,
+    logging,
+    requires_backends,
+)
+
+
+if is_torch_available():
+    import torch
+    import torch.nn.functional as F
+
+if is_torchvision_available():
+    from torchvision.ops.boxes import batched_nms
+
+if is_tf_available():
+    import tensorflow as tf
+    from tensorflow.experimental import numpy as tnp
+
+    from ...tf_utils import flatten, shape_list
+
+logger = logging.get_logger(__name__)
+
+
+def rank_print(s):
+    rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
+    print(f"[Rank {rank}] {s}")
+
+class ImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs an image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
+            `do_resize` parameter in the `preprocess` method.
+        size (`dict`, *optional*, defaults to `{"longest_edge": 1024}`):
+            Size of the output image after resizing. If "longest_edge" is specified, resizes the longest edge of the image to match
+            `size["longest_edge"]` while maintaining the aspect ratio. If "width" and "height" are specified, resizes the image
+            to that size, possibly changing the aspect ratio. Can be overridden by the `size` parameter in the
+            `preprocess` method.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
+            `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
+            `do_rescale` parameter in the `preprocess` method.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
+            overridden by the `rescale_factor` parameter in the `preprocess` method.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
+            overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_pad (`bool`, *optional*, defaults to `True`):
+            Whether to pad the image to the specified `pad_size`. Can be overridden by the `do_pad` parameter in the
+            `preprocess` method.
+        pad_size (`dict`, *optional*, defaults to `{"height": 1024, "width": 1024}`):
+            Size of the output image after padding. Can be overridden by the `pad_size` parameter in the `preprocess`
+            method.
+        pad_value (`float` or `Iterable[float]`, *optional*, defaults to `0.`):
+            Value of padded pixels.
+        pad_multiple (`int`, *optional*, defaults to `None`):
+            Pad to a multiple of specified number.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: bool = True,
+        pad_size: int = None,
+        pad_multiple: int = None,
+        pad_value: Optional[Union[float, List[float]]] = 0.,
+        do_convert_rgb: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        x = 0
+        size = size if size is not None else {"longest_edge": 1024}
+        size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size
+
+        if pad_size is not None and pad_multiple is not None:
+            raise ValueError("pad_size and pad_multiple should not be set at the same time.")
+
+        pad_size = pad_size if pad_size is not None else {"height": 1024, "width": 1024} if pad_multiple is not None else None
+        if do_pad:
+            pad_size = get_size_dict(pad_size, default_to_square=True)
+
+        self.do_resize = do_resize
+        self.size = size
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
+        self.do_pad = do_pad
+        self.pad_multiple = pad_multiple
+        self.pad_size = pad_size
+        self.pad_value = tuple(pad_value) if isinstance(pad_value, list) else pad_value
+        self.do_convert_rgb = do_convert_rgb
+        self._valid_processor_keys = [
+            "images",
+            "segmentation_maps",
+            "do_resize",
+            "size",
+            "resample",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "do_pad",
+            "pad_size",
+            "do_convert_rgb",
+            "return_tensors",
+            "data_format",
+            "input_data_format",
+        ]
+
+    def pad_image(
+        self,
+        image: np.ndarray,
+        pad_size: Dict[str, int],
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Pad an image to `(pad_size["height"], pad_size["width"])` to the right and bottom.
+
+        Args:
+            image (`np.ndarray`):
+                Image to pad.
+            pad_size (`Dict[str, int]`):
+                Size of the output image after padding.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The data format of the image. Can be either "channels_first" or "channels_last". If `None`, the
+                `data_format` of the `image` will be used.
+            input_data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format of the input image. If not provided, it will be inferred.
+        """
+        output_height, output_width = pad_size["height"], pad_size["width"]
+        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
+
+        pad_width = output_width - input_width
+        pad_height = output_height - input_height
+
+        padded_image = pad(
+            image,
+            ((0, pad_height), (0, pad_width)),
+            data_format=data_format,
+            input_data_format=input_data_format,
+            constant_values=self.pad_value,
+            **kwargs,
+        )
+        return padded_image
+
+    def _get_preprocess_shape(self, old_shape: Tuple[int, int], longest_edge: int):
+        """
+        Compute the output size given input size and target long side length.
+        """
+        oldh, oldw = old_shape
+        scale = longest_edge * 1.0 / max(oldh, oldw)
+        newh, neww = oldh * scale, oldw * scale
+        newh = int(newh + 0.5)
+        neww = int(neww + 0.5)
+        return (newh, neww)
+
+    def resize(
+        self,
+        image: np.ndarray,
+        size: Dict[str, int],
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image to `(size["height"], size["width"])`.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            size (`Dict[str, int]`):
+                Dictionary in the format `{"longest_edge": int}` or `{"width": int, "height": int}` specifying the size
+                of the output image. If "longest_edge" is specified, resizes the longest edge of the image to match
+                `size["longest_edge"]` while maintaining the aspect ratio. If "width" and "height" are specified, resizes the image
+                to that size, possibly changing the aspect ratio.
+            resample:
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+            data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the output image. If unset, the channel dimension format of the input
+                image is used. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+
+        Returns:
+            `np.ndarray`: The resized image.
+        """
+        size = get_size_dict(size)
+        if "longest_edge" not in size:
+            if "width" not in size or "height" not in size:
+                raise ValueError(f"The `size` dictionary must contain the key `longest_edge`, or `width` and `height`. Got {size.keys()}")
+        input_size = get_image_size(image, channel_dim=input_data_format)
+        if "longest_edge" in size:
+            output_height, output_width = self._get_preprocess_shape(input_size, size["longest_edge"])
+        else:
+            output_height, output_width = size["height"], size["width"]
+        return resize(
+            image,
+            size=(output_height, output_width),
+            resample=resample,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def _preprocess(
+        self,
+        image: ImageInput,
+        do_resize: bool,
+        do_rescale: bool,
+        do_normalize: bool,
+        size: Optional[Dict[str, int]] = None,
+        resample: PILImageResampling = None,
+        rescale_factor: Optional[float] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: Optional[bool] = None,
+        pad_size: Optional[Dict[str, int]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        if do_resize:
+            image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+        reshaped_input_size = get_image_size(image, channel_dim=input_data_format)
+
+        if do_rescale:
+            image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+
+        if do_normalize:
+            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+
+        if do_pad:
+            if self.pad_multiple:
+                h, w = get_image_size(image, channel_dim=input_data_format)
+                pad_size = {
+                    "height": math.ceil(h / self.pad_multiple) * self.pad_multiple,
+                    "width": math.ceil(w / self.pad_multiple) * self.pad_multiple,
+                }
+
+            image = self.pad_image(image=image, pad_size=pad_size, input_data_format=input_data_format)
+
+        return image, reshaped_input_size
+
+    def _preprocess_image(
+        self,
+        image: ImageInput,
+        do_resize: Optional[bool] = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: Optional[float] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: Optional[bool] = None,
+        pad_size: Optional[Dict[str, int]] = None,
+        do_convert_rgb: Optional[bool] = None,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]:
+        #image = to_numpy_array(image)
+
+#        import time
+#        if int(time.time()*1000) % 10 == 0:
+#            # create an PIL image of size 1x1
+#            image = PIL.Image.new('RGB', (1, 1))
+
+        if isinstance(image, Image):
+            # PIL always uses Channels Last.
+            input_data_format = ChannelDimension.LAST
+
+        # PIL RGBA images are converted to RGB
+        #mode_before = image.mode
+        if do_convert_rgb:
+            image = convert_to_rgb(image)
+
+        # All transformations expect numpy arrays.
+        image_ = image
+        image = to_numpy_array(image)
+
+#        if isinstance(image_, np.ndarray):
+#            rank_print(f"preprocess image type={type(image_)} shape={image_.shape} array shape={image.shape}")
+#        elif isinstance(image_, Image):
+#            rank_print(f"preprocessimage type={type(image_)} size={image_.size} mode={image_.mode} array shape={image.shape}")
+#        else:
+#            rank_print(f"preprocess unknown image type={type(image_)} array shape={image.shape}")
+
+        if len(image.shape) == 2:
+            h, w = image.shape
+            ret = np.empty((h, w, 3), dtype=np.uint8)
+            ret[:, :, 0] = image
+            ret[:, :, 1] = image
+            ret[:, :, 2] = image
+            image = ret
+            rank_print(f"preprocess new image shape={image.shape}")
+        elif len(image.shape) == 3 and image.shape[-1] == 1:
+            ret = np.empty((h, w, 3), dtype=np.uint8)
+            ret[:, :, 0] = image[:, :, 0]
+            ret[:, :, 1] = image[:, :, 0]
+            ret[:, :, 2] = image[:, :, 0]
+            image = ret
+            rank_print(f"preprocess new image shape={image.shape}")
+
+        if is_scaled_image(image) and do_rescale:
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+
+        if input_data_format is None:
+            input_data_format = infer_channel_dimension_format(image)
+
+        original_size = get_image_size(image, channel_dim=input_data_format)
+
+        image, reshaped_input_size = self._preprocess(
+            image=image,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_pad=do_pad,
+            pad_size=pad_size,
+            input_data_format=input_data_format,
+        )
+
+        if data_format is not None:
+            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+
+#        rank_print(f"preprocess original_size={original_size} reshaped_input_size={reshaped_input_size} image shape={image.shape} type={type(image)}")
+
+        # if image is a single channel convert to rgb
+        if do_convert_rgb and image.shape[0] == 1:
+            c, h, w = image.shape
+            ret = np.empty((3, h, w), dtype=np.uint8)
+            ret[0, :, :] = image[0, :, :]
+            ret[1, :, :] = image[0, :, :]
+            ret[2, :, :] = image[0, :, :]
+            image = ret
+            rank_print(f"preprocess final: {image.shape}")
+
+        return image, original_size, reshaped_input_size
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: Optional[bool] = None,
+        size: Optional[Dict[str, int]] = None,
+        resample: Optional["PILImageResampling"] = None,
+        do_rescale: Optional[bool] = None,
+        rescale_factor: Optional[Union[int, float]] = None,
+        do_normalize: Optional[bool] = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: Optional[bool] = None,
+        pad_size: Optional[Dict[str, int]] = None,
+        do_convert_rgb: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: ChannelDimension = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ):
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Controls the size of the image after `resize`. The longest edge of the image is resized to
+                `size["longest_edge"]` whilst preserving the aspect ratio.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image pixel values by rescaling factor.
+            rescale_factor (`int` or `float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to apply to the image pixel values.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to normalize the image by if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to normalize the image by if `do_normalize` is set to `True`.
+            do_pad (`bool`, *optional*, defaults to `self.do_pad`):
+                Whether to pad the image.
+            pad_size (`Dict[str, int]`, *optional*, defaults to `self.pad_size`):
+                Controls the size of the padding applied to the image. The image is padded to `pad_size["height"]` and
+                `pad_size["width"]` if `do_pad` is set to `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                    - Unset: Return a list of `np.ndarray`.
+                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        size = get_size_dict(max_size=size, default_to_square=False) if not isinstance(size, dict) else size
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_pad = do_pad if do_pad is not None else self.do_pad
+        pad_size = pad_size if pad_size is not None else self.pad_size
+        if do_pad:
+            pad_size = get_size_dict(pad_size, default_to_square=True)
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        images, original_sizes, reshaped_input_sizes = zip(
+            *(
+                self._preprocess_image(
+                    image=img,
+                    do_resize=do_resize,
+                    size=size,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    do_pad=do_pad,
+                    pad_size=pad_size,
+                    do_convert_rgb=do_convert_rgb,
+                    data_format=data_format,
+                    input_data_format=input_data_format,
+                )
+                for img in images
+            )
+        )
+
+        data = {
+            "pixel_values": images,
+            "original_sizes": original_sizes,
+            "reshaped_input_sizes": reshaped_input_sizes,
+        }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/llava/model/multimodal_encoder/intern/configuration_intern_vit.py b/llava/model/multimodal_encoder/intern/configuration_intern_vit.py
new file mode 100644
index 00000000..95d154c4
--- /dev/null
+++ b/llava/model/multimodal_encoder/intern/configuration_intern_vit.py
@@ -0,0 +1,119 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+    
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/intern/flash_attention.py b/llava/model/multimodal_encoder/intern/flash_attention.py
new file mode 100644
index 00000000..60c9155f
--- /dev/null
+++ b/llava/model/multimodal_encoder/intern/flash_attention.py
@@ -0,0 +1,76 @@
+# https://github.com/Dao-AILab/flash-attention/blob/v0.2.8/flash_attn/flash_attention.py
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+try:  # v1
+    from flash_attn.flash_attn_interface import \
+        flash_attn_unpadded_qkvpacked_func
+except:  # v2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+from flash_attn.bert_padding import pad_input, unpad_input
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_unpadded_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_unpadded_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_unpadded_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/intern/modeling_intern_vit.py b/llava/model/multimodal_encoder/intern/modeling_intern_vit.py
new file mode 100644
index 00000000..0d77764d
--- /dev/null
+++ b/llava/model/multimodal_encoder/intern/modeling_intern_vit.py
@@ -0,0 +1,547 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from llava.model.multimodal_encoder.intern.configuration_intern_vit import InternVisionConfig
+
+from .flash_attention import FlashAttention
+has_flash_attn = True
+
+
+logger = logging.get_logger(__name__)
+
+
+
+
+""" DropBlock, DropPath
+
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+
+Papers:
+DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
+
+Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
+
+Code:
+DropBlock impl inspired by two Tensorflow impl that I liked:
+ - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
+ - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
+    """generate N-D grid in dimension order.
+
+    The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
+
+    That is, the statement
+    [X1,X2,X3] = ndgrid(x1,x2,x3)
+
+    produces the same result as
+
+    [X2,X1,X3] = meshgrid(x2,x1,x3)
+
+    This naming is based on MATLAB, the purpose is to avoid confusion due to torch's change to make
+    torch.meshgrid behaviour move from matching ndgrid ('ij') indexing to numpy meshgrid defaults of ('xy').
+
+    """
+    try:
+        return torch.meshgrid(*tensors, indexing='ij')
+    except TypeError:
+        # old PyTorch < 1.10 will follow this path as it does not have indexing arg,
+        # the old behaviour of meshgrid was 'ij'
+        return torch.meshgrid(*tensors)
+
+
+def drop_block_2d(
+        x,
+        drop_prob: float = 0.1,
+        block_size: int = 7,
+        gamma_scale: float = 1.0,
+        with_noise: bool = False,
+        inplace: bool = False,
+        batchwise: bool = False
+):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
+    runs with success, but needs further validation and possibly optimization for lower runtime impact.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    # seed_drop_rate, the gamma parameter
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    # Forces the block to be inside the feature map.
+    w_i, h_i = ndgrid(torch.arange(W, device=x.device), torch.arange(H, device=x.device))
+    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
+                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
+    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
+    else:
+        uniform_noise = torch.rand_like(x)
+    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
+    block_mask = -F.max_pool2d(
+        -block_mask,
+        kernel_size=clipped_block_size,  # block_size,
+        stride=1,
+        padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
+        else:
+            x = x * block_mask + normal_noise * (1 - block_mask)
+    else:
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+def drop_block_fast_2d(
+        x: torch.Tensor,
+        drop_prob: float = 0.1,
+        block_size: int = 7,
+        gamma_scale: float = 1.0,
+        with_noise: bool = False,
+        inplace: bool = False,
+):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
+    block mask at edges.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    block_mask = torch.empty_like(x).bernoulli_(gamma)
+    block_mask = F.max_pool2d(
+        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.empty_like(x).normal_()
+        if inplace:
+            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
+        else:
+            x = x * (1. - block_mask) + normal_noise * block_mask
+    else:
+        block_mask = 1 - block_mask
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)).to(dtype=x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+class DropBlock2d(nn.Module):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
+
+    def __init__(
+            self,
+            drop_prob: float = 0.1,
+            block_size: int = 7,
+            gamma_scale: float = 1.0,
+            with_noise: bool = False,
+            inplace: bool = False,
+            batchwise: bool = False,
+            fast: bool = True):
+        super(DropBlock2d, self).__init__()
+        self.drop_prob = drop_prob
+        self.gamma_scale = gamma_scale
+        self.block_size = block_size
+        self.with_noise = with_noise
+        self.inplace = inplace
+        self.batchwise = batchwise
+        self.fast = fast  # FIXME finish comparisons of fast vs not
+
+    def forward(self, x):
+        if not self.training or not self.drop_prob:
+            return x
+        if self.fast:
+            return drop_block_fast_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace)
+        else:
+            return drop_block_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob,3):0.3f}'
+    
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    config_class = InternVisionConfig
+    _no_split_modules = ['InternVisionEncoderLayer']
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/intern_encoder.py b/llava/model/multimodal_encoder/intern_encoder.py
new file mode 100644
index 00000000..896053a6
--- /dev/null
+++ b/llava/model/multimodal_encoder/intern_encoder.py
@@ -0,0 +1,53 @@
+
+from llava.model.multimodal_encoder.vision_encoder import VisionTower
+from llava.model.multimodal_encoder.intern.configuration_intern_vit import InternVisionConfig
+from llava.model.multimodal_encoder.intern.modeling_intern_vit import InternVisionModel
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+import torch
+from transformers.image_processing_utils import BaseImageProcessor
+from transformers import AutoConfig, AutoModel
+
+
+def build_transform(input_size):
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+    ])
+    return transform
+
+class InternVisionPreprocessor(BaseImageProcessor):
+
+    @property
+    def size(self):
+        return {'height':448, 'width':448}
+
+    def preprocess(self, image, return_tensors):
+        transform = build_transform(448)
+        if isinstance(image, list):
+            image_tensor = [transform(img) for img in image]
+            return {'pixel_values': image_tensor}
+        else:
+            image_tensor = transform(image) 
+            return {'pixel_values': [image_tensor]}
+
+
+class InternVisionTower(VisionTower):
+    def __init__(self, vision_tower, config, drop_path_rate=0.):
+        super().__init__(vision_tower, config)
+        self._drop_path_rate = drop_path_rate
+        
+        self.image_processor = InternVisionPreprocessor()
+        vision_config = InternVisionConfig.from_pretrained(vision_tower)
+        vision_config.drop_path_rate = self._drop_path_rate
+        self.vision_tower = InternVisionModel.from_pretrained(
+            vision_tower,
+            torch_dtype=eval(config.model_dtype),
+            config=vision_config)
+
+        self.is_loaded = True
+
+AutoConfig.register("intern_vit_6b", InternVisionConfig)
+AutoModel.register(InternVisionConfig, InternVisionModel)
\ No newline at end of file
diff --git a/llava/model/multimodal_encoder/radio_encoder.py b/llava/model/multimodal_encoder/radio_encoder.py
new file mode 100644
index 00000000..78e76491
--- /dev/null
+++ b/llava/model/multimodal_encoder/radio_encoder.py
@@ -0,0 +1,317 @@
+from argparse import Namespace
+import os
+import torch
+from typing import Any, Dict
+import warnings
+
+from llava.model.multimodal_encoder.vision_encoder import VisionTower
+from llava.train.utils import mprint, rprint
+from transformers import CLIPVisionConfig
+from .image_processor import ImageProcessor
+from PIL import Image
+import numpy as np
+
+from .visualize_features import get_pca_map
+
+
+def get_prefix_state_dict(state_dict: Dict[str, Any], prefix: str):
+    mod_state_dict = {
+        k[len(prefix) :]: v for k, v in state_dict.items() if k.startswith(prefix)
+    }
+    return mod_state_dict
+
+def is_rank0():
+    return not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0
+
+class RADIOVisionTower(VisionTower):
+    """
+    Vision Tower for the RADIO model.
+
+    Args:
+        vision_tower (str): Vision tower name. This is passed on
+            the command line with the `--vision_tower` argument.
+            The string is expected in the pattern of:
+            `radio:<image_size>:<checkpoint>:<extra_config>`.
+            Where <extra_config> is a comma-separated list of key=value pairs.
+            <image_size> can also be a comma-separated list of resolutions in
+            the case of multi-res inference. Limitations apply, e.g. only two
+            resolutions are supported and the second resolution must be a divisor
+            of the first one.
+        args (Namespace): Arguments.
+        delay_load (bool): Delay loading the model.
+    """
+    def __init__(self, vision_tower, args, delay_load=False):
+        """Initialization Routine."""
+
+        super().__init__(vision_tower, args, delay_load)
+
+        mprint(f"RADIOVisionTower: {vision_tower}. Args: {args} Delay load: {delay_load}")
+
+        self.vision_tower_name = vision_tower[len("radio:"):]
+        config_items = self.vision_tower_name.split(":")
+        self.image_sizes = [int(x) for x in config_items[0].split(",")]
+        if len(self.image_sizes) == 0:
+            raise ValueError("Expected more than zero images sizes!")
+        self.image_size = self.image_sizes[0]
+        self.image_aspect_ratio = args.image_aspect_ratio
+
+        self.downscale_factor = None
+        if len(self.image_sizes) > 1:
+            self.downscale_factor = self.image_sizes[0] // self.image_sizes[1]
+            assert self.downscale_factor == self.image_sizes[0] / self.image_sizes[1]
+            self.pool2d = torch.nn.AvgPool2d(self.downscale_factor, self.downscale_factor)
+            if len(self.image_sizes) > 2:
+                raise ValueError(f"Only support up to two resolutions")
+        elif self.image_size >= 512:
+            self.downscale_factor = 2
+
+        self.vision_tower_checkpoint = config_items[1]
+
+        extra_config = {}
+        if len(config_items) > 2:
+            # Parse extra config items. These are provided as a comma-separated list
+            # of key=value pairs.
+            extra_config_items = config_items[2].split(",")
+
+            for item in extra_config_items:
+                key, value = item.split("=")
+                extra_config[key] = value
+
+        self.adaptor_name = extra_config.get("adaptor", "backbone")
+        self.fuse_adaptor_with_backbone = eval(extra_config.get("fuse_adaptor_with_backbone", "False"))
+        self.skip_layer_norm = eval(extra_config.get("skip_layer_norm", "False"))
+        self.allow_pixel_unshuffle = eval(extra_config.get("pixel_unshuffle", "False"))
+
+        self.pixel_unshuffle = None
+        if self.allow_pixel_unshuffle and self.downscale_factor is not None:
+            self.pixel_unshuffle = torch.nn.PixelUnshuffle(self.downscale_factor)
+
+        if not delay_load:
+            self.load_model()
+        else:
+            # FIXME: This is a hack to avoid having to load the config from the checkpoint.
+            hidden_size = self.get_hidden_size(self.adaptor_name)
+            patch_size = 16
+
+            self.cfg_only = CLIPVisionConfig(
+                **{
+
+                    "hidden_size": hidden_size,
+                    "image_size": self.image_size,
+                    "model_type": "radio_vision_model",
+                    "num_attention_heads": None,
+                    "num_channels": 3,
+                    "num_hidden_layers": None,
+                    "patch_size": patch_size,
+                }
+            )
+
+        self.sample_count = 0
+
+        self.debug = True
+
+    def get_hidden_size(self):
+        if self.adaptor_name == "openai_clip":
+            hidden_size = 1024
+        elif self.adaptor_name == "clip":
+            hidden_size = 1280
+        elif self.adaptor_name == "rtx-translate":
+            hidden_size = 2048
+        elif self.adaptor_name == "backbone":
+            hidden_size = 1280
+        else:
+            raise ValueError(f"Unknown adaptor name: {self.adaptor_name}")
+
+        if self.fuse_adaptor_with_backbone:
+            hidden_size += 1280
+
+        if len(self.image_sizes) == 2:
+            if self.pixel_unshuffle is not None:
+                hidden_size = hidden_size * 5
+            else:
+                hidden_size = hidden_size * 2
+        elif self.pixel_unshuffle is not None:
+            hidden_size = hidden_size * 4
+
+        return hidden_size
+
+    def load_model(self):
+
+        if self.image_aspect_ratio == "resize":
+            self.image_processor = ImageProcessor(
+                size={"width": self.image_size, "height": self.image_size},
+                do_pad=False,
+                do_normalize=False,
+                do_convert_rgb=True,
+            )
+        else:
+            self.image_processor = ImageProcessor(
+                    size={"longest_edge": self.image_size},
+                    do_pad=True,
+                    pad_multiple=16,
+                    do_normalize=False,
+                    do_convert_rgb=True,
+                    pad_value=0.456,
+                )
+        # For compatibility with CLIP Image Processor: the data loader uses width/height to
+        # create dummy blank images for samples that don't have an image.
+        self.image_processor.crop_size = {"width": self.image_size, "height": self.image_size}
+
+        # Load weights from checkpoint.
+        checkpoint_path = self.vision_tower_checkpoint
+        rprint(f"Loading checkpoint from {checkpoint_path}")
+
+        # NOTE: do a lazy import of Timm to avoid issues with
+        # DeepSpeed's ZeRO-3.
+        from timm.models.vision_transformer import VisionTransformer
+
+        self.vision_tower = torch.hub.load('NVlabs/RADIO',
+                                           'radio_model',
+                                           version=checkpoint_path,
+                                           progress=True,
+                                           adaptor_names=self.adaptor_name if self.adaptor_name != "backbone" else None)
+
+        if isinstance(self.vision_tower.model, VisionTransformer):
+            hidden_size = self.vision_tower.model.embed_dim
+        else:
+            raise ValueError(f"Unknown model type: {self.vision_tower}")
+
+        # Override hidden size for OpenAI CLIP.
+        hidden_size = self.get_hidden_size()
+
+        if hasattr(self.vision_tower.model, "patch_generator"):
+            patch_gen = self.vision_tower.model.patch_generator
+            # Cropped Positional Embedding (CPE) case.
+            patch_size = patch_gen.patch_size
+        else:
+            # Standard ViT case.
+            patch_size = self.vision_tower.model.patch_embed.patch_size[0]
+
+        self.vision_tower.config = CLIPVisionConfig(
+                **{
+                    "hidden_size": hidden_size,
+                    "image_size": self.image_size,
+                    "model_type": "radio_vision_model",
+                    "num_attention_heads": None,
+                    "num_channels": 3,
+                    "num_hidden_layers": None,
+                    "patch_size": patch_size,
+                }
+            )
+
+        self.vision_tower.eval()
+        self.vision_tower.requires_grad_(False)
+
+        self.is_loaded = True
+        self._to_dtype = None
+
+        if self.skip_layer_norm:
+            rank0_print(f"Removing layer norm from the model: {self.vision_tower.model.norm}")
+            self.vision_tower.model.norm = torch.nn.Identity()
+
+
+    def to(self, *args, **kwargs):
+        # Prevent casting the RADIO model's weights
+        kwargs = dict(kwargs)
+        self._to_dtype = kwargs.pop('dtype', None)
+        mprint(f"RADIO: bypass cast to dtype={self._to_dtype}")
+        super().to(*args, **kwargs)
+        pass
+
+    def train(self, mode=True):
+        """Intercept call."""
+        # Drop a warning if mode is True.
+        if mode:
+            warnings.warn("RADIOEncoder is always in eval mode.")
+        pass
+
+    @torch.no_grad()
+    def get_features(self, x: torch.Tensor):
+        output = self.vision_tower(x)
+        if isinstance(output, dict):
+            _, features = output[self.adaptor_name]
+            if self.fuse_adaptor_with_backbone:
+                _, backbone_features = output["backbone"]
+                features = torch.cat([features, backbone_features], dim=2)
+        else:
+            _, features = output
+        return features
+
+    @torch.no_grad()
+    def forward(self, images: torch.Tensor):
+        """Main forward pass."""
+        input_shape = images.shape
+
+        x = images
+        # Add a batch dimension if necessary.
+        if len(input_shape) == 3:
+            x = x.unsqueeze(0)
+
+        rprint(f"input shape={input_shape}->{x.shape} device={x.device} mean={x.mean().item()} std={x.std().item()} dtype={x.dtype}")
+
+        features = self.get_features(x) # B, T, C
+
+        B, _, H, W = x.shape
+        _, _, C = features.shape
+        patch_size = self.vision_tower.config.patch_size
+        spatial_features = features.reshape(B, H // patch_size, W // patch_size, C)
+        spatial_features = spatial_features.permute(0, 3, 1, 2) # B, C, H/patch_size, W/patch_size
+
+        if self.debug and is_rank0() and self.sample_count % 1000 == 0:
+            spatial_features_hwc = spatial_features.permute(0, 2, 3, 1)
+            # create the debug directory
+            os.makedirs("radio-debug", exist_ok=True)
+            torch.save(x, f"radio-debug/sample_{self.sample_count}_input.pt")
+            torch.save(features, f"radio-debug/sample_{self.sample_count}_features.pt")
+            torch.save(spatial_features_hwc, f"radio-debug/sample_{self.sample_count}_features_reshaped.pt")
+            for i in range(B):
+                image = x[i].permute(1, 2, 0).float() * 255
+                image = Image.fromarray(image.cpu().numpy().astype(np.uint8))
+                image.save(os.path.join("radio-debug/", f"sample_{self.sample_count}_preprocessed_{i}.png"))
+                pca_map = get_pca_map(spatial_features_hwc[i:i+1], x.shape[-2:])
+                torch.save(pca_map, f"radio-debug/sample_{self.sample_count}_pca_map_{i}.pt")
+                image = pca_map * 255
+                image = Image.fromarray(image.astype(np.uint8))
+                image.save(os.path.join("radio-debug/", f"sample_{self.sample_count}_pca_map_{i}.png"))
+                pass
+
+        if self.pixel_unshuffle is not None:
+            spatial_features = self.pixel_unshuffle(spatial_features)
+            # B, C*downscale_factor**2, H/patch_size/downscale_factor, W/patch_size/downscale_factor
+            features = spatial_features.reshape(
+                B,
+                C * self.downscale_factor**2,
+                (H // patch_size // self.downscale_factor) * (W // patch_size // self.downscale_factor)).permute(0, 2, 1)
+
+        if len(self.image_sizes) > 1:
+            # Experimental support for multi-resolution inference.
+            if self.pixel_unshuffle is None:
+                # downscale features
+                spatial_features = self.pool2d(spatial_features) # B, C, H/patch_size/downscale_factor, W/patch_size/downscale_factor
+                features = spatial_features.reshape(
+                    B,
+                    C,
+                    (H // patch_size // self.downscale_factor) * (W // patch_size // self.downscale_factor))
+                features = features.permute(0, 2, 1) # B, (H/patch_size/downscale_factor) * (W/patch_size/downscale_factor), C
+
+            # Downscale the input image.
+            x = self.pool2d(x) # B, 3, H/downscale_factor, W/downscale_factor)
+            features_stage2 = self.get_features(x) # B, (H/patch_size/downscale_factor) * (W/patch_size/downscale_factor), C
+
+            # Concatenate stage1 and stage 2 features.
+            features = torch.cat([features, features_stage2], dim=2)
+
+        # Remove the batch dimension if we added it.
+        if len(input_shape) == 3:
+            features = features.squeeze(0)
+
+        # Cast back to the input's dtype.
+        features = features.to(images.dtype)
+
+        adaptor_name = f"{self.adaptor_name}{'+backbone' if self.fuse_adaptor_with_backbone else ''}"
+        rprint(f"features ({adaptor_name}) shape={features.shape} mean={features.mean().item()} std={features.std().item()} dtype={features.dtype}")
+
+        assert features.shape[-1] == self.get_hidden_size()
+        self.sample_count += 1
+
+        return features
diff --git a/llava/model/multimodal_encoder/siglip/__init__.py b/llava/model/multimodal_encoder/siglip/__init__.py
new file mode 100644
index 00000000..19dbec21
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/__init__.py
@@ -0,0 +1,94 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from transformers.utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_torch_available,
+    is_vision_available,
+)
+
+
+_import_structure = {
+    "configuration_siglip": [
+        "SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "SiglipConfig",
+        "SiglipTextConfig",
+        "SiglipVisionConfig",
+    ],
+    "processing_siglip": ["SiglipProcessor"],
+    "tokenization_siglip": ["SiglipTokenizer"],
+}
+
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["image_processing_siglip"] = ["SiglipImageProcessor"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_siglip"] = [
+        "SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "SiglipModel",
+        "SiglipPreTrainedModel",
+        "SiglipTextModel",
+        "SiglipVisionModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_siglip import (
+        SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        SiglipConfig,
+        SiglipTextConfig,
+        SiglipVisionConfig,
+    )
+    from .processing_siglip import SiglipProcessor
+    from .tokenization_siglip import SiglipTokenizer
+
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .image_processing_siglip import SiglipImageProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_siglip import (
+            SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            SiglipModel,
+            SiglipPreTrainedModel,
+            SiglipTextModel,
+            SiglipVisionModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/llava/model/multimodal_encoder/siglip/configuration_siglip.py b/llava/model/multimodal_encoder/siglip/configuration_siglip.py
new file mode 100644
index 00000000..4341b472
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/configuration_siglip.py
@@ -0,0 +1,302 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Siglip model configuration"""
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/config.json",
+}
+
+
+class SiglipTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
+    Siglip text encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the text encoder of the Siglip
+    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by
+            the `inputs_ids` passed when calling [`SiglipModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        max_position_embeddings (`int`, *optional*, defaults to 64):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        pad_token_id (`int`, *optional*, defaults to 1):
+            The id of the padding token in the vocabulary.
+        bos_token_id (`int`, *optional*, defaults to 49406):
+            The id of the beginning-of-sequence token in the vocabulary.
+        eos_token_id (`int`, *optional*, defaults to 49407):
+            The id of the end-of-sequence token in the vocabulary.
+
+    Example:
+
+    ```python
+    >>> from transformers import SiglipTextConfig, SiglipTextModel
+
+    >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
+    >>> configuration = SiglipTextConfig()
+
+    >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
+    >>> model = SiglipTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "siglip_text_model"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        max_position_embeddings=64,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        # This differs from `CLIPTokenizer`'s default and from openai/siglip
+        # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
+        pad_token_id=1,
+        bos_token_id=49406,
+        eos_token_id=49407,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.attention_dropout = attention_dropout
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        # cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the text config dict if we are loading from SiglipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["text_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class SiglipVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
+    Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
+    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of channels in the input images.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    Example:
+
+    ```python
+    >>> from transformers import SiglipVisionConfig, SiglipVisionModel
+
+    >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
+    >>> configuration = SiglipVisionConfig()
+
+    >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
+    >>> model = SiglipVisionModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "siglip_vision_model"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        image_size=224,
+        patch_size=16,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        # cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the vision config dict if we are loading from SiglipConfig
+        if config_dict.get("model_type") == "siglip":
+            config_dict = config_dict["vision_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class SiglipConfig(PretrainedConfig):
+    r"""
+    [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
+    instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip
+    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        text_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`SiglipTextConfig`].
+        vision_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
+        kwargs (*optional*):
+            Dictionary of keyword arguments.
+
+    Example:
+
+    ```python
+    >>> from transformers import SiglipConfig, SiglipModel
+
+    >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration
+    >>> configuration = SiglipConfig()
+
+    >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration
+    >>> model = SiglipModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+
+    >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig
+    >>> from transformers import SiglipTextConfig, SiglipVisionConfig
+
+    >>> # Initializing a SiglipText and SiglipVision configuration
+    >>> config_text = SiglipTextConfig()
+    >>> config_vision = SiglipVisionConfig()
+
+    >>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision)
+    ```"""
+
+    model_type = "siglip"
+
+    def __init__(self, text_config=None, vision_config=None, **kwargs):
+        super().__init__(**kwargs)
+
+        if text_config is None:
+            text_config = {}
+            logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.")
+
+        if vision_config is None:
+            vision_config = {}
+            logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.")
+
+        self.text_config = SiglipTextConfig(**text_config)
+        self.vision_config = SiglipVisionConfig(**vision_config)
+
+        self.initializer_factor = 1.0
+
+    @classmethod
+    def from_text_vision_configs(cls, text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs):
+        r"""
+        Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
+        model configuration.
+
+        Returns:
+            [`SiglipConfig`]: An instance of a configuration object
+        """
+
+        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
diff --git a/llava/model/multimodal_encoder/siglip/convert_siglip_to_hf.py b/llava/model/multimodal_encoder/siglip/convert_siglip_to_hf.py
new file mode 100644
index 00000000..6adacef8
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/convert_siglip_to_hf.py
@@ -0,0 +1,413 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert SigLIP checkpoints from the original repository.
+
+URL: https://github.com/google-research/big_vision/tree/main
+"""
+
+
+import argparse
+import collections
+from pathlib import Path
+
+import numpy as np
+import requests
+import torch
+from huggingface_hub import hf_hub_download
+from numpy import load
+from PIL import Image
+
+from transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+model_name_to_checkpoint = {
+    # base checkpoints
+    "siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz",
+    "siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz",
+    "siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz",
+    "siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz",
+    # large checkpoints
+    "siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz",
+    "siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz",
+    # multilingual checkpoint
+    "siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz",
+    # so400m checkpoints
+    "siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz",
+}
+
+model_name_to_image_size = {
+    "siglip-base-patch16-224": 224,
+    "siglip-base-patch16-256": 256,
+    "siglip-base-patch16-384": 384,
+    "siglip-base-patch16-512": 512,
+    "siglip-large-patch16-256": 256,
+    "siglip-large-patch16-384": 384,
+    "siglip-base-patch16-256-i18n": 256,
+    "siglip-so400m-patch14-384": 384,
+}
+
+
+def get_siglip_config(model_name):
+    config = SiglipConfig()
+
+    vocab_size = 250000 if "i18n" in model_name else 32000
+    image_size = model_name_to_image_size[model_name]
+    patch_size = 16 if "patch16" in model_name else 14
+
+    # size of the architecture
+    config.vision_config.image_size = image_size
+    config.vision_config.patch_size = patch_size
+    config.text_config.vocab_size = vocab_size
+
+    if "base" in model_name:
+        pass
+    elif "large" in model_name:
+        config.text_config.hidden_size = 1024
+        config.text_config.intermediate_size = 4096
+        config.text_config.num_hidden_layers = 24
+        config.text_config.num_attention_heads = 16
+        config.vision_config.hidden_size = 1024
+        config.vision_config.intermediate_size = 4096
+        config.vision_config.num_hidden_layers = 24
+        config.vision_config.num_attention_heads = 16
+    elif "so400m" in model_name:
+        config.text_config.hidden_size = 1152
+        config.text_config.intermediate_size = 4304
+        config.text_config.num_hidden_layers = 27
+        config.text_config.num_attention_heads = 16
+        config.vision_config.hidden_size = 1152
+        config.vision_config.intermediate_size = 4304
+        config.vision_config.num_hidden_layers = 27
+        config.vision_config.num_attention_heads = 16
+    else:
+        raise ValueError("Model not supported")
+
+    return config
+
+
+def create_rename_keys(config):
+    rename_keys = []
+    # fmt: off
+
+    # vision encoder
+
+    rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight"))
+    rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias"))
+    rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight"))
+
+    for i in range(config.vision_config.num_hidden_layers):
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
+        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))
+
+    rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight"))
+    rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias"))
+
+    rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe"))
+    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight"))
+    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias"))
+    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight"))
+    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias"))
+    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight"))
+    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias"))
+    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight"))
+    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias"))
+
+    # text encoder
+
+    rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight"))
+    rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight"))
+
+    for i in range(config.text_config.num_hidden_layers):
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight"))
+        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias"))
+
+    rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight"))
+    rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias"))
+    rename_keys.append(("params/txt/head/kernel", "text_model.head.weight"))
+    rename_keys.append(("params/txt/head/bias", "text_model.head.bias"))
+
+    # learned temperature and bias
+    rename_keys.append(("params/t", "logit_scale"))
+    rename_keys.append(("params/b", "logit_bias"))
+
+    # fmt: on
+    return rename_keys
+
+
+def rename_key(dct, old, new, config):
+    val = dct.pop(old)
+
+    if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "vision" in new:
+        val = val.reshape(-1, config.vision_config.hidden_size)
+    if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "text" in new:
+        val = val.reshape(-1, config.text_config.hidden_size)
+
+    if "patch_embedding.weight" in new:
+        val = val.transpose(3, 2, 0, 1)
+    elif new.endswith("weight") and "position_embedding" not in new and "token_embedding" not in new:
+        val = val.T
+
+    if "position_embedding" in new and "vision" in new:
+        val = val.reshape(-1, config.vision_config.hidden_size)
+    if "position_embedding" in new and "text" in new:
+        val = val.reshape(-1, config.text_config.hidden_size)
+
+    if new.endswith("bias"):
+        val = val.reshape(-1)
+
+    dct[new] = torch.from_numpy(val)
+
+
+def read_in_q_k_v_head(state_dict, config):
+    # read in individual input projection layers
+    key_proj_weight = (
+        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel")
+        .reshape(-1, config.vision_config.hidden_size)
+        .T
+    )
+    key_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias").reshape(-1)
+    value_proj_weight = (
+        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel")
+        .reshape(-1, config.vision_config.hidden_size)
+        .T
+    )
+    value_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias").reshape(-1)
+    query_proj_weight = (
+        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel")
+        .reshape(-1, config.vision_config.hidden_size)
+        .T
+    )
+    query_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias").reshape(-1)
+
+    # next, add them to the state dict as a single matrix + vector
+    state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy(
+        np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0)
+    )
+    state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy(
+        np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0)
+    )
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    return image
+
+
+def flatten_nested_dict(params, parent_key="", sep="/"):
+    items = []
+
+    for k, v in params.items():
+        new_key = parent_key + sep + k if parent_key else k
+
+        if isinstance(v, collections.abc.MutableMapping):
+            items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
+        else:
+            items.append((new_key, v))
+    return dict(items)
+
+
+@torch.no_grad()
+def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False):
+    """
+    Copy/paste/tweak model's weights to our SigLIP structure.
+    """
+
+    # define default SigLIP configuration
+    config = get_siglip_config(model_name)
+
+    # get checkpoint
+    checkpoint = model_name_to_checkpoint[model_name]
+
+    # get vocab file
+    if "i18n" in model_name:
+        vocab_file = "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model"
+    else:
+        vocab_file = "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model"
+
+    # load original state dict
+    data = load(checkpoint)
+    state_dict = flatten_nested_dict(data)
+
+    # remove and rename some keys
+    rename_keys = create_rename_keys(config)
+    for src, dest in rename_keys:
+        rename_key(state_dict, src, dest, config)
+
+    # qkv matrices of attention pooling head need special treatment
+    read_in_q_k_v_head(state_dict, config)
+
+    # load HuggingFace model
+    model = SiglipModel(config).eval()
+    model.load_state_dict(state_dict)
+
+    # create processor
+    # important: make tokenizer not return attention_mask since original one doesn't require it
+    image_size = config.vision_config.image_size
+    size = {"height": image_size, "width": image_size}
+    image_processor = SiglipImageProcessor(size=size)
+    tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"])
+    processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer)
+
+    # verify on dummy images and texts
+    url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg"
+    image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB")
+    url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg"
+    image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB")
+    texts = ["an apple", "a picture of an apple"]
+
+    inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length")
+
+    # verify input_ids against original ones
+    if image_size == 224:
+        filename = "siglip_pixel_values.pt"
+    elif image_size == 256:
+        filename = "siglip_pixel_values_256.pt"
+    elif image_size == 384:
+        filename = "siglip_pixel_values_384.pt"
+    elif image_size == 512:
+        filename = "siglip_pixel_values_512.pt"
+    else:
+        raise ValueError("Image size not supported")
+
+    filepath = hf_hub_download(repo_id="nielsr/test-image", filename=filename, repo_type="dataset")
+    original_pixel_values = torch.load(filepath)
+    filepath = hf_hub_download(repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset")
+    original_input_ids = torch.load(filepath)
+
+    if "i18n" not in model_name:
+        assert inputs.input_ids.tolist() == original_input_ids.tolist()
+
+    print("Mean of original pixel values:", original_pixel_values.mean())
+    print("Mean of new pixel values:", inputs.pixel_values.mean())
+
+    # note: we're testing with original pixel values here since we don't have exact pixel values
+    with torch.no_grad():
+        outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values)
+
+    # with torch.no_grad():
+    #     outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values)
+
+    print(outputs.logits_per_image[:3, :3])
+
+    probs = torch.sigmoid(outputs.logits_per_image)  # these are the probabilities
+    print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+    print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")
+
+    if verify_logits:
+        if model_name == "siglip-base-patch16-224":
+            expected_slice = torch.tensor(
+                [[-2.9621, -2.1672], [-0.2713, 0.2910]],
+            )
+        elif model_name == "siglip-base-patch16-256":
+            expected_slice = torch.tensor(
+                [[-3.1146, -1.9894], [-0.7312, 0.6387]],
+            )
+        elif model_name == "siglip-base-patch16-384":
+            expected_slice = torch.tensor(
+                [[-2.8098, -2.1891], [-0.4242, 0.4102]],
+            )
+        elif model_name == "siglip-base-patch16-512":
+            expected_slice = torch.tensor(
+                [[-2.7899, -2.2668], [-0.4295, -0.0735]],
+            )
+        elif model_name == "siglip-large-patch16-256":
+            expected_slice = torch.tensor(
+                [[-1.5827, -0.5801], [-0.9153, 0.1363]],
+            )
+        elif model_name == "siglip-large-patch16-384":
+            expected_slice = torch.tensor(
+                [[-2.1523, -0.2899], [-0.2959, 0.7884]],
+            )
+        elif model_name == "siglip-so400m-patch14-384":
+            expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]])
+        elif model_name == "siglip-base-patch16-256-i18n":
+            expected_slice = torch.tensor(
+                [[-0.9064, 0.1073], [-0.0299, 0.5304]],
+            )
+
+        assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4)
+        print("Looks ok!")
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        print(f"Saving processor to {pytorch_dump_folder_path}")
+        processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        model.push_to_hub(f"nielsr/{model_name}")
+        processor.push_to_hub(f"nielsr/{model_name}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--model_name",
+        default="siglip-base-patch16-224",
+        type=str,
+        choices=model_name_to_checkpoint.keys(),
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
+    )
+    parser.add_argument(
+        "--verify_logits",
+        action="store_false",
+        help="Whether to verify logits against the original implementation.",
+    )
+    parser.add_argument(
+        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
+    )
+
+    args = parser.parse_args()
+    convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)
diff --git a/llava/model/multimodal_encoder/siglip/image_processing_siglip.py b/llava/model/multimodal_encoder/siglip/image_processing_siglip.py
new file mode 100644
index 00000000..833ce336
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/image_processing_siglip.py
@@ -0,0 +1,302 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for SigLIP."""
+
+from typing import Dict, List, Optional, Union
+
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from transformers.image_transforms import (
+    center_crop,
+    resize,
+    rescale,
+    normalize,
+    to_channel_dimension_format,
+    get_resize_output_image_size,
+    get_channel_dimension_axis,
+    convert_to_rgb,
+)
+from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    infer_channel_dimension_format,
+    # is_scaled_image,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+)
+from transformers.utils import TensorType, is_vision_available, logging
+import numpy as np
+
+logger = logging.get_logger(__name__)
+
+def is_scaled_image(image: np.ndarray) -> bool:
+    """
+    Checks to see whether the pixel values have already been rescaled to [0, 1].
+    """
+    if image.dtype == np.uint8:
+        return False
+
+    # It's possible the image has pixel values in [0, 255] but is of floating type
+    return np.min(image) >= 0 and np.max(image) <= 1
+
+
+if is_vision_available():
+    import PIL
+
+
+class SiglipImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a SigLIP image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
+            `do_resize` in the `preprocess` method.
+        size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
+            Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
+            the `preprocess` method.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
+            method.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image by the specified mean and standard deviation. Can be overridden by
+            `do_normalize` in the `preprocess` method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+            Can be overridden by the `image_std` parameter in the `preprocess` method.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        size = size if size is not None else {"shortest_edge": 384}
+        size = get_size_dict(size, default_to_square=False)
+        image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+        
+        self.do_resize = do_resize
+        self.size = size
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def resize(
+        self,
+        image: np.ndarray,
+        size: Dict[str, int],
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
+        resized to keep the input aspect ratio.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            size (`Dict[str, int]`):
+                Size of the output image.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                Resampling filter to use when resiizing the image.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format of the image. If not provided, it will be the same as the input image.
+        """
+        # size = get_size_dict(size, default_to_square=False)
+        default_to_square = True
+        if "shortest_edge" in size:
+            size = size["shortest_edge"]
+            default_to_square = False
+        elif "height" in size and "width" in size:
+            size = (size["height"], size["width"])
+        else:
+            raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
+        output_size = get_resize_output_image_size(image, size=size, default_to_square=default_to_square)
+        return resize(image, size=output_size, resample=resample, data_format=data_format, **kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool = None,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_convert_rgb: bool = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        size = get_size_dict(size, param_name="size", default_to_square=False)
+        resample = resample if resample is not None else self.resample
+        # do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
+        # crop_size = crop_size if crop_size is not None else self.crop_size
+        # crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+
+        images = make_list_of_images(images)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_resize and size is None:
+            raise ValueError("Size must be specified if do_resize is True.")
+
+        if do_rescale and rescale_factor is None:
+            raise ValueError("Rescale factor must be specified if do_rescale is True.")
+
+        if do_normalize and (image_mean is None or image_std is None):
+            raise ValueError("Image mean and std must be specified if do_normalize is True.")
+
+        # PIL RGBA images are converted to RGB
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if is_scaled_image(images[0]) and do_rescale:
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+
+        # if input_data_format is None:
+        #     # We assume that all images have the same channel dimension format.
+        #     input_data_format = infer_channel_dimension_format(images[0])
+        
+        if do_resize:
+            images = [self.resize(image=image, size=size, resample=resample) for image in images]
+
+        if do_rescale:
+            images = [rescale(image=image, scale=rescale_factor) for image in images]
+
+        if do_normalize:
+            output_images = []
+            for image in images:
+                if get_channel_dimension_axis(image) == 0:
+                    image = image.transpose((1, 2, 0))
+                if image.shape[-1] == 1:    
+                    image = np.dstack((image, image, image))
+                output_images.append(image)
+            images = output_images
+            # for image in images:
+            # #     print("image shape", image.shape)
+            #     channel_axis = get_channel_dimension_axis(image)
+            #     num_channels = image.shape[channel_axis]
+            #     if num_channels != len(image_mean):
+            #         print("image_mean", image_mean)
+            #         print("channel_axis", channel_axis)
+            #         print("num_channels", num_channels)
+            #         print("image.shape", image.shape)
+            #         raise ValueError(
+            #             f"Number of channels in the image ({num_channels}) does not match the length of image mean "
+            #             f"({len(image_mean)})."
+            #         )
+
+            images = [normalize(image=image, mean=image_mean, std=image_std) for image in images]
+
+        images = [to_channel_dimension_format(image, data_format) for image in images]
+
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/llava/model/multimodal_encoder/siglip/modeling_siglip.py b/llava/model/multimodal_encoder/siglip/modeling_siglip.py
new file mode 100644
index 00000000..bb0d6497
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/modeling_siglip.py
@@ -0,0 +1,1186 @@
+# coding=utf-8
+# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Siglip model."""
+
+
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+from transformers.activations import ACT2FN
+# from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+# _CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
+
+# SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
+#     "google/siglip-base-patch16-224",
+#     # See all SigLIP models at https://huggingface.co/models?filter=siglip
+# ]
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.0))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    tensor.clamp_(min=a, max=b)
+
+
+def trunc_normal_tf_(
+    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
+) -> torch.Tensor:
+    """Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \\leq \text{mean} \\leq b`.
+
+    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
+    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
+    and the result is subsquently scaled and shifted by the mean and std args.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    """
+    with torch.no_grad():
+        _trunc_normal_(tensor, 0, 1.0, a, b)
+        tensor.mul_(std).add_(mean)
+
+
+def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == "fan_in":
+        denom = fan_in
+    elif mode == "fan_out":
+        denom = fan_out
+    elif mode == "fan_avg":
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
+    elif distribution == "normal":
+        with torch.no_grad():
+            tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        with torch.no_grad():
+            tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
+
+
+def default_flax_embed_init(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="normal")
+
+
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip
+class SiglipVisionModelOutput(ModelOutput):
+    """
+    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
+
+    Args:
+        image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+            The image embeddings obtained by applying the projection layer to the pooler_output.
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    image_embeds: Optional[torch.FloatTensor] = None
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip
+class SiglipTextModelOutput(ModelOutput):
+    """
+    Base class for text model's outputs that also contains a pooling of the last hidden states.
+
+    Args:
+        text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
+            The text embeddings obtained by applying the projection layer to the pooler_output.
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    text_embeds: Optional[torch.FloatTensor] = None
+    last_hidden_state: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip
+class SiglipOutput(ModelOutput):
+    """
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+            Contrastive loss for image-text similarity.
+        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
+            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
+            similarity scores.
+        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
+            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
+            similarity scores.
+        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
+            The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`].
+        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
+            The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`].
+        text_model_output(`BaseModelOutputWithPooling`):
+            The output of the [`SiglipTextModel`].
+        vision_model_output(`BaseModelOutputWithPooling`):
+            The output of the [`SiglipVisionModel`].
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits_per_image: torch.FloatTensor = None
+    logits_per_text: torch.FloatTensor = None
+    text_embeds: torch.FloatTensor = None
+    image_embeds: torch.FloatTensor = None
+    text_model_output: BaseModelOutputWithPooling = None
+    vision_model_output: BaseModelOutputWithPooling = None
+
+    def to_tuple(self) -> Tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+class SiglipVisionEmbeddings(nn.Module):
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        embeddings = patch_embeds.flatten(2).transpose(1, 2)
+
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+        return embeddings
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip
+class SiglipTextEmbeddings(nn.Module):
+    def __init__(self, config: SiglipTextConfig):
+        super().__init__()
+        embed_dim = config.hidden_size
+
+        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
+        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+    ) -> torch.Tensor:
+        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.token_embedding(input_ids)
+
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = inputs_embeds + position_embeddings
+
+        return embeddings
+
+
+class SiglipAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        batch_size, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        k_v_seq_len = key_states.shape[-2]
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale
+
+        if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
+class SiglipMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
+class SiglipEncoderLayer(nn.Module):
+    def __init__(self, config: SiglipConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = SiglipAttention(config)
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+    # Ignore copy
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input to the layer of shape `(batch, seq_len, embed_dim)`.
+            attention_mask (`torch.FloatTensor`):
+                Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+        """
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class SiglipPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = SiglipConfig
+    base_model_prefix = "siglip"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, SiglipVisionEmbeddings):
+            width = (
+                self.config.vision_config.hidden_size
+                if isinstance(self.config, SiglipConfig)
+                else self.config.hidden_size
+            )
+            nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
+        elif isinstance(module, nn.Embedding):
+            default_flax_embed_init(module.weight)
+        elif isinstance(module, SiglipAttention):
+            nn.init.xavier_uniform_(module.q_proj.weight)
+            nn.init.xavier_uniform_(module.k_proj.weight)
+            nn.init.xavier_uniform_(module.v_proj.weight)
+            nn.init.xavier_uniform_(module.out_proj.weight)
+            nn.init.zeros_(module.q_proj.bias)
+            nn.init.zeros_(module.k_proj.bias)
+            nn.init.zeros_(module.v_proj.bias)
+            nn.init.zeros_(module.out_proj.bias)
+        elif isinstance(module, SiglipMLP):
+            nn.init.xavier_uniform_(module.fc1.weight)
+            nn.init.xavier_uniform_(module.fc2.weight)
+            nn.init.normal_(module.fc1.bias, std=1e-6)
+            nn.init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, SiglipMultiheadAttentionPoolingHead):
+            nn.init.xavier_uniform_(module.probe.data)
+            nn.init.xavier_uniform_(module.attention.in_proj_weight.data)
+            nn.init.zeros_(module.attention.in_proj_bias.data)
+        elif isinstance(module, SiglipModel):
+            logit_scale_init = torch.log(torch.tensor(1.0))
+            module.logit_scale.data.fill_(logit_scale_init)
+            module.logit_bias.data.zero_()
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            lecun_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+SIGLIP_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`SiglipConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+SIGLIP_TEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+SIGLIP_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+SIGLIP_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
+class SiglipEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`SiglipEncoderLayer`].
+
+    Args:
+        config: SiglipConfig
+    """
+
+    def __init__(self, config: SiglipConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    # Ignore copy
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    encoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+        )
+
+
+class SiglipTextTransformer(nn.Module):
+    def __init__(self, config: SiglipTextConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        self.embeddings = SiglipTextEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+        self.head = nn.Linear(embed_dim, embed_dim)
+
+    @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is None:
+            raise ValueError("You have to specify input_ids")
+
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)
+
+        # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model.
+        # expand attention_mask
+        # if attention_mask is not None:
+        #     # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+        #     attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.final_layer_norm(last_hidden_state)
+
+        # Assuming "sticky" EOS tokenization, last token is always EOS.
+        pooled_output = last_hidden_state[:, -1, :]
+        pooled_output = self.head(pooled_output)
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """The text model from SigLIP without any head or projection on top.""",
+    SIGLIP_START_DOCSTRING,
+)
+class SiglipTextModel(SiglipPreTrainedModel):
+    config_class = SiglipTextConfig
+
+    _no_split_modules = ["SiglipTextEmbeddings", "SiglipEncoderLayer"]
+
+    def __init__(self, config: SiglipTextConfig):
+        super().__init__(config)
+        self.text_model = SiglipTextTransformer(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.text_model.embeddings.token_embedding
+
+    def set_input_embeddings(self, value):
+        self.text_model.embeddings.token_embedding = value
+
+    @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, SiglipTextModel
+
+        >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> # important: make sure to set padding="max_length" as that's how the model was trained
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        return self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+
+class SiglipVisionTransformer(nn.Module):
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = SiglipVisionEmbeddings(config)
+        self.encoder = SiglipEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.head = SiglipMultiheadAttentionPoolingHead(config)
+
+    @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
+    def forward(
+        self,
+        pixel_values,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        hidden_states = self.embeddings(pixel_values)
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.post_layernorm(last_hidden_state)
+
+        pooled_output = self.head(last_hidden_state)
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class SiglipMultiheadAttentionPoolingHead(nn.Module):
+    """Multihead Attention Pooling."""
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__()
+
+        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
+        self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True)
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(config)
+
+    def forward(self, hidden_state):
+        batch_size = hidden_state.shape[0]
+        probe = self.probe.repeat(batch_size, 1, 1)
+
+        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]
+
+        residual = hidden_state
+        hidden_state = self.layernorm(hidden_state)
+        hidden_state = residual + self.mlp(hidden_state)
+
+        return hidden_state[:, 0]
+
+
+@add_start_docstrings(
+    """The vision model from SigLIP without any head or projection on top.""",
+    SIGLIP_START_DOCSTRING,
+)
+class SiglipVisionModel(SiglipPreTrainedModel):
+    config_class = SiglipVisionConfig
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: SiglipVisionConfig):
+        super().__init__(config)
+
+        self.vision_model = SiglipVisionTransformer(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.vision_model.embeddings.patch_embedding
+
+    @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig)
+    def forward(
+        self,
+        pixel_values,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, SiglipVisionModel
+
+        >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled features
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        return self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+
+@add_start_docstrings(SIGLIP_START_DOCSTRING)
+class SiglipModel(SiglipPreTrainedModel):
+    config_class = SiglipConfig
+
+    def __init__(self, config: SiglipConfig):
+        super().__init__(config)
+
+        if not isinstance(config.text_config, SiglipTextConfig):
+            raise ValueError(
+                "config.text_config is expected to be of type SiglipTextConfig but is of type"
+                f" {type(config.text_config)}."
+            )
+
+        if not isinstance(config.vision_config, SiglipVisionConfig):
+            raise ValueError(
+                "config.vision_config is expected to be of type SiglipVisionConfig but is of type"
+                f" {type(config.vision_config)}."
+            )
+
+        text_config = config.text_config
+        vision_config = config.vision_config
+
+        self.text_model = SiglipTextTransformer(text_config)
+        self.vision_model = SiglipVisionTransformer(vision_config)
+
+        self.logit_scale = nn.Parameter(torch.randn(1))
+        self.logit_bias = nn.Parameter(torch.randn(1))
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING)
+    def get_text_features(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        Returns:
+            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
+            applying the projection layer to the pooled output of [`SiglipTextModel`].
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModel
+        >>> import torch
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> # important: make sure to set padding="max_length" as that's how the model was trained
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
+        >>> with torch.no_grad():
+        ...     text_features = model.get_text_features(**inputs)
+        ```"""
+        # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = text_outputs[1]
+
+        return pooled_output
+
+    @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING)
+    def get_image_features(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        r"""
+        Returns:
+            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
+            applying the projection layer to the pooled output of [`SiglipVisionModel`].
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, AutoModel
+        >>> import torch
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> inputs = processor(images=image, return_tensors="pt")
+
+        >>> with torch.no_grad():
+        ...     image_features = model.get_image_features(**inputs)
+        ```"""
+        # Use SiglipModel's config for some fields (if specified) instead of those of vision & text components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = vision_outputs[1]
+
+        return pooled_output
+
+    @add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=SiglipOutput, config_class=SiglipConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        return_loss: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SiglipOutput]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, AutoModel
+        >>> import torch
+
+        >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
+        >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
+        >>> inputs = processor(text=texts, images=image, return_tensors="pt")
+
+        >>> with torch.no_grad():
+        ...     outputs = model(**inputs)
+
+        >>> logits_per_image = outputs.logits_per_image
+        >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
+        >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
+        31.9% that image 0 is 'a photo of 2 cats'
+        ```"""
+        # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components.
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        image_embeds = vision_outputs[1]
+        text_embeds = text_outputs[1]
+
+        # normalized features
+        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
+        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
+
+        # cosine similarity as logits
+        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * self.logit_scale.exp() + self.logit_bias
+        logits_per_image = logits_per_text.t()
+
+        loss = None
+        if return_loss:
+            raise NotImplementedError("SigLIP loss to be implemented")
+
+        if not return_dict:
+            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
+            return ((loss,) + output) if loss is not None else output
+
+        return SiglipOutput(
+            loss=loss,
+            logits_per_image=logits_per_image,
+            logits_per_text=logits_per_text,
+            text_embeds=text_embeds,
+            image_embeds=image_embeds,
+            text_model_output=text_outputs,
+            vision_model_output=vision_outputs,
+        )
diff --git a/llava/model/multimodal_encoder/siglip/processing_siglip.py b/llava/model/multimodal_encoder/siglip/processing_siglip.py
new file mode 100644
index 00000000..5c529512
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/processing_siglip.py
@@ -0,0 +1,143 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Image/Text processor class for SigLIP.
+"""
+
+from typing import List, Optional, Union
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from transformers.utils import TensorType
+
+
+class SiglipProcessor(ProcessorMixin):
+    r"""
+    Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
+
+    [`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
+    [`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
+
+    Args:
+        image_processor ([`SiglipImageProcessor`]):
+            The image processor is a required input.
+        tokenizer ([`SiglipTokenizer`]):
+            The tokenizer is a required input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "SiglipImageProcessor"
+    tokenizer_class = "SiglipTokenizer"
+
+    def __init__(self, image_processor, tokenizer):
+        super().__init__(image_processor, tokenizer)
+
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        images: ImageInput = None,
+        padding: Union[bool, str, PaddingStrategy] = "max_length",
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length=None,
+        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` argument to
+        SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
+        of the above two methods for more information.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are image height and width.
+            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `max_length`):
+                Select a strategy to pad the returned sequences (according to the model's padding side and padding
+                index) among:
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            max_length (`int`, *optional*):
+                Maximum length of the returned list and optionally padding length (see above).
+            truncation (`bool`, *optional*):
+                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+
+        if text is None and images is None:
+            raise ValueError("You have to specify either text or images. Both cannot be none.")
+
+        if text is not None:
+            encoding = self.tokenizer(
+                text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
+            )
+
+        if images is not None:
+            image_features = self.image_processor(images, return_tensors=return_tensors)
+
+        if text is not None and images is not None:
+            encoding["pixel_values"] = image_features.pixel_values
+            return encoding
+        elif text is not None:
+            return encoding
+        else:
+            return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
diff --git a/llava/model/multimodal_encoder/siglip/tokenization_siglip.py b/llava/model/multimodal_encoder/siglip/tokenization_siglip.py
new file mode 100644
index 00000000..b743f706
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip/tokenization_siglip.py
@@ -0,0 +1,389 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Tokenization class for SigLIP model."""
+
+import os
+import re
+import string
+import warnings
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.convert_slow_tokenizer import import_protobuf
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.tokenization_utils_base import AddedToken
+
+
+if TYPE_CHECKING:
+    from transformers.tokenization_utils_base import TextInput
+from transformers.utils import logging, requires_backends
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/spiece.model",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "google/siglip-base-patch16-224": 256,
+}
+
+SPIECE_UNDERLINE = "▁"
+
+
+class SiglipTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"</s>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        additional_special_tokens (`List[str]`, *optional*):
+            Additional special tokens used by the tokenizer.
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+        model_max_length (`int`, *optional*, defaults to 64):
+            The maximum length (in number of tokens) for model inputs.
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="</s>",
+        additional_special_tokens=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        model_max_length=64,
+        do_lower_case=True,
+        **kwargs,
+    ) -> None:
+        requires_backends(self, "protobuf")
+
+        pad_token = (
+            AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(pad_token, str)
+            else pad_token
+        )
+        unk_token = (
+            AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(unk_token, str)
+            else unk_token
+        )
+        eos_token = (
+            AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        self.do_lower_case = do_lower_case
+        self.vocab_file = vocab_file
+
+        self.sp_model = self.get_spm_processor()
+        self.vocab_file = vocab_file
+
+        super().__init__(
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            additional_special_tokens=additional_special_tokens,
+            sp_model_kwargs=self.sp_model_kwargs,
+            model_max_length=model_max_length,
+            do_lower_case=do_lower_case,
+            **kwargs,
+        )
+
+    def get_spm_processor(self):
+        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        with open(self.vocab_file, "rb") as f:
+            sp_model = f.read()
+            model_pb2 = import_protobuf()
+            model = model_pb2.ModelProto.FromString(sp_model)
+            normalizer_spec = model_pb2.NormalizerSpec()
+            normalizer_spec.add_dummy_prefix = False
+            model.normalizer_spec.MergeFrom(normalizer_spec)
+            sp_model = model.SerializeToString()
+            tokenizer.LoadFromSerializedProto(sp_model)
+        return tokenizer
+
+    @property
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.vocab_size
+    def vocab_size(self):
+        return self.sp_model.get_piece_size()
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        # normal case: some special tokens
+        if token_ids_1 is None:
+            return ([0] * len(token_ids_0)) + [1]
+        return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
+    def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
+        """Do not add eos again if user already added it."""
+        if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
+            warnings.warn(
+                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
+                " eos tokens being added."
+            )
+            return token_ids
+        else:
+            return token_ids + [self.eos_token_id]
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A sequence has the following format:
+
+        - single sequence: `X </s>`
+        - pair of sequences: `A </s> B </s>`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        token_ids_0 = self._add_eos_if_not_present(token_ids_0)
+        if token_ids_1 is None:
+            return token_ids_0
+        else:
+            token_ids_1 = self._add_eos_if_not_present(token_ids_1)
+            return token_ids_0 + token_ids_1
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__setstate__
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    def remove_punctuation(self, text: str) -> str:
+        return text.translate(str.maketrans("", "", string.punctuation))
+
+    # source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
+    def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
+        """Returns canonicalized `text` (puncuation removed).
+
+        Args:
+            text (`str`):
+                String to be canonicalized.
+            keep_punctuation_exact_string (`str`, *optional*):
+                If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
+                (but will still remove '{' and '}' that appear separately).
+        """
+        if keep_punctuation_exact_string:
+            text = keep_punctuation_exact_string.join(
+                self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
+            )
+        else:
+            text = self.remove_punctuation(text)
+        text = re.sub(r"\s+", " ", text)
+        text = text.strip()
+
+        return text
+
+    def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
+        """
+        Converts a string to a list of tokens.
+        """
+        tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
+
+        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+            tokens = tokens[1:]
+        return tokens
+
+    @property
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.unk_token_length
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string.
+
+        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+        SPIECE_UNDERLINE.
+
+        For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.
+
+        Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
+        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
+        """
+        text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
+        tokens = self.sp_model.encode(text, out_type=str)
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_token_to_id
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_id_to_token
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.convert_tokens_to_string
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        # since we manually add the prefix space, we have to remove it
+        tokens[0] = tokens[0].lstrip(SPIECE_UNDERLINE)
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.save_vocabulary
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
diff --git a/llava/model/multimodal_encoder/siglip_encoder.py b/llava/model/multimodal_encoder/siglip_encoder.py
new file mode 100644
index 00000000..679d7428
--- /dev/null
+++ b/llava/model/multimodal_encoder/siglip_encoder.py
@@ -0,0 +1,39 @@
+import torch
+from llava.model.multimodal_encoder.vision_encoder import VisionTower, VisionTowerS2
+
+from transformers import AutoConfig, PretrainedConfig, AutoModel
+from .siglip import (
+    SiglipVisionConfig,
+    SiglipVisionModel,
+    SiglipImageProcessor,
+)
+
+
+class SiglipVisionTower(VisionTower):
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig, state_dict=None):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = SiglipVisionModel.from_pretrained(
+            # TODO(ligeng): why pass config here leading to errors?
+            model_name_or_path, torch_dtype=eval(config.model_dtype), state_dict=state_dict
+        )
+        self.is_loaded = True
+
+
+class SiglipVisionTowerS2(VisionTowerS2):
+    def __init__(self, model_name_or_path: str, config: PretrainedConfig):
+        super().__init__(model_name_or_path, config)
+        self.image_processor = SiglipImageProcessor.from_pretrained(model_name_or_path)
+        self.vision_tower = SiglipVisionModel.from_pretrained(
+            model_name_or_path, torch_dtype=eval(config.model_dtype)
+        )
+
+        # Make sure it crops/resizes the image to the largest scale in self.scales to maintain high-res information
+        self.image_processor.size['height'] = self.image_processor.size['width'] = self.scales[-1]
+
+        self.is_loaded = True
+
+
+AutoConfig.register("siglip_vision_model", SiglipVisionConfig)
+AutoModel.register(SiglipVisionConfig, SiglipVisionModel)
+
diff --git a/llava/model/multimodal_encoder/vision_encoder.py b/llava/model/multimodal_encoder/vision_encoder.py
index 244f8df0..9cdc2e6d 100644
--- a/llava/model/multimodal_encoder/vision_encoder.py
+++ b/llava/model/multimodal_encoder/vision_encoder.py
@@ -1,8 +1,14 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
 
 from abc import abstractmethod
+
 import torch
 import torch.nn as nn
+from accelerate.hooks import add_hook_to_module
+from transformers import AutoConfig, PreTrainedModel
+from transformers.image_processing_utils import BaseImageProcessor
+from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from s2wrapper import forward as multiscale_forward
 
 
 class VisionTower(nn.Module):
@@ -12,36 +18,113 @@ def __init__(self, vision_tower, args, delay_load=False):
         self.is_loaded = False
 
         self.vision_tower_name = vision_tower
-        self.select_layer = args.mm_vision_select_layer
-        self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
+        self.select_layer = getattr(args, "mm_vision_select_layer", -2)
+        self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
 
         self.cfg_only = None
 
-    @abstractmethod
-    def load_model(self):
-        pass
-        
-
     def feature_select(self, image_forward_outs):
         image_features = image_forward_outs.hidden_states[self.select_layer]
-        if self.select_feature == 'patch':
+        if self.select_feature == "patch":
             image_features = image_features[:, 1:]
-        elif self.select_feature == 'cls_patch':
+        elif self.select_feature == "cls_patch":
             image_features = image_features
         else:
-            raise ValueError(f'Unexpected select feature: {self.select_feature}')
+            raise ValueError(f"Unexpected select feature: {self.select_feature}")
         return image_features
 
-    @torch.no_grad()
+    def _maybe_resize_pos_embeds(
+        self,
+        model: PreTrainedModel,
+        image_processor: BaseImageProcessor,
+        resolution: int = -1,
+        interpolate_mode: str = "linear",
+    ):
+        if resolution in [model.config.image_size, -1]:
+            return
+        print(f"Resizing vision model's position embeddings to support higher vision resolution: from {model.config.image_size} to {resolution} ...")
+        embeddings = model.vision_model.embeddings
+        patch_size = embeddings.patch_size
+        num_new_tokens = int((resolution // patch_size) ** 2)
+
+        old_embeddings = embeddings.position_embedding
+        match interpolate_mode:
+            case "linear":
+                ## Step 1: Calculate the corresponding patch ID (pid) in the current resolution (M patches) based on the target resolution (N patches). Formula: pid = pid / N * M
+                ## Step 2:  Obtain new embeddings by interpolating between the embeddings of the two nearest calculated patch IDs. Formula: new_embeds = (pid - floor(pid)) * embeds[ceil(pid)] + (ceil(pid) - pid) * embeds[floor(pid)]
+                import torch
+                import torch.nn as nn
+
+                if is_deepspeed_zero3_enabled():
+                    import deepspeed
+
+                    with deepspeed.zero.GatheredParameters([old_embeddings.weight], modifier_rank=None):
+                        old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
+                else:
+                    old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
+                new_embeddings = nn.Embedding(
+                    num_new_tokens,
+                    old_embedding_dim,
+                    dtype=old_embeddings.weight.dtype,
+                    device=old_embeddings.weight.device,
+                )
+                mapped_indices = (
+                    torch.arange(num_new_tokens).to(old_embeddings.weight.device)
+                    / (num_new_tokens - 1)
+                    * (old_num_tokens - 1)
+                )
+                floor_indices = torch.clamp(mapped_indices.floor().long(), min=0, max=old_num_tokens - 1)
+                ceil_indices = torch.clamp(mapped_indices.ceil().long(), min=0, max=old_num_tokens - 1)
+                if is_deepspeed_zero3_enabled():
+                    params = [old_embeddings.weight, new_embeddings.weight]
+                    with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                        interpolated_embeds = (mapped_indices - floor_indices)[:, None] * old_embeddings.weight.data[
+                            ceil_indices, :
+                        ] + (ceil_indices - mapped_indices)[:, None] * old_embeddings.weight.data[floor_indices, :]
+                else:
+                    interpolated_embeds = (mapped_indices - floor_indices)[:, None] * old_embeddings.weight.data[
+                        ceil_indices, :
+                    ] + (ceil_indices - mapped_indices)[:, None] * old_embeddings.weight.data[floor_indices, :]
+                new_embeddings.weight.data = interpolated_embeds
+            case _:
+                raise NotImplementedError
+
+        if hasattr(old_embeddings, "_hf_hook"):
+            hook = old_embeddings._hf_hook
+            add_hook_to_module(new_embeddings, hook)
+        new_embeddings.requires_grad_(old_embeddings.weight.requires_grad)
+        ## update vision encoder's configurations
+        model.config.image_size = resolution
+        if hasattr(image_processor, "crop_size"):
+            # CLIP vision tower
+            image_processor.crop_size = resolution
+        else:
+            # SIGLIP vision tower
+            assert hasattr(image_processor, "size")
+            image_processor.size = {"height": resolution, "width": resolution}
+        ## TODO define a '_reinitialize' method for VisionTower
+        embeddings.position_embedding = new_embeddings
+        embeddings.image_size = resolution
+        embeddings.num_patches = embeddings.num_positions = num_new_tokens
+        embeddings.position_ids = (
+            torch.arange(embeddings.num_positions).expand((1, -1)).to(old_embeddings.weight.device)
+        )
+
     def forward(self, images):
         if type(images) is list:
             image_features = []
             for image in images:
-                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
+                image_forward_out = self.vision_tower(
+                    image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
+                    output_hidden_states=True,
+                )
                 image_feature = self.feature_select(image_forward_out).to(image.dtype)
                 image_features.append(image_feature)
         else:
-            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
+            image_forward_outs = self.vision_tower(
+                images.to(device=self.device, dtype=self.dtype),
+                output_hidden_states=True,
+            )
             image_features = self.feature_select(image_forward_outs).to(images.dtype)
 
         return image_features
@@ -72,3 +155,40 @@ def hidden_size(self):
     @property
     def num_patches(self):
         return (self.config.image_size // self.config.patch_size) ** 2
+
+
+class VisionTowerS2(VisionTower):
+    def __init__(self, vision_tower, args, delay_load=False):
+        super().__init__(vision_tower, args, delay_load)
+
+        self.scales = list(map(int, args.s2_scales.split(',')))
+        self.scales.sort()
+        self.max_split_size = args.s2_max_split_size
+
+    @torch.no_grad()
+    def forward_feature(self, images):
+        image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
+        image_features = self.feature_select(image_forward_outs).to(images.dtype)
+        return image_features
+
+    @torch.no_grad()
+    def forward(self, images):
+        if type(images) is list:
+            image_features = []
+            for image in images:
+                image_feature = multiscale_forward(self.forward_feature,
+                                                   image.unsqueeze(0),
+                                                   img_sizes=self.scales,
+                                                   max_split_size=self.max_split_size)
+                image_features.append(image_feature)
+        else:
+            image_features = multiscale_forward(self.forward_feature,
+                                                images,
+                                                img_sizes=self.scales,
+                                                max_split_size=self.max_split_size)
+
+        return image_features
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size * len(self.scales)
diff --git a/llava/model/multimodal_encoder/visualize_features.py b/llava/model/multimodal_encoder/visualize_features.py
new file mode 100644
index 00000000..7ef12de6
--- /dev/null
+++ b/llava/model/multimodal_encoder/visualize_features.py
@@ -0,0 +1,351 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+import argparse
+from collections import defaultdict
+import gc
+import math
+import os
+from PIL import Image
+import random
+from tqdm import tqdm
+from typing import Any, Dict, Iterable, List, Tuple
+
+#import cv2
+import numpy as np
+import torch
+from torch import nn
+import torch.distributed as dist
+from torch.utils.data import DataLoader
+import torch.nn.functional as F
+from torchvision.utils import make_grid
+
+from einops import rearrange
+
+from datasets import load_dataset_builder, load_dataset
+from datasets.distributed import split_dataset_by_node
+
+#from common import rank_print, load_model, get_standard_transform, collate
+#
+#try:
+#    import wandb
+#except ImportError:
+#    wandb = None
+
+
+LAYER_STATS = dict()
+
+
+@torch.inference_mode()
+def main(rank: int = 0, world_size: int = 1):
+    '''
+    Computes the RankMe (http://arxiv.org/abs/2210.02885) and LiDAR (http://arxiv.org/abs/2312.04000)
+    estimates of the rank of the produced embeddings. While RADIO doesn't train in a multi-view setting
+    which is an assumption of LiDAR, the metric does integrate an important concept of the invariance of the
+    summary features to different view/augmentations of the same image.
+    '''
+
+    local_rank = rank % torch.cuda.device_count()
+    torch.cuda.set_device(local_rank)
+    cv2.setNumThreads(1)
+
+    device = torch.device('cuda', local_rank)
+    parser = argparse.ArgumentParser(description='Compute SSL embedding rank estimates')
+    parser.add_argument('-v', '--model-version', default='radio_v2',
+                        help='Which radio model to load.'
+    )
+    parser.add_argument('-d', '--dataset', default='imagenet-1k',
+                        help='The name of the dataset to classify'
+    )
+    parser.add_argument('--split', default='validation',
+                        help='The dataset split to use.'
+    )
+    parser.add_argument('-n', default=10, type=int, help='The number of samples to load')
+    parser.add_argument('-r', '--resolution', nargs='+', type=int, default=None,
+                        help='The input image resolution.'
+                             ' If one value is specified, the shortest dimension is resized to this.'
+                             ' If two, the image is center cropped.'
+                             ' If not specified, center cropped 378px is used.'
+                             ' Default: The RADIO model\'s preferred resolution.'
+    )
+    parser.add_argument('--resize-multiple', type=int, default=None,
+                        help='Resize images with dimensions a multiple of this value.'
+                             ' This should be equal to the patch size of a ViT (e.g. RADIOv1)'
+    )
+    parser.add_argument('--batch-size', type=int, default=16,
+                        help='The batch size. If the input is variable sized, then this argument becomes a maximum.'
+    )
+    parser.add_argument('--workers', default=8, type=int, help='Number of loader workers to use')
+    parser.add_argument('--vitdet-window-size', default=None, type=int, help='Enable ViTDet at the specific window size')
+    parser.add_argument('--output-dir', default='vis_denoise', type=str)
+    parser.add_argument('--adaptor-name', default=None, type=str, help='Generate features from a teacher adaptor')
+
+    args, _ = parser.parse_known_args()
+
+    torch.manual_seed(42 + rank)
+    np.random.seed(42 + rank)
+    random.seed(42 + rank)
+
+    rank_print('Loading model...')
+    model, preprocessor, info = load_model(args.model_version, vitdet_window_size=args.vitdet_window_size, adaptor_name=args.adaptor_name)
+    model.to(device=device).eval()
+    if isinstance(preprocessor, nn.Module):
+        preprocessor.to(device).eval()
+    rank_print('Done')
+
+    rank_print('Loading dataset...')
+    ds_builder = load_dataset_builder(args.dataset, trust_remote_code=True)
+
+    if args.resolution is None:
+        args.resolution = (model.preferred_resolution.height, model.preferred_resolution.width)
+
+    patch_size = model.patch_size
+
+    if args.resize_multiple is None:
+        args.resize_multiple = getattr(model, 'min_resolution_step', model.patch_size)
+
+    transform = get_standard_transform(args.resolution, args.resize_multiple)
+    dataset = ds_builder.as_dataset(split=args.split)
+    dataset = dataset.to_iterable_dataset(num_shards=world_size * max(1, args.workers))
+    dataset = split_dataset_by_node(dataset, rank=rank, world_size=world_size)
+    dataset = dataset.map(lambda ex: dict(image=transform(ex['image']), label=torch.as_tensor(ex['label'], dtype=torch.int64)))
+
+    loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False,
+                        num_workers=args.workers, collate_fn=collate,
+                        pin_memory=args.workers > 0,
+                        drop_last=False,
+    )
+    rank_print('Done')
+    rank_print(f'Description: {ds_builder.info.description}')
+
+    dirs = dict(
+        orig=os.path.join(args.output_dir, 'orig'),
+        viz=os.path.join(args.output_dir, 'viz'),
+        sbs=os.path.join(args.output_dir, 'sbs'),
+    )
+
+    for d in dirs.values():
+        os.makedirs(d, exist_ok=True)
+
+    ctr = 0
+    for batches in loader:
+        if ctr >= args.n:
+            break
+
+        for images, _ in batches:
+            images = images.to(device=device, non_blocking=True)
+
+            all_feat = []
+            with torch.autocast(device.type, dtype=torch.bfloat16):
+                p_images = preprocessor(images)
+
+                output = model(p_images)
+                if args.adaptor_name:
+                    all_feat = [
+                        output['backbone'].features,
+                        output[args.adaptor_name].features,
+                    ]
+                else:
+                    all_feat = [output[1]]
+
+            all_feat = torch.stack(all_feat, dim=1)
+
+            num_rows = images.shape[-2] // patch_size
+            num_cols = images.shape[-1] // patch_size
+
+            all_feat = rearrange(all_feat, 'b m (h w) c -> b m h w c', h=num_rows, w=num_cols).float()
+
+            for i, feats in enumerate(all_feat):
+                colored = []
+                for features in feats:
+                    color = get_pca_map(features, images.shape[-2:])
+                    colored.append(color)
+
+                orig = cv2.cvtColor(images[i].permute(1, 2, 0).cpu().numpy(), cv2.COLOR_RGB2BGR)
+
+                cv2.imwrite(f'{dirs["orig"]}/vis_{ctr}.jpg', orig * 255)
+                cv2.imwrite(f'{dirs["viz"]}/vis_{ctr}.jpg', colored[-1] * 255)
+
+                op = np.concatenate([orig] + colored, axis=1) * 255
+
+                cv2.imwrite(f'{dirs["sbs"]}/vis_{ctr}.jpg', op)
+                ctr += 1
+
+
+def get_robust_pca(features: torch.Tensor, m: float = 2, remove_first_component=False):
+    # features: (N, C)
+    # m: a hyperparam controlling how many std dev outside for outliers
+    assert len(features.shape) == 2, "features should be (N, C)"
+    reduction_mat = torch.pca_lowrank(features, q=3, niter=20)[2]
+    colors = features @ reduction_mat
+    if remove_first_component:
+        colors_min = colors.min(dim=0).values
+        colors_max = colors.max(dim=0).values
+        tmp_colors = (colors - colors_min) / (colors_max - colors_min)
+        fg_mask = tmp_colors[..., 0] < 0.2
+        reduction_mat = torch.pca_lowrank(features[fg_mask], q=3, niter=20)[2]
+        colors = features @ reduction_mat
+    else:
+        fg_mask = torch.ones_like(colors[:, 0]).bool()
+    d = torch.abs(colors[fg_mask] - torch.median(colors[fg_mask], dim=0).values)
+    mdev = torch.median(d, dim=0).values
+    s = d / mdev
+    try:
+        rins = colors[fg_mask][s[:, 0] < m, 0]
+        gins = colors[fg_mask][s[:, 1] < m, 1]
+        bins = colors[fg_mask][s[:, 2] < m, 2]
+        rgb_min = torch.tensor([rins.min(), gins.min(), bins.min()])
+        rgb_max = torch.tensor([rins.max(), gins.max(), bins.max()])
+    except:
+        rins = colors
+        gins = colors
+        bins = colors
+        rgb_min = torch.tensor([rins.min(), gins.min(), bins.min()])
+        rgb_max = torch.tensor([rins.max(), gins.max(), bins.max()])
+
+    return reduction_mat, rgb_min.to(reduction_mat), rgb_max.to(reduction_mat)
+
+
+def get_pca_map(
+    feature_map: torch.Tensor,
+    img_size,
+    interpolation="bicubic",
+    return_pca_stats=False,
+    pca_stats=None,
+):
+    """
+    feature_map: (1, h, w, C) is the feature map of a single image.
+    """
+    feature_map = feature_map.float()
+    if feature_map.shape[0] != 1:
+        # make it (1, h, w, C)
+        feature_map = feature_map[None]
+    if pca_stats is None:
+        reduct_mat, color_min, color_max = get_robust_pca(
+            feature_map.reshape(-1, feature_map.shape[-1])
+        )
+    else:
+        reduct_mat, color_min, color_max = pca_stats
+    pca_color = feature_map @ reduct_mat
+    pca_color = (pca_color - color_min) / (color_max - color_min)
+    pca_color = F.interpolate(
+        pca_color.permute(0, 3, 1, 2),
+        size=img_size,
+        mode=interpolation,
+    ).permute(0, 2, 3, 1)
+    pca_color = pca_color.clamp(0, 1)
+    pca_color = pca_color.cpu().numpy().squeeze(0)
+    if return_pca_stats:
+        return pca_color, (reduct_mat, color_min, color_max)
+    return pca_color
+
+
+def get_scale_map(
+    scalar_map: torch.Tensor,
+    img_size,
+    interpolation="nearest",
+):
+    """
+    scalar_map: (1, h, w, C) is the feature map of a single image.
+    """
+    if scalar_map.shape[0] != 1:
+        scalar_map = scalar_map[None]
+    scalar_map = (scalar_map - scalar_map.min()) / (
+        scalar_map.max() - scalar_map.min() + 1e-6
+    )
+    scalar_map = F.interpolate(
+        scalar_map.permute(0, 3, 1, 2),
+        size=img_size,
+        mode=interpolation,
+    ).permute(0, 2, 3, 1)
+    # cmap = plt.get_cmap("viridis")
+    # scalar_map = cmap(scalar_map)[..., :3]
+    # make it 3 channels
+    scalar_map = torch.cat([scalar_map] * 3, dim=-1)
+    scalar_map = scalar_map.cpu().numpy().squeeze(0)
+    return scalar_map
+
+
+def get_similarity_map(features: torch.Tensor, img_size=(224, 224)):
+    """
+    compute the similarity map of the central patch to the rest of the image
+    """
+    assert len(features.shape) == 4, "features should be (1, C, H, W)"
+    H, W, C = features.shape[1:]
+    center_patch_feature = features[0, H // 2, W // 2, :]
+    center_patch_feature_normalized = center_patch_feature / center_patch_feature.norm()
+    center_patch_feature_normalized = center_patch_feature_normalized.unsqueeze(1)
+    # Reshape and normalize the entire feature tensor
+    features_flat = features.view(-1, C)
+    features_normalized = features_flat / features_flat.norm(dim=1, keepdim=True)
+
+    similarity_map_flat = features_normalized @ center_patch_feature_normalized
+    # Reshape the flat similarity map back to the spatial dimensions (H, W)
+    similarity_map = similarity_map_flat.view(H, W)
+
+    # Normalize the similarity map to be in the range [0, 1] for visualization
+    similarity_map = (similarity_map - similarity_map.min()) / (
+        similarity_map.max() - similarity_map.min()
+    )
+    # we don't want the center patch to be the most similar
+    similarity_map[H // 2, W // 2] = -1.0
+    similarity_map = (
+        F.interpolate(
+            similarity_map.unsqueeze(0).unsqueeze(0),
+            size=img_size,
+            mode="bilinear",
+        )
+        .squeeze(0)
+        .squeeze(0)
+    )
+
+    similarity_map_np = similarity_map.cpu().numpy()
+    negative_mask = similarity_map_np < 0
+
+    colormap = plt.get_cmap("turbo")
+
+    # Apply the colormap directly to the normalized similarity map and multiply by 255 to get RGB values
+    similarity_map_rgb = colormap(similarity_map_np)[..., :3]
+    similarity_map_rgb[negative_mask] = [1.0, 0.0, 0.0]
+    return similarity_map_rgb
+
+
+def get_cluster_map(
+    feature_map: torch.Tensor,
+    img_size,
+    num_clusters=10,
+) -> torch.Tensor:
+    kmeans = KMeans(n_clusters=num_clusters, distance=CosineSimilarity, verbose=False)
+    if feature_map.shape[0] != 1:
+        # make it (1, h, w, C)
+        feature_map = feature_map[None]
+    labels = kmeans.fit_predict(
+        feature_map.reshape(1, -1, feature_map.shape[-1])
+    ).float()
+    labels = (
+        F.interpolate(
+            labels.reshape(1, *feature_map.shape[:-1]), size=img_size, mode="nearest"
+        )
+        .squeeze()
+        .cpu()
+        .numpy()
+    ).astype(int)
+    cmap = plt.get_cmap("rainbow", num_clusters)
+    cluster_map = cmap(labels)[..., :3]
+    return cluster_map.reshape(img_size[0], img_size[1], 3)
+
+
+if __name__ == '__main__':
+    rank = 0
+    world_size = 1
+
+    # if 'WORLD_SIZE' in os.environ:
+    #     dist.init_process_group(backend='nccl')
+    #     rank = dist.get_rank()
+    #     world_size = dist.get_world_size()
+
+    main(rank, world_size)
diff --git a/llava/model/multimodal_projector/base_projector.py b/llava/model/multimodal_projector/base_projector.py
new file mode 100644
index 00000000..ed2ae1be
--- /dev/null
+++ b/llava/model/multimodal_projector/base_projector.py
@@ -0,0 +1,100 @@
+import torch.nn as nn
+import re
+import torch
+from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
+
+
+class IdentityMap(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, *args, **kwargs):
+        return x
+
+    @property
+    def config(self):
+        return {"mm_projector_type": "identity"}
+
+
+class SimpleResBlock(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(channels)
+
+        self.proj = nn.Sequential(
+            nn.Linear(channels, channels), nn.GELU(), nn.Linear(channels, channels)
+        )
+
+    def forward(self, x):
+        x = self.pre_norm(x)
+        return x + self.proj(x)
+
+
+class DownSampleBlock(nn.Module):
+
+    def forward(self, x):
+        vit_embeds = x
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.flat_square(vit_embeds)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        return vit_embeds
+
+    def flat_square(self, x):
+        n, w, h, c = x.size()
+        if w % 2 == 1:
+            x = torch.concat([x, torch.zeros((n, 1, h, c), dtype=x.dtype).to(x.device)], dim=1).contiguous()
+            n, w, h, c = x.size()
+        if h % 2 == 1:
+            x = torch.concat([x, torch.zeros((n, w, 1, c), dtype=x.dtype).to(x.device)], dim=2).contiguous()
+            n, w, h, c = x.size()
+        x = x.view(n, w, int(h / 2), int(c * 2))
+        x = x.permute(0, 2, 1, 3).contiguous()
+        x = x.view(n, int(h / 2), int(w / 2), int(c * 4))
+        return x
+
+class MultimodalProjectorConfig(PretrainedConfig):
+    model_type = "v2l_projector"
+
+    def __init__(self, mm_projector_type: str=None, **kwargs):
+        super().__init__()
+        self.mm_projector_type = mm_projector_type
+
+
+class MultimodalProjector(PreTrainedModel):
+    config_class = MultimodalProjectorConfig
+
+    def __init__(
+        self, mm_projector_cfg: MultimodalProjectorConfig, config: PretrainedConfig
+    ):
+        super().__init__(mm_projector_cfg)
+        mm_projector_type = mm_projector_cfg.mm_projector_type
+        if mm_projector_type == "identity":
+            self.layers = IdentityMap()
+        elif mm_projector_type == "linear":
+            self.layers = nn.Linear(config.mm_hidden_size, config.hidden_size)
+        elif mm_projector_type == "mlp_downsample":
+            self.layers = nn.Sequential(
+                DownSampleBlock(),
+                nn.LayerNorm(config.mm_hidden_size * 4),
+                nn.Linear(config.mm_hidden_size * 4, config.hidden_size),
+                nn.GELU(),
+                nn.Linear(config.hidden_size, config.hidden_size)
+            )
+        else:
+            mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", mm_projector_type)
+            if mlp_gelu_match:
+                mlp_depth = int(mlp_gelu_match.group(1))
+                modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
+                for _ in range(1, mlp_depth):
+                    modules.append(nn.GELU())
+                    modules.append(nn.Linear(config.hidden_size, config.hidden_size))
+                self.layers = nn.Sequential(*modules)
+            else:
+                raise ValueError(f"Unknown projector type: {mm_projector_type}")
+
+    def forward(self, x, *args, **kwargs):
+        return self.layers(x)
+
+AutoConfig.register("v2l_projector", MultimodalProjectorConfig)
+AutoModel.register(MultimodalProjectorConfig, MultimodalProjector)
\ No newline at end of file
diff --git a/llava/model/multimodal_projector/builder.py b/llava/model/multimodal_projector/builder.py
index a62d56cf..f2854975 100644
--- a/llava/model/multimodal_projector/builder.py
+++ b/llava/model/multimodal_projector/builder.py
@@ -1,53 +1,30 @@
 # This file is modified from https://github.com/haotian-liu/LLaVA/
 
+import os
 import torch
-import torch.nn as nn
-import re
 
+from .base_projector import MultimodalProjectorConfig, MultimodalProjector
+from transformers import PretrainedConfig, PreTrainedModel
 
-class IdentityMap(nn.Module):
-    def __init__(self):
-        super().__init__()
 
-    def forward(self, x, *args, **kwargs):
-        return x
+def build_mm_projector(
+    model_type_or_path: str, config: PretrainedConfig
+) -> PreTrainedModel:
+    if model_type_or_path is None:
+        return None
 
-    @property
-    def config(self):
-        return {"mm_projector_type": 'identity'}
-
-
-class SimpleResBlock(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.pre_norm = nn.LayerNorm(channels)
-
-        self.proj = nn.Sequential(
-            nn.Linear(channels, channels),
-            nn.GELU(),
-            nn.Linear(channels, channels)
+    ## load from pretrained model
+    if config.resume_path:
+        assert os.path.exists(
+            model_type_or_path
+        ), f"Resume mm projector path {model_type_or_path} does not exist!"
+        return MultimodalProjector.from_pretrained(
+            model_type_or_path, config, torch_dtype=eval(config.model_dtype)
         )
-    def forward(self, x):
-        x = self.pre_norm(x)
-        return x + self.proj(x)
-
-
-def build_vision_projector(config, delay_load=False, **kwargs):
-    projector_type = getattr(config, 'mm_projector_type', 'linear')
-
-    if projector_type == 'linear':
-        return nn.Linear(config.mm_hidden_size, config.hidden_size)
-
-    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
-    if mlp_gelu_match:
-        mlp_depth = int(mlp_gelu_match.group(1))
-        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
-        for _ in range(1, mlp_depth):
-            modules.append(nn.GELU())
-            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
-        return nn.Sequential(*modules)
-
-    if projector_type == 'identity':
-        return IdentityMap()
-
-    raise ValueError(f'Unknown projector type: {projector_type}')
+    ## build from scratch
+    else:
+        mm_projector_cfg = MultimodalProjectorConfig(model_type_or_path)
+        mm_projector = MultimodalProjector(mm_projector_cfg, config).to(
+            eval(config.model_dtype)
+        )
+        return mm_projector
diff --git a/llava/model/utils.py b/llava/model/utils.py
index e27bba94..36dc8e3d 100644
--- a/llava/model/utils.py
+++ b/llava/model/utils.py
@@ -14,11 +14,55 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 # This file is modified from https://github.com/haotian-liu/LLaVA/
-
+import os, os.path as osp
 from transformers import AutoConfig
+from transformers import  PretrainedConfig
+from huggingface_hub import snapshot_download, repo_exists
+from huggingface_hub.utils import validate_repo_id, HFValidationError
+
+def get_model_config(config):
+    default_keys = ["llm_cfg", "vision_tower_cfg", "mm_projector_cfg"]
+    
+    if hasattr(config, "_name_or_path") and len(config._name_or_path) >= 2:
+        root_path = config._name_or_path
+    else:
+        root_path = config.resume_path 
+        
+    # download from huggingface
+    if root_path is not None and not osp.exists(root_path):
+        try:
+            valid_hf_repo = repo_exists(root_path)
+        except HFValidationError as e:
+            valid_hf_repo = False
+        if valid_hf_repo:
+            root_path = snapshot_download(root_path)
+
+    return_list = []
+    for key in default_keys:
+        cfg = getattr(config, key, None)
+        if isinstance(cfg, dict):
+            try:
+                return_list.append(os.path.join(root_path, key[:-4]))
+            except:
+                raise ValueError(f"Cannot find resume path in config for {key}!")
+        elif isinstance(cfg, PretrainedConfig):
+            return_list.append(os.path.join(root_path, key[:-4]))
+        elif isinstance(cfg, str):
+            return_list.append(cfg)
+        
+    return return_list
 
 
 def is_mm_model(model_path):
+    """
+    Check if the model at the given path is a visual language model.
+
+    Args:
+        model_path (str): The path to the model.
+
+    Returns:
+        bool: True if the model is an MM model, False otherwise.
+    """
     config = AutoConfig.from_pretrained(model_path)
     architectures = config.architectures
     for architecture in architectures:
@@ -29,16 +73,20 @@ def is_mm_model(model_path):
 
 def auto_upgrade(config):
     cfg = AutoConfig.from_pretrained(config)
-    if 'llava' in config and 'llava' not in cfg.model_type:
-        assert cfg.model_type == 'llama'
-        print("You are using newer LLaVA code base, while the checkpoint of v0 is from older code base.")
-        print("You must upgrade the checkpoint to the new code base (this can be done automatically).")
+    if "llava" in config and "llava" not in cfg.model_type:
+        assert cfg.model_type == "llama"
+        print(
+            "You are using newer LLaVA code base, while the checkpoint of v0 is from older code base."
+        )
+        print(
+            "You must upgrade the checkpoint to the new code base (this can be done automatically)."
+        )
         confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]")
         if confirm.lower() in ["y", "yes"]:
             print("Upgrading checkpoint...")
             assert len(cfg.architectures) == 1
             setattr(cfg.__class__, "model_type", "llava")
-            cfg.architectures[0] = 'LlavaLlamaForCausalLM'
+            cfg.architectures[0] = "LlavaLlamaForCausalLM"
             cfg.save_pretrained(config)
             print("Checkpoint upgraded.")
         else:
diff --git a/llava/train/args.py b/llava/train/args.py
index 235e1b53..de33f5ae 100644
--- a/llava/train/args.py
+++ b/llava/train/args.py
@@ -18,32 +18,42 @@
 import transformers
 from typing import Dict, Optional, Sequence, List
 
+
 @dataclass
 class DataArguments:
-    data_path: str = field(default=None,
-                           metadata={"help": "Path to the training data."})
+    data_path: str = field(
+        default=None, metadata={"help": "Path to the training data."}
+    )
     lazy_preprocess: bool = False
     is_multimodal: bool = False
     image_folder: Optional[str] = field(default=None)
-    image_aspect_ratio: str = 'square'
-    data_mixture: str = 'coyo_25m_mmc4core'
+    image_aspect_ratio: str = "square"
+    data_mixture: str = "llava_1_5_mm_align"
+    eval_data_mixture: str = None
     vflan_no_system_prompt: bool = False
     downsample_video: bool = False
 
+    # for video training
+    num_video_frames: int = 8
+
 
 @dataclass
 class ModelArguments:
-    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
     version: Optional[str] = field(default="v0")
-    freeze_backbone: bool = field(default=False)
-    tune_mm_mlp_adapter: bool = field(default=False)
-    vision_tower: Optional[str] = field(default=None)
-    mm_vision_select_layer: Optional[int] = field(default=-1)   # default to the last layer
-    pretrain_mm_mlp_adapter: Optional[str] = field(default=None)
-    mm_projector_type: Optional[str] = field(default='linear')
+    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
+    vision_tower: Optional[str] = field(default="google/siglip-so400m-patch14-384")
+    mm_projector: Optional[str] = field(default="mlp2x_gelu")
     mm_use_im_start_end: bool = field(default=False)
     mm_use_im_patch_token: bool = field(default=True)
+    mm_vision_select_layer: Optional[int] = field(default=-1)  # default to the last layer
     mm_vision_select_feature: Optional[str] = field(default="patch")
+    vision_resolution: Optional[int] = field(default=-1)
+    interpolate_mode: Optional[str] = field(default="linear")
+    drop_path_rate: Optional[float] = field(default=0.)
+    s2: bool = field(default=False)
+    s2_scales: Optional[str] = field(default="336,672,1008")
+    s2_max_split_size: int = field(default=336)
+
 
 
 @dataclass
@@ -51,27 +61,30 @@ class TrainingArguments(transformers.TrainingArguments):
     cache_dir: Optional[str] = field(default=None)
     optim: str = field(default="adamw_torch")
     remove_unused_columns: bool = field(default=False)
-    freeze_mm_mlp_adapter: bool = field(default=False)
     mpt_attn_impl: Optional[str] = field(default="triton")
+    tune_vision_tower: bool = field(default=False)
+    tune_language_model: bool = field(default=False)
+    tune_mm_projector: bool = field(default=False)
+    model_dtype: str = field(default="torch.bfloat16")
     model_max_length: int = field(
         default=512,
         metadata={
-            "help":
-            "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
         },
     )
     double_quant: bool = field(
         default=True,
-        metadata={"help": "Compress the quantization statistics through double quantization."}
+        metadata={
+            "help": "Compress the quantization statistics through double quantization."
+        },
     )
     quant_type: str = field(
         default="nf4",
-        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
-    )
-    bits: int = field(
-        default=16,
-        metadata={"help": "How many bits to use."}
+        metadata={
+            "help": "Quantization data type to use. Should be one of `fp4` or `nf4`."
+        },
     )
+    bits: int = field(default=16, metadata={"help": "How many bits to use."})
     lora_enable: bool = False
     lora_r: int = 64
     lora_alpha: int = 16
@@ -80,3 +93,12 @@ class TrainingArguments(transformers.TrainingArguments):
     lora_bias: str = "none"
     mm_projector_lr: Optional[float] = None
     group_by_modality_length: bool = field(default=False)
+    total_time_limit: int = field(
+        default=-1, metadata={"help": "Timeout limit for this job (in minutes)."}
+    )
+    pre_terminate_time: int = field(
+        default=10,
+        metadata={
+            "help": "Time to terminate the task inadvance (minutes), saveing checkpoints needs time."
+        },
+    )
diff --git a/llava/train/callbacks/autoresume_callback.py b/llava/train/callbacks/autoresume_callback.py
new file mode 100644
index 00000000..712c7b17
--- /dev/null
+++ b/llava/train/callbacks/autoresume_callback.py
@@ -0,0 +1,61 @@
+
+""" AutoResume callback.
+
+A transformer trainer callback for interfacing with ADLR's AutoResume SDK.
+
+Copyright 2024 NVIDIA CORPORATION.
+"""
+import os
+import sys
+
+import torch
+import transformers
+from transformers.utils import logging
+
+logger = logging.get_logger("transformers")
+
+def rank_print(*s):
+    if not torch.distributed.is_initialized():
+        rank = 0
+    else:
+        rank =torch.distributed.get_rank()
+    print(rank, *s)
+
+sys.path.append(os.environ.get("SUBMIT_SCRIPTS", "."))
+try:
+    logger.info("Importing AutoResume lib...")
+    from userlib.auto_resume import AutoResume
+
+    AutoResume.init()
+    logger.info("Found AutoResume SDK!")
+except:
+    logger.warn("Did not find AutoResume SDK!")
+    AutoResume = None
+
+
+class AutoResumeCallback(transformers.TrainerCallback):
+    """
+    A [`TrainerCallback`] that handles autoresume.
+
+    Args:
+        interval: interval (in number of iterations) between checks as to
+            whether to suspend.
+    """
+
+    def __init__(self, interval: int = 50):
+        self.interval = interval
+
+    def on_step_end(self, args, state, control, **kwargs):
+        if state.global_step % self.interval == 0:
+            rank_print("AutoResumeHook: Checking whether to suspend...")
+
+            # Check whether to suspend the job.
+            should_preempt = AutoResume is not None and AutoResume.termination_requested()
+
+            if should_preempt:
+                if state.is_local_process_zero:
+                    logger.warn(f"AutoResumeHook: Request resume...")
+                    if AutoResume is not None:
+                        AutoResume.request_resume()
+                control.should_training_stop = True
+                control.should_save = True
diff --git a/llava/train/llava_trainer.py b/llava/train/llava_trainer.py
index c5d4ee33..54a793e5 100644
--- a/llava/train/llava_trainer.py
+++ b/llava/train/llava_trainer.py
@@ -17,29 +17,26 @@
 
 
 import os
-import torch
-
-from torch.utils.data import ConcatDataset, DistributedSampler, RandomSampler, Sampler
-
-from transformers import Trainer
-from transformers.trainer import (
-    is_sagemaker_mp_enabled,
-    get_parameter_names,
-    has_length,
-    ALL_LAYERNORM_LAYERS,
-    # ShardedDDPOption,
-    logger,
-)
 from typing import List, Optional
 
+import torch
+from torch.utils.data import (ConcatDataset, Dataset, DistributedSampler,
+                              RandomSampler, Sampler)
+from transformers import PreTrainedModel, Trainer
+from transformers.modeling_utils import unwrap_model
+from transformers.trainer import ALL_LAYERNORM_LAYERS  # ShardedDDPOption,
+from transformers.trainer import (get_parameter_names, has_length,
+                                  is_sagemaker_mp_enabled, logger)
+from collections import OrderedDict
 
 def maybe_zero_3(param, ignore_status=False, name=None):
     from deepspeed import zero
     from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+
     if hasattr(param, "ds_id"):
         if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
             if not ignore_status:
-                print(name, 'no ignore status')
+                print(name, "no ignore status")
         with zero.GatheredParameters([param]):
             param = param.data.detach().cpu().clone()
     else:
@@ -85,7 +82,9 @@ def get_modality_length_grouped_indices(lengths, batch_size, world_size, generat
     lang_indices, lang_lengths = zip(*[(i, -l) for i, l in enumerate(lengths) if l < 0])
 
     mm_shuffle = [mm_indices[i] for i in get_length_grouped_indices(mm_lengths, batch_size, world_size, generator=None)]
-    lang_shuffle = [lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)]
+    lang_shuffle = [
+        lang_indices[i] for i in get_length_grouped_indices(lang_lengths, batch_size, world_size, generator=None)
+    ]
     megabatch_size = world_size * batch_size
     mm_megabatches = [mm_shuffle[i : i + megabatch_size] for i in range(0, len(mm_shuffle), megabatch_size)]
     lang_megabatches = [lang_shuffle[i : i + megabatch_size] for i in range(0, len(lang_shuffle), megabatch_size)]
@@ -116,37 +115,42 @@ def get_length_grouped_indices(lengths, batch_size, world_size, generator=None,
 
 class VILADistributedSampler(DistributedSampler):
     """This class is implemented by Jason Lu."""
-    def __init__(self, dataset, num_replicas: Optional[int] = None,
-                 rank: Optional[int] = None, shuffle: bool = True,
-                 seed: int = 0, drop_last: bool = False,
-                 batch_size=None,
-                 # NOTE: this is the total size but not per-worker
-                 sample_len_list = None,
-                 force_accumulation=True,
-                 ) -> None:
+
+    def __init__(
+        self,
+        dataset,
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        shuffle: bool = True,
+        seed: int = 0,
+        drop_last: bool = False,
+        batch_size=None,
+        # NOTE: this is the total size but not per-worker
+        sample_len_list=None,
+        force_accumulation=True,
+    ) -> None:
         import math
+
         import torch.distributed as dist
 
         if num_replicas is None:
             if not dist.is_available():
-                raise RuntimeError(
-                    "Requires distributed package to be available")
+                raise RuntimeError("Requires distributed package to be available")
             num_replicas = dist.get_world_size()
         if rank is None:
             if not dist.is_available():
-                raise RuntimeError(
-                    "Requires distributed package to be available")
+                raise RuntimeError("Requires distributed package to be available")
             rank = dist.get_rank()
         if rank >= num_replicas or rank < 0:
             raise ValueError(
-                "Invalid rank {}, rank should be in the interval"
-                " [0, {}]".format(rank, num_replicas - 1))
+                "Invalid rank {}, rank should be in the interval" " [0, {}]".format(rank, num_replicas - 1)
+            )
         self.dataset = dataset
         self.num_replicas = num_replicas
         self.rank = rank
         self.epoch = 0
         self.drop_last = True  # always True
-        
+
         # NOTE: org_ is without drop last
         self.org_sample_len_list = self.per_replica_samples = sample_len_list
         assert sum(sample_len_list) == len(self.dataset)
@@ -156,16 +160,15 @@ def __init__(self, dataset, num_replicas: Optional[int] = None,
 
         if self.drop_last:  # type: ignore[arg-type]
             self.per_replica_samples = [
-                sample_len // (self.num_replicas * batch_size) * batch_size
-                for sample_len in self.per_replica_samples
+                sample_len // (self.num_replicas * batch_size) * batch_size for sample_len in self.per_replica_samples
             ]
             self.num_samples = sum(self.per_replica_samples)
         else:
             raise NotImplementedError
-    
+
         self.total_size = self.num_samples * self.num_replicas
         self.total_samples = [samples * self.num_replicas for samples in self.per_replica_samples]
-        
+
         self.shuffle = shuffle
         self.seed = seed
 
@@ -174,34 +177,38 @@ def __init__(self, dataset, num_replicas: Optional[int] = None,
 
     def __iter__(self):
         import random
+
         indices = list(range(len(self.dataset)))
 
         indices_list = []
         for i in range(len(self.org_sample_len_list)):
-            indices_list.append(indices[
-                sum(self.org_sample_len_list[:i]): sum(self.org_sample_len_list[:i]) + self.total_samples[i]
-                ])
-            
+            indices_list.append(
+                indices[sum(self.org_sample_len_list[:i]) : sum(self.org_sample_len_list[:i]) + self.total_samples[i]]
+            )
+
         # 1. split the full indices first (note: without drop last at this moment)
         indices_list = []
         for i in range(len(self.org_sample_len_list)):
-            indices_list.append(indices[
-                sum(self.org_sample_len_list[:i]): sum(self.org_sample_len_list[:i]) + self.total_samples[i]
-                ])
+            indices_list.append(
+                indices[sum(self.org_sample_len_list[:i]) : sum(self.org_sample_len_list[:i]) + self.total_samples[i]]
+            )
 
-        assert sum([len(indices) for indices in indices_list]) == self.total_size, \
-            (sum([len(indices) for indices in indices_list]), self.total_size)
+        assert sum([len(indices) for indices in indices_list]) == self.total_size, (
+            sum([len(indices) for indices in indices_list]),
+            self.total_size,
+        )
 
         # let's first do subsample
         for idx, indices in enumerate(indices_list):
-            indices_list[idx] = indices[self.rank *
-                            self.per_replica_samples[idx]:(self.rank + 1) * self.per_replica_samples[idx]]
+            indices_list[idx] = indices[
+                self.rank * self.per_replica_samples[idx] : (self.rank + 1) * self.per_replica_samples[idx]
+            ]
 
         random.seed(self.seed + self.epoch)
         for indice in range(len(indices_list)):
             random.shuffle(indices_list[indice])
 
-        indices_list = sorted(indices_list, key=lambda x:-len(x))
+        indices_list = sorted(indices_list, key=lambda x: -len(x))
         all_indices = [-1] * self.num_samples
         indices_available = list(range(self.num_samples))
         for indice in indices_list:
@@ -214,7 +221,7 @@ def __iter__(self):
             for i, idx in enumerate(mapped_indices):
                 all_indices[idx] = indice[i]
         assert -1 not in all_indices
-        
+
         return iter(all_indices)
 
 
@@ -246,18 +253,21 @@ def __len__(self):
 
     def __iter__(self):
         if self.group_by_modality:
-            indices = get_modality_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
+            indices = get_modality_length_grouped_indices(
+                self.lengths, self.batch_size, self.world_size, generator=self.generator
+            )
         else:
-            indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size, generator=self.generator)
+            indices = get_length_grouped_indices(
+                self.lengths, self.batch_size, self.world_size, generator=self.generator
+            )
         return iter(indices)
 
 
 class LLaVATrainer(Trainer):
-
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if self.train_dataset is None or not has_length(self.train_dataset):
             return None
-        
+
         # Always using Jason's sampler.
         sample_len_list = self.args.sample_lens
         seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed
@@ -265,7 +275,8 @@ def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
             self.train_dataset,
             num_replicas=self.args.world_size,
             rank=self.args.process_index,
-            seed=seed, batch_size=self.args.train_batch_size,
+            seed=seed,
+            batch_size=self.args.train_batch_size,
             sample_len_list=sample_len_list,
         )
 
@@ -285,6 +296,22 @@ def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         else:
             return super()._get_train_sampler()
 
+    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.Sampler]:
+        if self.eval_dataset is None or not has_length(self.eval_dataset):
+            return None
+
+        # Always using Jason's sampler.
+        sample_len_list = self.args.eval_sample_lens
+        seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed
+        return VILADistributedSampler(
+            eval_dataset,
+            num_replicas=self.args.world_size,
+            rank=self.args.process_index,
+            seed=seed,
+            batch_size=self.args.eval_batch_size,
+            sample_len_list=sample_len_list,
+        )
+
     def create_optimizer(self):
         """
         Setup the optimizer.
@@ -307,26 +334,34 @@ def create_optimizer(self):
                 optimizer_grouped_parameters = [
                     {
                         "params": [
-                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
+                            p
+                            for n, p in opt_model.named_parameters()
+                            if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
                         ],
                         "weight_decay": self.args.weight_decay,
                     },
                     {
                         "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
+                            p
+                            for n, p in opt_model.named_parameters()
+                            if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
                         ],
                         "weight_decay": 0.0,
                     },
                     {
                         "params": [
-                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad)
+                            p
+                            for n, p in opt_model.named_parameters()
+                            if (n in decay_parameters and n in projector_parameters and p.requires_grad)
                         ],
                         "weight_decay": self.args.weight_decay,
                         "lr": self.args.mm_projector_lr,
                     },
                     {
                         "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
+                            p
+                            for n, p in opt_model.named_parameters()
+                            if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
                         ],
                         "weight_decay": 0.0,
                         "lr": self.args.mm_projector_lr,
@@ -342,7 +377,9 @@ def create_optimizer(self):
                     },
                     {
                         "params": [
-                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
+                            p
+                            for n, p in opt_model.named_parameters()
+                            if (n not in decay_parameters and p.requires_grad)
                         ],
                         "weight_decay": 0.0,
                     },
@@ -350,7 +387,7 @@ def create_optimizer(self):
 
             optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)
 
-            if 0: # self.sharded_ddp == ShardedDDPOption.SIMPLE:
+            if 0:  # self.sharded_ddp == ShardedDDPOption.SIMPLE:
                 self.optimizer = OSS(
                     params=optimizer_grouped_parameters,
                     optim=optimizer_cls,
@@ -374,29 +411,14 @@ def create_optimizer(self):
 
         return self.optimizer
 
-    def _save_checkpoint(self, model, trial, metrics=None):
-        if getattr(self.args, 'tune_mm_mlp_adapter', False):
-            from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
-            checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"
 
-            run_dir = self._get_output_dir(trial=trial)
-            output_dir = os.path.join(run_dir, checkpoint_folder)
-
-            # Only save Adapter
-            keys_to_match = ['mm_projector', 'vision_resampler']
-            if getattr(self.args, "use_im_start_end", False):
-                keys_to_match.extend(['embed_tokens', 'embed_in'])
-
-            weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)
-
-            if self.args.local_rank == 0 or self.args.local_rank == -1:
-                self.model.config.save_pretrained(output_dir)
-                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
+    def save_model(self, output_dir: Optional[str], _internal_call: bool):
+        ## save tuned model separately
+        if self.is_deepspeed_enabled:
+            state_dict = self.accelerator.get_state_dict(self.deepspeed)
         else:
-            super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)
+            # TODO(ligeng): fix save_model for multi-node training on large models (e.g., Llama-70b)
+            state_dict = self.model.state_dict()
 
-    def _save(self, output_dir: Optional[str] = None, state_dict=None):
-        if getattr(self.args, 'tune_mm_mlp_adapter', False):
-            pass
-        else:
-            super(LLaVATrainer, self)._save(output_dir, state_dict)
+        if self.args.should_save:
+            return self.model.save_pretrained(output_dir, state_dict=state_dict)
\ No newline at end of file
diff --git a/llava/train/short_video_filter.py b/llava/train/short_video_filter.py
new file mode 100644
index 00000000..5f182771
--- /dev/null
+++ b/llava/train/short_video_filter.py
@@ -0,0 +1,25 @@
+import os
+import json
+
+
+video_json_path = '/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/activitynet-qa/train-processed-filtered.json'
+video_output_json_path = '/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/activitynet-qa/train-processed-filtered-v2.json'
+video_dir = '/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets/Video_ChatGPT/activitynet_videos'
+
+video_json = json.load(open(video_json_path, "r"))
+
+output_list = []
+processed_files = 0
+for video in video_json:
+    print(f"Processing {processed_files} files")
+    processed_files += 1
+    if 'video' in video.keys():
+        path = os.path.join(video_dir, video['video'])
+    else:
+        path = os.path.join(video_dir, video['id'] + '.mp4') 
+    if os.path.isfile(path) and os.path.getsize(path) > 100 * 1024:
+        output_list.append(video)
+print(f"Num original videos: {len(video_json)}")
+print(f"Num new videos: {len(output_list)}")
+
+json.dump(output_list, open(video_output_json_path, "w"))
diff --git a/llava/train/slurm_utils.py b/llava/train/slurm_utils.py
new file mode 100644
index 00000000..4da7dc1e
--- /dev/null
+++ b/llava/train/slurm_utils.py
@@ -0,0 +1,78 @@
+import datetime
+import logging
+import logging.handlers
+import os
+import sys
+import requests
+import time
+import transformers
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, get_last_checkpoint
+
+from transformers.trainer_callback import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+import torch
+import warnings
+
+def get_rank():
+    if not torch.distributed.is_initialized():
+        return 0
+    return torch.distributed.get_rank()
+
+
+def get_local_rank():
+    if not torch.distributed.is_initialized():
+        return 0
+    num_gpus = torch.cuda.device_count()
+    return get_rank() % num_gpus
+
+
+def get_world_size():
+    if not torch.distributed.is_initialized():
+        return 1
+    return torch.distributed.get_world_size()
+
+
+class Timer:
+    def __init__(self):
+        self.start_time = None
+        self.elapsed_time = 0
+
+    def start(self):
+        self.start_time = time.time()
+
+    def reset(self):
+        self.start_time = None
+        self.elapsed_time = 0
+
+    def get_elapsed_time(self):
+        if self.start_time is not None:
+            return self.elapsed_time + (time.time() - self.start_time)
+        
+class TimeoutTerminateCallback(transformers.TrainerCallback):
+    def __init__(self, args, total_time_limit=240, pre_terminate_time=10):
+        self.training_args = args
+        self.total_time_limit = total_time_limit
+        self.pre_terminate_time = pre_terminate_time
+        self.timer = Timer()
+        self.timer.start()
+
+        if args.local_rank == 0:
+            print(f"Timer for terminate callback has been set.\nTotal limit: {total_time_limit}min\nPre terminate time: {pre_terminate_time}min")
+
+        self.time_to_kill = (total_time_limit - pre_terminate_time) * 60
+
+
+    def on_step_end(self, args, state, control, model, **kwargs):
+        elapsed_time = self.timer.get_elapsed_time()
+        
+        if elapsed_time > self.time_to_kill:
+            if args.local_rank == 0:
+                print("Timeout, start to save checkpoint....")
+            control.should_save = True
+            control.should_training_stop = True
+
+        return control
\ No newline at end of file
diff --git a/llava/train/train.py b/llava/train/train.py
index 11f805da..34443dd0 100644
--- a/llava/train/train.py
+++ b/llava/train/train.py
@@ -13,51 +13,53 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
-# This file is modified from https://github.com/haotian-liu/LLaVA/
 
 import os
-import copy
-from dataclasses import dataclass, field
-import json
 import logging
-import pathlib
 from typing import Dict, Optional, Sequence, List
+import warnings
 
 import torch
 import transformers
 
-from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
+from transformers import HfArgumentParser, AutoTokenizer, AutoConfig, LlamaForCausalLM
+from transformers.modeling_utils import unwrap_model
+from transformers import set_seed
+
 from torch.utils.data import Dataset
 from llava.train.llava_trainer import LLaVATrainer
 from llava.train.args import TrainingArguments, ModelArguments, DataArguments
+from llava.train.callbacks.autoresume_callback import AutoResumeCallback
 
 from llava import conversation as conversation_lib
-from llava.data import make_supervised_data_module, DataCollatorForSupervisedDataset
+from llava.data import make_supervised_data_module
 from llava.model import *
-from llava.mm_utils import tokenizer_image_token
-
-import math
-from peft import PeftModel
-from PIL import Image
+from llava.train.utils import (
+    get_checkpoint_path,
+    prepare_config_for_training,
+    vision_resolution_elevation,
+    unit_test_rope_scaling,
+    mprint,
+)
 
 
 local_rank = None
-os.environ["WANDB_PROJECT"] = "VILA"
-
-
-def rank0_print(*args):
-    if local_rank == 0:
-        print(*args)
 
+if "WANDB_PROJECT" not in os.environ:
+    # Default to WANDB project "VILA".
+    os.environ["WANDB_PROJECT"] = "VILA"
 
 
 def maybe_zero_3(param, ignore_status=False, name=None):
     from deepspeed import zero
     from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
+
     if hasattr(param, "ds_id"):
         if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
             if not ignore_status:
-                logging.warning(f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}")
+                logging.warning(
+                    f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}"
+                )
         with zero.GatheredParameters([param]):
             param = param.data.detach().cpu().clone()
     else:
@@ -95,67 +97,50 @@ def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True):
     to_return = {k: t for k, t in named_params if "lora_" not in k}
     if require_grad_only:
         to_return = {k: t for k, t in to_return.items() if t.requires_grad}
-    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
+    to_return = {
+        k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()
+    }
     return to_return
 
 
 def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
-    to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
-    to_return = {k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()}
+    to_return = {
+        k: t
+        for k, t in named_params
+        if any(key_match in k for key_match in keys_to_match)
+    }
+    to_return = {
+        k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items()
+    }
     return to_return
 
 
 def find_all_linear_names(model):
     cls = torch.nn.Linear
     lora_module_names = set()
-    multimodal_keywords = ['mm_projector', 'vision_tower', 'vision_resampler']
+    multimodal_keywords = ["mm_projector", "vision_tower", "vision_resampler"]
     for name, module in model.named_modules():
         if any(mm_keyword in name for mm_keyword in multimodal_keywords):
             continue
         if isinstance(module, cls):
-            names = name.split('.')
+            names = name.split(".")
             lora_module_names.add(names[0] if len(names) == 1 else names[-1])
 
-    if 'lm_head' in lora_module_names: # needed for 16-bit
-        lora_module_names.remove('lm_head')
+    if "lm_head" in lora_module_names:  # needed for 16-bit
+        lora_module_names.remove("lm_head")
     return list(lora_module_names)
 
 
-def safe_save_model_for_hf_trainer(trainer: transformers.Trainer,
-                                   output_dir: str):
+def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
     """Collects the state dict and dump to disk."""
-
-    if getattr(trainer.args, "tune_mm_mlp_adapter", False):
-        # Only save Adapter
-        keys_to_match = ['mm_projector']
-        if getattr(trainer.args, "use_im_start_end", False):
-            keys_to_match.extend(['embed_tokens', 'embed_in'])
-
-        weight_to_save = get_mm_adapter_state_maybe_zero_3(trainer.model.named_parameters(), keys_to_match)
-        trainer.model.config.save_pretrained(output_dir)
-
-        current_folder = output_dir.split('/')[-1]
-        parent_folder = os.path.dirname(output_dir)
-        if trainer.args.local_rank == 0 or trainer.args.local_rank == -1:
-            if current_folder.startswith('checkpoint-'):
-                mm_projector_folder = os.path.join(parent_folder, "mm_projector")
-                os.makedirs(mm_projector_folder, exist_ok=True)
-                torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
-            else:
-                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
-        return
-
     if trainer.deepspeed:
         torch.cuda.synchronize()
-        trainer.save_model(output_dir)
+        trainer.save_model(output_dir, _internal_call=True)
         return
 
     state_dict = trainer.model.state_dict()
     if trainer.args.should_save:
-        cpu_state_dict = {
-            key: value.cpu()
-            for key, value in state_dict.items()
-        }
+        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
         del state_dict
         trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
 
@@ -177,9 +162,11 @@ def smart_tokenizer_and_embedding_resize(
         output_embeddings = model.get_output_embeddings().weight.data
 
         input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
-            dim=0, keepdim=True)
+            dim=0, keepdim=True
+        )
         output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
-            dim=0, keepdim=True)
+            dim=0, keepdim=True
+        )
 
         input_embeddings[-num_new_tokens:] = input_embeddings_avg
         output_embeddings[-num_new_tokens:] = output_embeddings_avg
@@ -188,109 +175,158 @@ def smart_tokenizer_and_embedding_resize(
 def train():
     global local_rank
 
-    parser = transformers.HfArgumentParser(
-        (ModelArguments, DataArguments, TrainingArguments))
+    parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
     model_args, data_args, training_args = parser.parse_args_into_dataclasses()
     training_args.run_name = training_args.output_dir.split("/")[-1]
     local_rank = training_args.local_rank
-    compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
+    compute_dtype = (
+        torch.float16
+        if training_args.fp16
+        else (torch.bfloat16 if training_args.bf16 else torch.float32)
+    )
 
     bnb_model_from_pretrained_args = {}
     if training_args.bits in [4, 8]:
         from transformers import BitsAndBytesConfig
-        bnb_model_from_pretrained_args.update(dict(
-            device_map={"": training_args.device},
-            load_in_4bit=training_args.bits == 4,
-            load_in_8bit=training_args.bits == 8,
-            quantization_config=BitsAndBytesConfig(
+
+        bnb_model_from_pretrained_args.update(
+            dict(
+                device_map={"": training_args.device},
                 load_in_4bit=training_args.bits == 4,
                 load_in_8bit=training_args.bits == 8,
-                llm_int8_skip_modules=["mm_projector"],
-                llm_int8_threshold=6.0,
-                llm_int8_has_fp16_weight=False,
-                bnb_4bit_compute_dtype=compute_dtype,
-                bnb_4bit_use_double_quant=training_args.double_quant,
-                bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
+                quantization_config=BitsAndBytesConfig(
+                    load_in_4bit=training_args.bits == 4,
+                    load_in_8bit=training_args.bits == 8,
+                    llm_int8_skip_modules=["mm_projector"],
+                    llm_int8_threshold=6.0,
+                    llm_int8_has_fp16_weight=False,
+                    bnb_4bit_compute_dtype=compute_dtype,
+                    bnb_4bit_use_double_quant=training_args.double_quant,
+                    bnb_4bit_quant_type=training_args.quant_type,  # {'fp4', 'nf4'}
+                ),
             )
-        ))
-    
-    def context_length_extension(config):
-        orig_ctx_len = getattr(config, "max_position_embeddings", None)
-        if orig_ctx_len and training_args.model_max_length > orig_ctx_len:
-            scaling_factor = float(math.ceil(training_args.model_max_length / orig_ctx_len))
-            config.rope_scaling = {"type": "linear", "factor": scaling_factor}
-
-    if model_args.vision_tower is not None:
-        if 'mpt' in model_args.model_name_or_path:
-            config = transformers.AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
-            context_length_extension(config)
-            config.attn_config['attn_impl'] = training_args.mpt_attn_impl
-            model = LlavaMPTForCausalLM.from_pretrained(
-                model_args.model_name_or_path,
-                config=config,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
+        )
+
+    set_seed(training_args.seed)
+
+    resume_path, continue_training = get_checkpoint_path(training_args.output_dir)
+
+    if not continue_training:
+        print(f"Models has been ready under {training_args.output_dir}. Skipp training")
+        exit(0)
+
+    if resume_path:
+        resume_from_checkpoint = True
+        config = AutoConfig.from_pretrained(resume_path, trust_remote_code=True)
+        config.resume_path = resume_path
+        model_cls = eval(config.architectures[0])
+    else:
+        ## first time training
+        resume_from_checkpoint = False
+        if "mpt" in model_args.model_name_or_path:
+            config = AutoConfig.from_pretrained(
+                model_args.model_name_or_path, trust_remote_code=True
             )
-        elif 'mistral' in model_args.model_name_or_path.lower():
+            config.attn_config["attn_impl"] = training_args.mpt_attn_impl
+            model_cls = LlavaMPTForCausalLM
+        elif "mistral" in model_args.model_name_or_path.lower():
             config = LlavaMistralConfig.from_pretrained(model_args.model_name_or_path)
             config._attn_implementation = "flash_attention_2"
-            context_length_extension(config)
-            torch.set_default_dtype(torch.bfloat16)
-            model = LlavaMistralForCausalLM.from_pretrained(
-                model_args.model_name_or_path,
-                config=config,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
-            )
-        elif 'mixtral' in model_args.model_name_or_path.lower():
+            model_cls = LlavaMistralForCausalLM
+        elif "mixtral" in model_args.model_name_or_path.lower():
             config = LlavaMixtralConfig.from_pretrained(model_args.model_name_or_path)
             config._attn_implementation = "flash_attention_2"
-            context_length_extension(config)
-            torch.set_default_dtype(torch.bfloat16)
-            model = LlavaMixtralForCausalLM.from_pretrained(
-                model_args.model_name_or_path,
-                config=config,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
-            )
-        else:
-            config = LlavaConfig.from_pretrained(model_args.model_name_or_path)
+            model_cls = LlavaMixtralForCausalLM
+        elif "gemma" in model_args.model_name_or_path.lower():
+            config = LlavaGemmaConfig.from_pretrained(model_args.model_name_or_path)
             config._attn_implementation = "flash_attention_2"
-            context_length_extension(config)
-            torch.set_default_dtype(torch.bfloat16)
-            model = LlavaLlamaForCausalLM.from_pretrained(
+            model_cls = LlavaGemmaForCausalLM
+        else:
+            ## llm and default multimodal model
+            model_cls = LlavaLlamaModel
+            config = LlavaLlamaConfig.from_pretrained(
                 model_args.model_name_or_path,
-                config=config,
-                cache_dir=training_args.cache_dir,
-                **bnb_model_from_pretrained_args
+                resume=resume_from_checkpoint
             )
-    else:
-        model = transformers.LlamaForCausalLM.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            **bnb_model_from_pretrained_args
-        )
-        context_length_extension(config)
-    print(model)
-    model.config.use_cache = False
-    if model_args.freeze_backbone:
-        model.model.requires_grad_(False)
+        if getattr(config, "resume_path", None) is not None:
+            config.resume_path = model_args.model_name_or_path
+    
+    ## extra configurations
+    prepare_config_for_training(config, model_args, training_args, data_args)
+
+    model = model_cls(
+        config=config,
+        attn_implementation="flash_attention_2",
+        model_max_length=training_args.model_max_length,
+        cache_dir=training_args.cache_dir,
+        **bnb_model_from_pretrained_args,
+    )
+
+    vision_resolution_elevation(model, config)
+    # This is an empty func.
+    # It would be overwritten by unit test script.
+    if unit_test_rope_scaling(model, model.llm.config, training_args):
+        return
 
+    # Take a look on model architecture.
+    mprint(model)
+
+    model.llm.config.use_cache = False
+    ## set tunnable parameters
+    logging.warning(
+        "You are setting tunable parameters for the model. Previous args include 'freeze_backbone' and 'tune_mm_mlp_adapter' are deprecated.\n Notice: default value of tune_xxx is False, which means you would not tune this part."
+    )
+    model.get_llm().requires_grad_(training_args.tune_language_model)
+    mprint(f"Tunable parameters:\nlanguage model {training_args.tune_language_model}")
+    if model.get_vision_tower():
+        model.get_vision_tower().requires_grad_(training_args.tune_vision_tower)
+        model.get_mm_projector().requires_grad_(training_args.tune_mm_projector)
+        mprint(f"vision tower {training_args.tune_vision_tower}")
+        mprint(f"mm projector {training_args.tune_mm_projector}")
+    if not any([training_args.tune_language_model, training_args.tune_vision_tower, training_args.tune_mm_projector]):
+        logging.warning(
+            "You are not tuning any part of the model. Please check if this is intended."
+        )
+    def need_to_modify_do_sample(generation_config):
+        if generation_config.do_sample is False:
+            if (
+                generation_config.temperature is not None
+                and generation_config.temperature != 1.0
+            ):
+                return True
+            if generation_config.top_p is not None and generation_config.top_p != 1.0:
+                return True
+        return False
+
+    if need_to_modify_do_sample(model.llm.generation_config):
+        model.llm.generation_config.do_sample = True
+
+    ## quantize training @yunhao: be careful here
     if training_args.bits in [4, 8]:
         from peft import prepare_model_for_kbit_training
-        model.config.torch_dtype=(torch.float32 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
-        model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
+
+        model.llm.config.torch_dtype = (
+            torch.float32
+            if training_args.fp16
+            else (torch.bfloat16 if training_args.bf16 else torch.float32)
+        )
+        model.llm = prepare_model_for_kbit_training(
+            model.llm, use_gradient_checkpointing=training_args.gradient_checkpointing
+        )
 
     if training_args.gradient_checkpointing:
-        if hasattr(model, "enable_input_require_grads"):
-            model.enable_input_require_grads()
+        if hasattr(model.llm, "enable_input_require_grads"):
+            model.llm.enable_input_require_grads()
         else:
+
             def make_inputs_require_grad(module, input, output):
                 output.requires_grad_(True)
+
             model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
 
     if training_args.lora_enable:
         from peft import LoraConfig, get_peft_model
+
         lora_config = LoraConfig(
             r=training_args.lora_r,
             lora_alpha=training_args.lora_alpha,
@@ -304,114 +340,105 @@ def make_inputs_require_grad(module, input, output):
                 model.to(torch.bfloat16)
             if training_args.fp16:
                 model.to(torch.float16)
-        rank0_print("Adding LoRA adapters...")
+        mprint("Adding LoRA adapters...")
         model = get_peft_model(model, lora_config)
-
-    if 'mpt' in model_args.model_name_or_path:
-        tokenizer = transformers.AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            model_max_length=training_args.model_max_length,
-            padding_side="right",
-        )
-    else:
-        tokenizer = transformers.AutoTokenizer.from_pretrained(
-            model_args.model_name_or_path,
-            cache_dir=training_args.cache_dir,
-            model_max_length=training_args.model_max_length,
-            padding_side="right",
-            use_fast=False,
-            legacy=False,
-        )
-
+    # @yunhao: tokenizer instantiation is moved into build_llm
+    tokenizer = model.tokenizer
+    # @yunhao: may move this block into method "build_llm"
     if model_args.version == "v0":
         if tokenizer.pad_token is None:
             smart_tokenizer_and_embedding_resize(
                 special_tokens_dict=dict(pad_token="[PAD]"),
                 tokenizer=tokenizer,
-                model=model,
+                model=model.llm,
             )
     elif model_args.version == "v0.5":
         tokenizer.pad_token = tokenizer.unk_token
     else:
         tokenizer.pad_token = tokenizer.unk_token
+        if tokenizer.pad_token is None:
+            smart_tokenizer_and_embedding_resize(
+                special_tokens_dict=dict(pad_token="[PAD]"),
+                tokenizer=tokenizer,
+                model=model.llm,
+            )
         if model_args.version in conversation_lib.conv_templates:
-            conversation_lib.default_conversation = conversation_lib.conv_templates[model_args.version]
+            conversation_lib.default_conversation = conversation_lib.conv_templates[
+                model_args.version
+            ]
         else:
-            conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
+            conversation_lib.default_conversation = conversation_lib.conv_templates[
+                "vicuna_v1"
+            ]
 
     # kentang-mit@: It will be useful in on-the-fly packing
-    model.pad_token_id = tokenizer.pad_token_id
+    model.llm.pad_token_id = tokenizer.pad_token_id
+    model.llm.config.tokenizer_padding_side = tokenizer.padding_side
+    model.llm.config.tokenizer_model_max_length = tokenizer.model_max_length
     if training_args.lora_enable:
-        model.base_model.model.pad_token_id = tokenizer.pad_token_id
-
-    if model_args.vision_tower is not None:
-        model.get_model().initialize_vision_modules(
-            model_args=model_args,
-            fsdp=training_args.fsdp
-        )
-        
-        vision_tower = model.get_vision_tower()
-        vision_tower.to(dtype=torch.bfloat16 if training_args.bf16 else torch.float16, device=training_args.device)
+        model.base_model.model.llm.pad_token_id = tokenizer.pad_token_id
 
+    vision_tower = model.get_vision_tower()
+    if vision_tower is not None:
         data_args.image_processor = vision_tower.image_processor
         data_args.is_multimodal = True
 
+        model.config.num_video_frames = data_args.num_video_frames
         model.config.image_aspect_ratio = data_args.image_aspect_ratio
-        model.config.tokenizer_padding_side = tokenizer.padding_side
-        model.config.tokenizer_model_max_length = tokenizer.model_max_length
-
-        model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
-        if model_args.tune_mm_mlp_adapter:
-            model.requires_grad_(False)
-            for p in model.get_model().mm_projector.parameters():
-                p.requires_grad = True
-
-        model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter
-        if training_args.freeze_mm_mlp_adapter:
-            for p in model.get_model().mm_projector.parameters():
-                p.requires_grad = False
-
-        if training_args.bits in [4, 8]:
-            model.get_model().mm_projector.to(dtype=compute_dtype, device=training_args.device)
-
-        model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = model_args.mm_use_im_start_end
+        model.config.mm_use_im_start_end = data_args.mm_use_im_start_end = (
+            model_args.mm_use_im_start_end
+        )
         model.config.mm_projector_lr = training_args.mm_projector_lr
         training_args.use_im_start_end = model_args.mm_use_im_start_end
         model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token
         model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer)
-
+    ## TODO pay attention to quantize
     if training_args.bits in [4, 8]:
         from peft.tuners.lora import LoraLayer
+
         for name, module in model.named_modules():
             if isinstance(module, LoraLayer):
                 if training_args.bf16:
                     module = module.to(torch.bfloat16)
-            if 'norm' in name:
+            if "norm" in name:
                 module = module.to(torch.float32)
-            if 'lm_head' in name or 'embed_tokens' in name:
-                if hasattr(module, 'weight'):
+            if "lm_head" in name or "embed_tokens" in name:
+                if hasattr(module, "weight"):
                     if training_args.bf16 and module.weight.dtype == torch.float32:
                         module = module.to(torch.bfloat16)
 
-    data_module = make_supervised_data_module(tokenizer=tokenizer,
-                                              data_args=data_args,
-                                              training_args=training_args,)
-    trainer = LLaVATrainer(model=model,
-                    tokenizer=tokenizer,
-                    args=training_args,
-                    **data_module)
-    print("length of dataloader:", len(trainer.get_train_dataloader()), len(trainer.train_dataset))
-    print("before trainer", torch.cuda.memory_allocated() / 1024 / 1024 / 1024)
-
-    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
-        trainer.train(resume_from_checkpoint=True)
-    else:
-        trainer.train()
+    data_module = make_supervised_data_module(
+        tokenizer=tokenizer,
+        data_args=data_args,
+        training_args=training_args,
+    )
+
+    # Add a training step_end callback to check whether to autosuspend.
+    callbacks = [AutoResumeCallback()]
+
+    trainer = LLaVATrainer(
+        model=model, tokenizer=tokenizer, args=training_args,
+        callbacks=callbacks, **data_module
+    )
+    print(
+        "length of dataloader:",
+        len(trainer.get_train_dataloader()),
+        len(trainer.train_dataset),
+        flush=True,
+    )
+    print(
+        "[GPU memory] before trainer",
+        torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
+        flush=True,
+    )
+
+    # print(resume_from_checkpoint); input("DEBUG")
+    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
     trainer.save_state()
 
-    model.config.use_cache = True
-
+    model.llm.config.use_cache = True
+    model.config.resume_path = model.config._name_or_path = training_args.output_dir
+    ## TODO handle lora for new initialization
     if training_args.lora_enable:
         state_dict = get_peft_state_maybe_zero_3(
             model.named_parameters(), training_args.lora_bias
@@ -422,10 +449,14 @@ def make_inputs_require_grad(module, input, output):
         if training_args.local_rank == 0 or training_args.local_rank == -1:
             model.config.save_pretrained(training_args.output_dir)
             model.save_pretrained(training_args.output_dir, state_dict=state_dict)
-            torch.save(non_lora_state_dict, os.path.join(training_args.output_dir, 'non_lora_trainables.bin'))
+            torch.save(
+                non_lora_state_dict,
+                os.path.join(training_args.output_dir, "non_lora_trainables.bin"),
+            )
     else:
-        safe_save_model_for_hf_trainer(trainer=trainer,
-                                       output_dir=training_args.output_dir)
+        safe_save_model_for_hf_trainer(
+            trainer=trainer, output_dir=training_args.output_dir
+        )
 
 
 if __name__ == "__main__":
diff --git a/llava/train/transformers_replace/models/gemma/__init__.py b/llava/train/transformers_replace/models/gemma/__init__.py
new file mode 100644
index 00000000..64ff3445
--- /dev/null
+++ b/llava/train/transformers_replace/models/gemma/__init__.py
@@ -0,0 +1,121 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_sentencepiece_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_gemma": ["GEMMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "GemmaConfig"],
+}
+
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_gemma"] = ["GemmaTokenizer"]
+
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_gemma_fast"] = ["GemmaTokenizerFast"]
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_gemma"] = [
+        "GemmaForCausalLM",
+        "GemmaModel",
+        "GemmaPreTrainedModel",
+        "GemmaForSequenceClassification",
+    ]
+
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_flax_gemma"] = [
+        "FlaxGemmaForCausalLM",
+        "FlaxGemmaModel",
+        "FlaxGemmaPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_gemma import GEMMA_PRETRAINED_CONFIG_ARCHIVE_MAP, GemmaConfig
+
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_gemma import GemmaTokenizer
+
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_gemma_fast import GemmaTokenizerFast
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_gemma import (
+            GemmaForCausalLM,
+            GemmaForSequenceClassification,
+            GemmaModel,
+            GemmaPreTrainedModel,
+        )
+
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_flax_gemma import (
+            FlaxGemmaForCausalLM,
+            FlaxGemmaModel,
+            FlaxGemmaPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/llava/train/transformers_replace/models/gemma/configuration_gemma.py b/llava/train/transformers_replace/models/gemma/configuration_gemma.py
new file mode 100644
index 00000000..2e758bca
--- /dev/null
+++ b/llava/train/transformers_replace/models/gemma/configuration_gemma.py
@@ -0,0 +1,147 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Gemma model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+GEMMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class GemmaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the Gemma-7B.
+
+    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 256000):
+            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GemmaModel`]
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 24576):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        head_dim (`int`, *optional*, defaults to 256):
+            The attention head dimension.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 8192):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        eos_token_id (`int`, *optional*, defaults to 1):
+            End of stream token id.
+        bos_token_id (`int`, *optional*, defaults to 2):
+            Beginning of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import GemmaModel, GemmaConfig
+
+    >>> # Initializing a Gemma gemma-7b style configuration
+    >>> configuration = GemmaConfig()
+
+    >>> # Initializing a model from the gemma-7b style configuration
+    >>> model = GemmaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "gemma"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=256000,
+        hidden_size=3072,
+        intermediate_size=24576,
+        num_hidden_layers=28,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        head_dim=256,
+        hidden_act="gelu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        bos_token_id=2,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
diff --git a/llava/train/transformers_replace/models/gemma/modeling_gemma.py b/llava/train/transformers_replace/models/gemma/modeling_gemma.py
new file mode 100644
index 00000000..e5428082
--- /dev/null
+++ b/llava/train/transformers_replace/models/gemma/modeling_gemma.py
@@ -0,0 +1,1322 @@
+# coding=utf-8
+# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Gemma model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, StaticCache
+from ...modeling_attn_mask_utils import (
+    _prepare_4d_causal_attention_mask,
+)
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from ...utils.import_utils import is_torch_fx_available
+from .configuration_gemma import GemmaConfig
+
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "GemmaConfig"
+
+
+def _get_unpad_data(attention_mask, seqlens_in_batch):
+    if seqlens_in_batch is None:
+        seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    else:
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+class GemmaRMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float()).type_as(x)
+        return output * (1 + self.weight)
+
+
+ALL_LAYERNORM_LAYERS.append(GemmaRMSNorm)
+
+
+class GemmaRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        self.register_buffer("inv_freq", None, persistent=False)
+
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if self.inv_freq is None:
+            self.inv_freq = 1.0 / (
+                self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
+            )
+
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos().to(dtype=x.dtype), emb.sin().to(dtype=x.dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Gemma
+class GemmaMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class GemmaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    # Ignore copy
+    def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if self.hidden_size % self.num_heads != 0:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        self.rotary_emb = GemmaRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            if cache_position is not None:
+                causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
+            else:
+                causal_mask = attention_mask
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.view(bsz, q_len, -1)
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->Gemma
+class GemmaFlashAttention2(GemmaAttention):
+    """
+    Gemma flash attention module. This module inherits from `GemmaAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    # Ignore copy
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (GemmaRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None, seqlens_in_batch=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in GemmaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length, seqlens_in_batch=seqlens_in_batch
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length, seqlens_in_batch):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask, seqlens_in_batch=seqlens_in_batch)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Gemma
+class GemmaSdpaAttention(GemmaAttention):
+    """
+    Gemma attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `GemmaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Ignore copy
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "GemmaModel is using GemmaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=None)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
+
+        past_key_value = getattr(self, "past_key_value", past_key_value)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; position_ids needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None and cache_position is not None:
+            causal_mask = causal_mask[:, :, cache_position, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+GEMMA_ATTENTION_CLASSES = {
+    "eager": GemmaAttention,
+    "flash_attention_2": GemmaFlashAttention2,
+    "sdpa": GemmaSdpaAttention,
+}
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->GEMMA,Llama->Gemma
+class GemmaDecoderLayer(nn.Module):
+    def __init__(self, config: GemmaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = GEMMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = GemmaMLP(config)
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            seqlens_in_batch=seqlens_in_batch,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+GEMMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`GemmaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Gemma Model outputting raw hidden-states without any specific head on top.",
+    GEMMA_START_DOCSTRING,
+)
+class GemmaPreTrainedModel(PreTrainedModel):
+    config_class = GemmaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"]
+    _no_split_modules = ["GemmaDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values", "causal_mask"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
+        if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
+            raise ValueError(
+                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
+                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
+            )
+
+        if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
+            causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device)
+            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
+
+        for layer in self.model.layers:
+            weights = layer.self_attn.o_proj.weight
+            layer.self_attn.past_key_value = cache_cls(
+                self.config, max_batch_size, max_cache_len, device=weights.device, dtype=weights.dtype
+            )
+
+    def _reset_cache(self):
+        for layer in self.model.layers:
+            layer.self_attn.past_key_value = None
+
+
+GEMMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Gemma Model outputting raw hidden-states without any specific head on top.",
+    GEMMA_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaModel with LLAMA->GEMMA,Llama->Gemma
+class GemmaModel(GemmaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]
+
+    Args:
+        config: GemmaConfig
+    """
+
+    def __init__(self, config: GemmaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        # register a causal mask to separate causal and padding mask creation. Merging happends in the attention class
+        causal_mask = torch.full((config.max_position_embeddings, config.max_position_embeddings), fill_value=1)
+        self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
+    # Ignore copy
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        past_seen_tokens = 0
+        if use_cache:  # kept for BC (cache positions)
+            if not isinstance(past_key_values, StaticCache):
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_seen_tokens = past_key_values.get_seq_length()
+
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # normalized
+        hidden_states = hidden_states * (self.config.hidden_size**0.5)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                    seqlens_in_batch,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    seqlens_in_batch=seqlens_in_batch,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(self, attention_mask, input_tensor):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        batch_size, seq_length = input_tensor.shape[:2]
+        dtype = input_tensor.dtype
+        device = input_tensor.device
+
+        # support going beyond cached `max_position_embedding`
+        if seq_length > self.causal_mask.shape[-1]:
+            causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
+            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)
+
+        if hasattr(self, "causal_mask"):  # we use the current dtype to avoid any overflows
+            causal_mask = (
+                self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * torch.finfo(dtype).min
+            )
+        else:
+            mask = torch.full(
+                (self.config.max_position_embeddings, self.config.max_position_embeddings),
+                fill_value=torch.finfo(dtype).min,
+            )
+            causal_mask = torch.triu(mask, diagonal=1)
+
+        causal_mask = causal_mask.to(dtype=dtype, device=device)
+        if attention_mask is not None and attention_mask.dim() == 2:
+            mask_length = attention_mask.shape[-1]
+            padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
+            causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(
+                padding_mask, torch.finfo(dtype).min
+            )
+
+        if self.config._attn_implementation == "sdpa":
+            is_tracing = torch.jit.is_tracing() or isinstance(input_tensor, torch.fx.Proxy)
+            if not is_tracing and attention_mask is not None and torch.any(attention_mask != 1):
+                causal_mask = causal_mask.mul(~torch.all(causal_mask == causal_mask.min(), dim=-1)[..., None]).to(
+                    dtype
+                )
+
+        return causal_mask
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->GEMMA,Llama->Gemma,llama->gemma
+class GemmaForCausalLM(GemmaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = GemmaModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, GemmaForCausalLM
+
+        >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
+        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+
+        >>> prompt = "What is your favorite condiment?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "What is your favorite condiment?"
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            seqlens_in_batch=seqlens_in_batch,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        past_length = 0
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        if past_key_value := getattr(self.model.layers[0].self_attn, "past_key_value", None):
+            # generation with static cache
+            past_length = past_key_value.get_seq_length()
+            input_ids = input_ids[:, past_length:]
+            position_ids = position_ids[:, past_length:]
+
+        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
+        # same goes for position ids. Could also help with continued generation.
+        cache_position = kwargs.get("cache_position", None)
+        if cache_position is None:
+            cache_position = torch.arange(
+                past_length, past_length + position_ids.shape[-1], device=position_ids.device
+            )
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Gemma Model transformer with a sequence classification head on top (linear layer).
+
+    [`GemmaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    GEMMA_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->GEMMA,Llama->Gemma
+class GemmaForSequenceClassification(GemmaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = GemmaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
diff --git a/llava/train/transformers_replace/llama/configuring_llama.py b/llava/train/transformers_replace/models/llama/configuring_llama.py
similarity index 100%
rename from llava/train/transformers_replace/llama/configuring_llama.py
rename to llava/train/transformers_replace/models/llama/configuring_llama.py
diff --git a/llava/train/transformers_replace/llama/modeling_llama.py b/llava/train/transformers_replace/models/llama/modeling_llama.py
similarity index 99%
rename from llava/train/transformers_replace/llama/modeling_llama.py
rename to llava/train/transformers_replace/models/llama/modeling_llama.py
index d2ab3c6b..35bf9b73 100644
--- a/llava/train/transformers_replace/llama/modeling_llama.py
+++ b/llava/train/transformers_replace/models/llama/modeling_llama.py
@@ -999,7 +999,6 @@ def forward(
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
         "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
-
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
diff --git a/llava/train/transformers_replace/models/llama/tokenization_llama.py b/llava/train/transformers_replace/models/llama/tokenization_llama.py
new file mode 100644
index 00000000..2f899727
--- /dev/null
+++ b/llava/train/transformers_replace/models/llama/tokenization_llama.py
@@ -0,0 +1,482 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization classes for LLaMA."""
+import os
+from shutil import copyfile
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from ...convert_slow_tokenizer import import_protobuf
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+
+if TYPE_CHECKING:
+    from ...tokenization_utils_base import TextInput
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
+    },
+    "tokenizer_file": {
+        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
+    },
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "hf-internal-testing/llama-tokenizer": 2048,
+}
+SPIECE_UNDERLINE = "▁"
+
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+# fmt: off
+DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
+answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
+ that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
+correct. If you don't know the answer to a question, please don't share false information."""
+# fmt: on
+
+
+class LlamaTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
+    no padding token in the original model.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+            attention mechanisms or loss computation.
+        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+
+        add_bos_token (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an `bos_token` at the start of sequences.
+        add_eos_token (`bool`, *optional*, defaults to `False`):
+            Whether or not to add an `eos_token` at the end of sequences.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
+            Whether or not the default system prompt for Llama should be used.
+        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not to add spaces between special tokens.
+        legacy (`bool`, *optional*):
+            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
+            and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
+            example:
+
+            - `legacy=True`:
+            ```python
+            >>> from transformers import T5Tokenizer
+
+            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
+            >>> tokenizer.encode("Hello <extra_id_0>.")
+            [8774, 32099, 3, 5, 1]
+            ```
+            - `legacy=False`:
+            ```python
+            >>> from transformers import T5Tokenizer
+
+            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
+            >>> tokenizer.encode("Hello <extra_id_0>.")  # the extra space `[3]` is no longer here
+            [8774, 32099, 5, 1]
+            ```
+            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
+        add_prefix_space (`bool`, *optional*, defaults to `True`):
+            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
+            other word.
+
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        use_default_system_prompt=False,
+        spaces_between_special_tokens=False,
+        legacy=None,
+        add_prefix_space=True,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
+
+        if legacy is None:
+            logger.warning_once(
+                f"You are using the default legacy behaviour of the {self.__class__}. This is"
+                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
+                " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
+                " means, and thoroughly read the reason why this was added as explained in"
+                " https://github.com/huggingface/transformers/pull/24565"
+            )
+            legacy = True
+
+        self.legacy = legacy
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.use_default_system_prompt = use_default_system_prompt
+        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
+        self.add_prefix_space = add_prefix_space
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            sp_model_kwargs=self.sp_model_kwargs,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            legacy=legacy,
+            add_prefix_space=add_prefix_space,
+            **kwargs,
+        )
+
+    @property
+    def unk_token_length(self):
+        return len(self.sp_model.encode(str(self.unk_token)))
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
+    def get_spm_processor(self, from_slow=False):
+        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        if self.legacy or from_slow:  # no dependency on protobuf
+            tokenizer.Load(self.vocab_file)
+            return tokenizer
+
+        with open(self.vocab_file, "rb") as f:
+            sp_model = f.read()
+            model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
+            model = model_pb2.ModelProto.FromString(sp_model)
+            normalizer_spec = model_pb2.NormalizerSpec()
+            normalizer_spec.add_dummy_prefix = False
+            model.normalizer_spec.MergeFrom(normalizer_spec)
+            sp_model = model.SerializeToString()
+            tokenizer.LoadFromSerializedProto(sp_model)
+        return tokenizer
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
+    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
+        """
+        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
+        first token is special.
+        """
+        if self.legacy or len(text) == 0:
+            return super().tokenize(text, **kwargs)
+
+        text = text.replace(SPIECE_UNDERLINE, " ")
+        if self.add_prefix_space:
+            text = SPIECE_UNDERLINE + text
+
+        tokens = super().tokenize(text, **kwargs)
+
+        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
+            tokens = tokens[1:]
+        return tokens
+
+    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
+    def _tokenize(self, text, **kwargs):
+        """
+        Returns a tokenized string.
+
+        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
+        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
+        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
+        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
+        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
+        """
+        tokens = self.sp_model.encode(text, out_type=str)
+        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
+            return tokens
+
+        # 1. Encode string + prefix ex: "<unk> Hey"
+        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
+        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
+        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        # since we manually add the prefix space, we have to remove it when decoding
+        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
+            tokens[0] = tokens[0][1:]
+
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for i, token in enumerate(tokens):
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special and i != 0 and self.legacy:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = bos_token_id + token_ids_0 + eos_token_id
+
+        if token_ids_1 is not None:
+            output = output + bos_token_id + token_ids_1 + eos_token_id
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        bos_token_id = [1] if self.add_bos_token else []
+        eos_token_id = [1] if self.add_eos_token else []
+
+        if token_ids_1 is None:
+            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+        return (
+            bos_token_id
+            + ([0] * len(token_ids_0))
+            + eos_token_id
+            + bos_token_id
+            + ([0] * len(token_ids_1))
+            + eos_token_id
+        )
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+        sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        if token_ids_1 is None, only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of ids.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+        if token_ids_1 is not None:
+            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+        return output
+
+    @property
+    def default_chat_template(self):
+        """
+        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
+        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
+        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
+        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
+        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
+        to fine-tune a model with more flexible role ordering!
+
+        The output should look something like:
+
+        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
+        <bos>[INST] Prompt [/INST]
+
+        The reference for this chat template is [this code
+        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
+        in the original repository.
+        """
+        logger.warning_once(
+            "\nNo chat template is defined for this tokenizer - using the default template "
+            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
+            "your model, please set `tokenizer.chat_template` to an appropriate template. "
+            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
+        )
+        template = (
+            "{% if messages[0]['role'] == 'system' %}"
+            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
+            "{% set system_message = messages[0]['content'] %}"
+            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
+            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
+            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
+            "{% else %}"
+            "{% set loop_messages = messages %}"
+            "{% set system_message = false %}"
+            "{% endif %}"
+            "{% for message in loop_messages %}"  # Loop over all non-system messages
+            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
+            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
+            "{% endif %}"
+            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
+            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
+            "{% else %}"
+            "{% set content = message['content'] %}"
+            "{% endif %}"
+            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
+            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
+            "{% elif message['role'] == 'system' %}"
+            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{ ' '  + content.strip() + ' ' + eos_token }}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
+        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
+        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
+
+        return template
diff --git a/llava/train/transformers_replace/models/mistral/__init__.py b/llava/train/transformers_replace/models/mistral/__init__.py
new file mode 100644
index 00000000..0addcc90
--- /dev/null
+++ b/llava/train/transformers_replace/models/mistral/__init__.py
@@ -0,0 +1,62 @@
+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_mistral": ["MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP", "MistralConfig"],
+}
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_mistral"] = [
+        "MistralForCausalLM",
+        "MistralModel",
+        "MistralPreTrainedModel",
+        "MistralForSequenceClassification",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_mistral import MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP, MistralConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_mistral import (
+            MistralForCausalLM,
+            MistralForSequenceClassification,
+            MistralModel,
+            MistralPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
\ No newline at end of file
diff --git a/llava/train/transformers_replace/models/mistral/configuration_mistral.py b/llava/train/transformers_replace/models/mistral/configuration_mistral.py
new file mode 100644
index 00000000..568e230e
--- /dev/null
+++ b/llava/train/transformers_replace/models/mistral/configuration_mistral.py
@@ -0,0 +1,152 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Mistral model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
+    "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
+}
+
+
+class MistralConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MistralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MistralModel, MistralConfig
+
+    >>> # Initializing a Mistral 7B style configuration
+    >>> configuration = MistralConfig()
+
+    >>> # Initializing a model from the Mistral 7B style configuration
+    >>> model = MistralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mistral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
\ No newline at end of file
diff --git a/llava/train/transformers_replace/models/mistral/modeling_mistral.py b/llava/train/transformers_replace/models/mistral/modeling_mistral.py
new file mode 100644
index 00000000..ecc265fe
--- /dev/null
+++ b/llava/train/transformers_replace/models/mistral/modeling_mistral.py
@@ -0,0 +1,1364 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Mistral model."""
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from .configuration_mistral import MistralConfig
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MistralConfig"
+Cache = Tuple[torch.Tensor]
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask, seqlens_in_batch):
+    if seqlens_in_batch is None:
+        seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    else:
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
+class MistralRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MistralRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral
+class MistralRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MistralMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MistralAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = MistralRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MistralFlashAttention2(MistralAttention):
+    """
+    Mistral flash attention module. This module inherits from `MistralAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+            seqlens_in_batch=seqlens_in_batch
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+        seqlens_in_batch=None,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length, seqlens_in_batch=seqlens_in_batch
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length, seqlens_in_batch):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask, seqlens_in_batch=seqlens_in_batch)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mistral
+class MistralSdpaAttention(MistralAttention):
+    """
+    Mistral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MistralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MistralAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MistralModel is using MistralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+class MistralDecoderLayer(nn.Module):
+    def __init__(self, config: MistralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MistralFlashAttention2(config, layer_idx)
+
+        self.mlp = MistralMLP(config)
+        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            seqlens_in_batch=seqlens_in_batch,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+MISTRAL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MistralConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralPreTrainedModel(PreTrainedModel):
+    config_class = MistralConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MistralDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MISTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralModel(MistralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
+
+    Args:
+        config: MistralConfig
+    """
+
+    def __init__(self, config: MistralConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = "flash_attention_2"
+        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        # 2d mask is passed through the layers
+        attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    seqlens_in_batch
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    seqlens_in_batch=seqlens_in_batch
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class MistralForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            seqlens_in_batch=seqlens_in_batch,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Mistral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MISTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL
+class MistralForSequenceClassification(MistralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MistralModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
diff --git a/llava/train/transformers_replace/models/mixtral/__init__.py b/llava/train/transformers_replace/models/mixtral/__init__.py
new file mode 100644
index 00000000..03df7820
--- /dev/null
+++ b/llava/train/transformers_replace/models/mixtral/__init__.py
@@ -0,0 +1,62 @@
+# Copyright 2023 Mixtral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_mixtral": ["MIXTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP", "MixtralConfig"],
+}
+
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_mixtral"] = [
+        "MixtralForCausalLM",
+        "MixtralModel",
+        "MixtralPreTrainedModel",
+        "MixtralForSequenceClassification",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_mixtral import MIXTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP, MixtralConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_mixtral import (
+            MixtralForCausalLM,
+            MixtralForSequenceClassification,
+            MixtralModel,
+            MixtralPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
\ No newline at end of file
diff --git a/llava/train/transformers_replace/models/mixtral/configuration_mixtral.py b/llava/train/transformers_replace/models/mixtral/configuration_mixtral.py
new file mode 100644
index 00000000..30f1191a
--- /dev/null
+++ b/llava/train/transformers_replace/models/mixtral/configuration_mixtral.py
@@ -0,0 +1,169 @@
+# coding=utf-8
+# Copyright 2023 Mixtral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Mixtral model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MIXTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "mistral-ai/Mixtral-8x7B": "https://huggingface.co/mistral-ai/Mixtral-8x7B/resolve/main/config.json",
+}
+
+
+class MixtralConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
+    Mixtral model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Mixtral-7B-v0.1 or Mixtral-7B-Instruct-v0.1.
+
+    [mixtralai/Mixtral-8x7B](https://huggingface.co/mixtralai/Mixtral-8x7B)
+    [mixtralai/Mixtral-7B-Instruct-v0.1](https://huggingface.co/mixtralai/Mixtral-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MixtralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabeling this will also
+            allow the model to output the auxiliary loss. See [here]() for more details
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+
+    ```python
+    >>> from transformers import MixtralModel, MixtralConfig
+
+    >>> # Initializing a Mixtral 7B style configuration
+    >>> configuration = MixtralConfig()
+
+    >>> # Initializing a model from the Mixtral 7B style configuration
+    >>> model = MixtralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mixtral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
\ No newline at end of file
diff --git a/llava/train/transformers_replace/models/mixtral/modeling_mixtral.py b/llava/train/transformers_replace/models/mixtral/modeling_mixtral.py
new file mode 100644
index 00000000..52d2613d
--- /dev/null
+++ b/llava/train/transformers_replace/models/mixtral/modeling_mixtral.py
@@ -0,0 +1,1627 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Mixtral model."""
+from dataclasses import dataclass
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import (
+    ModelOutput,
+    SequenceClassifierOutputWithPast,
+)
+from ...modeling_utils import PreTrainedModel
+from ...utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from ...utils.import_utils import is_torch_fx_available
+from .configuration_mixtral import MixtralConfig
+
+
+from flash_attn import flash_attn_func, flash_attn_varlen_func
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MixtralConfig"
+Cache = Tuple[torch.Tensor]
+
+
+@dataclass
+class MoeCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for causal language model (or autoregressive) with mixture of experts outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+
+        aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
+            aux_loss for the sparse modules.
+
+        router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
+
+            Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary
+            loss for Mixture of Experts models.
+
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    aux_loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    router_logits: Optional[Tuple[torch.FloatTensor]] = None
+
+@dataclass
+class MoeModelOutputWithPast(ModelOutput):
+    """
+    Base class for model's outputs, with potential hidden states and attentions.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
+            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
+            encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
+            `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
+            input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
+
+            Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary
+            loss for Mixture of Experts models.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    router_logits: Optional[Tuple[torch.FloatTensor]] = None
+
+
+
+
+def load_balancing_loss_func(gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2) -> float:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        num_experts (`int`, *optional*):
+            Number of experts
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    # treat `top_k` as tokens (shape is `top_k X [batch_size X sequence_length]`)
+    selected_experts = selected_experts.reshape(-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+    expert_mask = torch.max(expert_mask, dim=-2).values
+
+    # Compute the percentage of tokens routed to each experts
+    tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+    # Compute the average probability of routing to these experts
+    router_prob_per_expert = torch.mean(routing_weights, dim=0)
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(-1))
+    return overall_loss * num_experts
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask, seqlens_in_batch):
+    if seqlens_in_batch is None:
+        seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    else:
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+        max_seqlen_in_batch = seqlens_in_batch.max().item()
+        cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mixtral
+class MixtralRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MixtralRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mixtral
+class MixtralRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Mixtral
+class MixtralAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: MixtralConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = MixtralRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Mixtral
+class MixtralFlashAttention2(MixtralAttention):
+    """
+    Mixtral flash attention module. This module inherits from `MixtralAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            elif torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+            seqlens_in_batch=seqlens_in_batch
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+        seqlens_in_batch=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length, seqlens_in_batch=seqlens_in_batch
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length, seqlens_in_batch):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask, seqlens_in_batch=seqlens_in_batch)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Mixtral
+class MixtralSdpaAttention(MixtralAttention):
+    """
+    Mixtral attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MixtralAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MixtralAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MixtralModel is using MixtralSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+class MixtralBLockSparseTop2MLP(nn.Module):
+    def __init__(self, config: MixtralConfig):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.hidden_size
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class MixtralSparseMoeBlock(nn.Module):
+    """
+    This implementation is
+    strictly equivalent to standard MoE with full capacity (no
+    dropped tokens). It's faster since it formulates MoE operations
+    in terms of block-sparse operations to accomodate imbalanced
+    assignments of tokens to experts, whereas standard MoE either
+    (1) drop tokens at the cost of reduced performance or (2) set
+    capacity factor to number of experts and thus waste computation
+    and memory on padding.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_dim = config.hidden_size
+        self.ffn_dim = config.intermediate_size
+        self.num_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+        self.experts = nn.ModuleList([MixtralBLockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """ """
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be sollicitated
+        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            if top_x.shape[0] == 0:
+                continue
+
+            # in torch it is faster to index using lists than torch tensors
+            top_x_list = top_x.tolist()
+            idx_list = idx.tolist()
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class MixtralDecoderLayer(nn.Module):
+    def __init__(self, config: MixtralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MixtralFlashAttention2(config, layer_idx)
+
+        self.block_sparse_moe = MixtralSparseMoeBlock(config)
+        self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+                should not be returned during inference.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            seqlens_in_batch=seqlens_in_batch,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+
+MIXTRAL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MixtralConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mixtral Model outputting raw hidden-states without any specific head on top.",
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->Mixtral
+class MixtralPreTrainedModel(PreTrainedModel):
+    config_class = MixtralConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MixtralDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MIXTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        output_router_logits (`bool`, *optional*):
+            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mixtral Model outputting raw hidden-states without any specific head on top.",
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->Mixtral
+class MixtralModel(MixtralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MixtralDecoderLayer`]
+
+    Args:
+        config: MixtralConfig
+    """
+
+    def __init__(self, config: MixtralConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = "flash_attention_2"
+        self.norm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        past_key_values_length = 0
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+            if is_padding_right:
+                raise ValueError(
+                    "You are attempting to perform batched generation with padding_side='right'"
+                    " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        # 2d mask is passed through the layers
+        attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+       
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_logits = () if output_router_logits else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                    seqlens_in_batch
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    output_router_logits=output_router_logits,
+                    use_cache=use_cache,
+                    seqlens_in_batch=seqlens_in_batch
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+            if output_router_logits:
+                all_router_logits += (layer_outputs[-1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
+                if v is not None
+            )
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_logits=all_router_logits,
+        )
+
+
+class MixtralForCausalLM(MixtralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MixtralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    # Ignore copy
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        seqlens_in_batch: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MixtralForCausalLM
+
+        >>> model = MixtralForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+            seqlens_in_batch=seqlens_in_batch,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            if output_router_logits:
+                output = (aux_loss,) + output
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Mixtral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MixtralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mixtral, LLAMA->MIXTRAL
+class MixtralForSequenceClassification(MixtralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MixtralModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
diff --git a/llava/train/utils.py b/llava/train/utils.py
new file mode 100644
index 00000000..31c08119
--- /dev/null
+++ b/llava/train/utils.py
@@ -0,0 +1,115 @@
+import os
+import re
+import torch
+import pathlib
+from dataclasses import dataclass
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from accelerate.hooks import add_hook_to_module
+
+
+def rprint(*args, **kwargs):
+    rank = int(os.environ.get("RANK", 0))
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    if world_size > 1:
+        return print(f"[dist-{rank}-of-{world_size}]", *args, **kwargs)
+    else:
+        return print(*args, **kwargs)
+
+
+def mprint(*args, **kwargs):
+    rank = int(os.environ.get("RANK", 0))
+    world_size = int(os.environ.get("WORLD_SIZE", 1))
+    if world_size > 1:
+        if rank == 0:
+            return print(f"[dist-{rank}-of-{world_size}]", *args, **kwargs)
+        else:
+            return
+    else:
+        return print(*args, **kwargs)
+
+
+def is_local(model_name_or_path: str) -> bool:
+    return os.path.isdir(model_name_or_path)
+
+
+def get_checkpoint_path(
+    output_dir: str, checkpoint_prefix: str = "checkpoint"
+) -> str | None:
+    output_dir = os.path.abspath(output_dir)
+    pathlib_dir = pathlib.Path(output_dir)
+
+    if list(pathlib_dir.glob("config.json")):
+        # training has been finished
+        return output_dir, False
+    else:
+        try:
+            ordering_and_checkpoint_path = []
+            glob_checkpoints = [
+                str(x)
+                for x in pathlib.Path(output_dir).glob(f"{checkpoint_prefix}-*")
+                if os.path.isdir(x)
+            ]
+            for path in glob_checkpoints:
+                regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
+                if regex_match is not None and regex_match.groups() is not None:
+                    ordering_and_checkpoint_path.append(
+                        (int(regex_match.groups()[0]), path)
+                    )
+            checkpoints_sorted = sorted(ordering_and_checkpoint_path)
+            return checkpoints_sorted[-1][1], True
+        except:
+            return None, True
+
+
+def prepare_config_for_training(
+    config: PretrainedConfig, model_args: dataclass, training_args: dataclass, data_args: dataclass
+) -> None:
+    assert model_args.vision_tower is not None, "requires vision tower"
+    ## set module configurations
+    if getattr(config, "llm_cfg", None) is None:
+        config.llm_cfg = model_args.model_name_or_path
+    if getattr(config, "vision_tower_cfg", None) is None:
+        config.vision_tower_cfg = model_args.vision_tower
+    if getattr(config, "mm_projector_cfg", None) is None:
+        config.mm_projector_cfg = model_args.mm_projector
+    ## set default dtype
+    config.model_dtype = torch.bfloat16 if training_args.bf16 else torch.float16
+    config.model_dtype = config.model_dtype.__str__()
+    ## set tuning modules
+    config.tune_language_model = training_args.tune_language_model
+    config.tune_vision_tower = training_args.tune_vision_tower
+    config.tune_mm_projector = training_args.tune_mm_projector
+    ## set data args
+    config.image_aspect_ratio = data_args.image_aspect_ratio
+    ## extra vision tower configuration
+    if getattr(config, "vision_tower_cfg", None) is not None:
+        config.mm_vision_select_layer = model_args.mm_vision_select_layer
+        config.mm_vision_select_feature = model_args.mm_vision_select_feature
+        ## vision tower configurations
+        config.vision_resolution = model_args.vision_resolution
+        config.interpolate_mode = model_args.interpolate_mode
+        config.drop_path_rate = model_args.drop_path_rate
+        config.s2 = model_args.s2
+        config.s2_scales = model_args.s2_scales
+        config.s2_max_split_size = model_args.s2_max_split_size
+
+
+def vision_resolution_elevation(model: PreTrainedModel, config: PretrainedConfig):
+    vision_tower = model.get_vision_tower()
+    if (
+        vision_tower is not None
+        and "radio" not in vision_tower.__class__.__name__.lower()
+    ):
+        vision_tower._maybe_resize_pos_embeds(
+            model=vision_tower.vision_tower,
+            image_processor=vision_tower.image_processor,
+            resolution=getattr(config, "vision_resolution", -1),
+            interpolate_mode=getattr(config, "interpolate_mode", "linear"),
+        )
+
+
+def unit_test_rope_scaling(
+    model: PreTrainedModel, config: PretrainedConfig, training_args: dataclass
+):
+    return False
diff --git a/llava/unit_test_utils.py b/llava/unit_test_utils.py
new file mode 100644
index 00000000..dda3a0cc
--- /dev/null
+++ b/llava/unit_test_utils.py
@@ -0,0 +1,102 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest.case import _id as __id, skip as __skip
+
+def requires_gpu(reason=None):
+    import torch
+    reason = "no GPUs detected. Only test in GPU environemnts" if reason is None else reason
+    if not torch.cuda.is_available():
+        return __skip(reason)
+    return __id
+
+
+def requires_lustre(reason=None):
+    import os.path as osp
+    if not osp.isdir("/lustre"):
+        reason = "lustre path is not avaliable." if reason is None else reason
+        return __skip(reason)
+    return __id
+
+
+def requires_lustre_nvr_datataset(reason=None):
+    import os.path as osp
+    if not osp.isdir("/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset"):
+        reason = "nvr dataset path is not avaliable." if reason is None else reason
+        return __skip(reason)
+    return __id
+
+
+def test_make_supervised_data_module(dataset_name, max_samples=-1, batch_size=32, num_workers=16, skip_before=0):
+    import torch
+    import transformers
+    
+    from llava import conversation as conversation_lib
+    from llava.data.dataset import make_supervised_data_module
+    from llava.train.args import DataArguments, TrainingArguments
+    from llava.model.multimodal_encoder.siglip.image_processing_siglip import SiglipImageProcessor
+    
+    # datasets_mixture.register_datasets_mixtures()
+    tokenizer = transformers.AutoTokenizer.from_pretrained(
+        "lmsys/vicuna-7b-v1.5",
+        model_max_length=8192,
+        padding_side="right",
+        use_fast=False,
+        legacy=False,
+    )
+    tokenizer.pad_token = tokenizer.unk_token
+    image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+
+    data_args = DataArguments(
+        data_mixture=dataset_name,
+        is_multimodal=True,
+        lazy_preprocess=True,
+    )
+    data_args.image_processor = image_processor
+    conversation_lib.default_conversation = conversation_lib.conv_templates["vicuna_v1"]
+    training_args = TrainingArguments(
+        output_dir="output",
+    )
+
+    # training_args["process_index"] = 0
+    # training_args.world_size = 1
+    data_args.mm_use_im_start_end = False
+    data_module = make_supervised_data_module(
+        tokenizer=tokenizer,
+        data_args=data_args,
+        training_args=training_args,
+    )
+
+    dataset = data_module["train_dataset"]
+    dataset_len = len(data_module["train_dataset"])
+    from torch.utils.data import DataLoader
+
+    dloader = DataLoader(dataset, collate_fn=data_module["data_collator"], batch_size=batch_size, num_workers=num_workers)
+    dloader_len = len(dloader)
+    for idx, batch in enumerate(dloader):
+        if idx < skip_before:
+            continue
+        
+        if max_samples > 0 and idx > min(max_samples, dloader_len):
+            break
+
+        info = []
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                info.append((k, v.shape))
+            else:
+                info.append((k, type(v)))
+        print(f"[{idx}/{len(dloader)}]", info)
diff --git a/llava/wids/wids.py b/llava/wids/wids.py
index 9bb4d060..97538895 100644
--- a/llava/wids/wids.py
+++ b/llava/wids/wids.py
@@ -9,6 +9,7 @@
 import sys
 import uuid
 import warnings
+import tempfile
 from functools import partial
 from typing import Any, BinaryIO, Dict, Optional, TypeVar, Union
 from urllib.parse import quote, urlparse
@@ -92,7 +93,8 @@ def compute_num_samples(fname):
 def splitname(fname):
     """Returns the basename and extension of a filename"""
     assert "." in fname, "Filename must have an extension"
-    basename, extension = re.match(r"^((?:.*/)?.*?)(\..*)$", fname).groups()
+    # basename, extension = re.match(r"^((?:.*/)?.*?)(\..*)$", fname).groups()
+    basename, extension = os.path.splitext(fname)
     return basename, extension
 
 
@@ -192,6 +194,14 @@ def default_decoder(sample: Dict[str, Any], format: Optional[Union[bool, str]] =
             import pickle
 
             sample[key] = pickle.load(stream)
+        elif extension == "mp4":
+            # Write stream to a temporary file
+            # with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmpfile:
+            #     tmpfile.write(stream.read())
+            #     tmpfile_path = tmpfile.name
+
+            # sample[key] = tmpfile_path
+            sample[key] = io.BytesIO(stream.read())
     return sample
 
 
@@ -270,7 +280,11 @@ def __len__(self):
 
     def __getitem__(self, idx):
         # Get indexes of files for the sample at index idx
-        indexes = self.samples[idx]
+        try:
+            indexes = self.samples[idx]
+        except IndexError as e:
+            print(f"[wids-debug] curr idx: {idx}, total sample length: {len(self.samples)} {e}")
+            raise e
         sample = {}
         key = None
         for i in indexes:
diff --git a/pyproject.toml b/pyproject.toml
old mode 100644
new mode 100755
index 5efbb6c9..44ad02e0
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,18 +14,21 @@ classifiers = [
 ]
 dependencies = [
     "torch==2.0.1", "torchvision==0.15.2",
-    "transformers==4.31.0", "tokenizers>=0.12.1,<0.14", "sentencepiece==0.1.99", "shortuuid",
-    "accelerate==0.21.0", "peft==0.5.0", "bitsandbytes==0.41.0",
+    "transformers==4.36.2", "tokenizers>=0.15.2", "sentencepiece==0.1.99", "shortuuid",
+    "accelerate==0.27.2", "peft==0.5.0", "bitsandbytes==0.41.0",
     "pydantic<2,>=1", "markdown2[all]", "numpy", "scikit-learn==1.2.2",
     "gradio==3.35.2", "gradio_client==0.2.9",
     "requests", "httpx==0.24.0", "uvicorn", "fastapi",
-    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.6.13",
+    "einops==0.6.1", "einops-exts==0.0.4", "timm==0.9.12",
     "openpyxl==3.1.2", "pytorchvideo==0.1.5", "decord==0.6.0",
-    "datasets==2.16.1", "openai==1.8.0", "webdataset==0.2.86"
+    "datasets==2.16.1", "openai==1.8.0", "webdataset==0.2.86",
+    "nltk==3.3", "pywsd==1.2.4", "opencv-python==4.8.0.74",
+    "s2wrapper@git+https://github.com/bfshi/scaling_on_scales",
 ]
 
 [project.optional-dependencies]
 train = ["deepspeed==0.9.5", "ninja", "wandb"]
+eval = ["mmengine", "word2number", "Levenshtein", "nltk", "pywsd"]
 
 [project.urls]
 "Homepage" = "https://hanlab.mit.edu/projects/vila"
diff --git a/scripts/convert_gqa_for_eval.py b/scripts/convert_gqa_for_eval.py
old mode 100644
new mode 100755
index 4d46c8b8..0872f79d
--- a/scripts/convert_gqa_for_eval.py
+++ b/scripts/convert_gqa_for_eval.py
@@ -1,3 +1,19 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
 import os
 import json
 import argparse
diff --git a/scripts/convert_mmbench_for_submission.py b/scripts/convert_mmbench_for_submission.py
old mode 100644
new mode 100755
index a310d00a..d284d742
--- a/scripts/convert_mmbench_for_submission.py
+++ b/scripts/convert_mmbench_for_submission.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import json
@@ -26,4 +40,4 @@ def get_args():
         pred = json.loads(pred)
         cur_df.loc[df['index'] == pred['question_id'], 'prediction'] = pred['text']
 
-    cur_df.to_excel(os.path.join(args.upload_dir, f"{args.experiment}.xlsx"), index=False, engine='openpyxl')
+    cur_df.to_excel(os.path.join(args.upload_dir, f"{args.experiment}_upload.xlsx"), index=False, engine='openpyxl')
diff --git a/scripts/convert_mmvet_for_eval.py b/scripts/convert_mmvet_for_eval.py
old mode 100644
new mode 100755
index baee2b87..b47b86ac
--- a/scripts/convert_mmvet_for_eval.py
+++ b/scripts/convert_mmvet_for_eval.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import json
diff --git a/scripts/convert_seed_for_submission.py b/scripts/convert_seed_for_submission.py
old mode 100644
new mode 100755
index 873453ea..ac6b0c4e
--- a/scripts/convert_seed_for_submission.py
+++ b/scripts/convert_seed_for_submission.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import json
diff --git a/scripts/convert_sqa_to_llava.py b/scripts/convert_sqa_to_llava.py
old mode 100644
new mode 100755
index 231e52a6..7d64bec0
--- a/scripts/convert_sqa_to_llava.py
+++ b/scripts/convert_sqa_to_llava.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import json
 import os
diff --git a/scripts/convert_sqa_to_llava_base_prompt.py b/scripts/convert_sqa_to_llava_base_prompt.py
old mode 100644
new mode 100755
index 00db0c9a..a331bf2a
--- a/scripts/convert_sqa_to_llava_base_prompt.py
+++ b/scripts/convert_sqa_to_llava_base_prompt.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 def get_question_text(problem):
     question = problem['question']
diff --git a/scripts/convert_vizwiz_for_submission.py b/scripts/convert_vizwiz_for_submission.py
old mode 100644
new mode 100755
index d9c069dc..2ebc860d
--- a/scripts/convert_vizwiz_for_submission.py
+++ b/scripts/convert_vizwiz_for_submission.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import argparse
diff --git a/scripts/convert_vqav2_for_submission.py b/scripts/convert_vqav2_for_submission.py
old mode 100644
new mode 100755
index c1ad998b..6589239b
--- a/scripts/convert_vqav2_for_submission.py
+++ b/scripts/convert_vqav2_for_submission.py
@@ -1,4 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import argparse
@@ -10,7 +24,6 @@
 def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument('--dir', type=str, default="./playground/data/eval/vqav2")
-    parser.add_argument('--ckpt', type=str, required=True)
     parser.add_argument('--split', type=str, required=True)
     return parser.parse_args()
 
@@ -19,9 +32,9 @@ def parse_args():
 
     args = parse_args()
 
-    src = os.path.join(args.dir, 'answers', args.split, args.ckpt, 'merge.jsonl')
+    src = os.path.join(args.dir, args.split, 'answers', 'merge.jsonl')
     test_split = os.path.join(args.dir, 'llava_vqav2_mscoco_test2015.jsonl')
-    dst = os.path.join(args.dir, 'answers_upload', args.split, f'{args.ckpt}.json')
+    dst = os.path.join(args.dir, args.split, f'{args.split}_answers_upload.json')
     os.makedirs(os.path.dirname(dst), exist_ok=True)
 
     results = []
diff --git a/scripts/extract_mm_projector.py b/scripts/extract_mm_projector.py
old mode 100644
new mode 100755
index 6e17267c..7e92df92
--- a/scripts/extract_mm_projector.py
+++ b/scripts/extract_mm_projector.py
@@ -1,11 +1,18 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
-
-"""
-This is just a utility that I use to extract the projector for quantized models.
-It is NOT necessary at all to train, or run inference/serve demos.
-Use this script ONLY if you fully understand its implications.
-"""
-
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
 
 import os
 import argparse
@@ -15,34 +22,40 @@
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='Extract MMProjector weights')
-    parser.add_argument('--model-path', type=str, help='model folder')
-    parser.add_argument('--output', type=str, help='output file')
+    parser = argparse.ArgumentParser(description="Extract MMProjector weights")
+    parser.add_argument("--model_name_or_path", type=str, help="model folder")
+    parser.add_argument("--output", type=str, help="output file")
     args = parser.parse_args()
     return args
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     args = parse_args()
 
-    keys_to_match = ['mm_projector']
+    keys_to_match = ["mm_projector", "embed_tokens", "transformer.wte"]
     ckpt_to_key = defaultdict(list)
     try:
-        model_indices = json.load(open(os.path.join(args.model_path, 'pytorch_model.bin.index.json')))
-        for k, v in model_indices['weight_map'].items():
+        model_indices = json.load(
+            open(os.path.join(args.model_name_or_path, "pytorch_model.bin.index.json"))
+        )
+        for k, v in model_indices["weight_map"].items():
             if any(key_match in k for key_match in keys_to_match):
                 ckpt_to_key[v].append(k)
     except FileNotFoundError:
         # Smaller models or model checkpoints saved by DeepSpeed.
-        v = 'pytorch_model.bin'
-        for k in torch.load(os.path.join(args.model_path, v), map_location='cpu').keys():
+        v = "pytorch_model.bin"
+        for k in torch.load(
+            os.path.join(args.model_name_or_path, v), map_location="cpu"
+        ).keys():
             if any(key_match in k for key_match in keys_to_match):
                 ckpt_to_key[v].append(k)
 
     loaded_weights = {}
 
     for ckpt_name, weight_keys in ckpt_to_key.items():
-        ckpt = torch.load(os.path.join(args.model_path, ckpt_name), map_location='cpu')
+        ckpt = torch.load(
+            os.path.join(args.model_name_or_path, ckpt_name), map_location="cpu"
+        )
         for k in weight_keys:
             loaded_weights[k] = ckpt[k]
 
diff --git a/scripts/merge_lora_weights.py b/scripts/merge_lora_weights.py
deleted file mode 100644
index 1633da78..00000000
--- a/scripts/merge_lora_weights.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# This file is originated from: https://github.com/haotian-liu/LLaVA/
-
-import argparse
-from llava.model.builder import load_pretrained_model
-from llava.mm_utils import get_model_name_from_path
-
-
-def merge_lora(args):
-    model_name = get_model_name_from_path(args.model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map='cpu')
-
-    model.save_pretrained(args.save_model_path)
-    tokenizer.save_pretrained(args.save_model_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, required=True)
-    parser.add_argument("--model-base", type=str, required=True)
-    parser.add_argument("--save-model-path", type=str, required=True)
-
-    args = parser.parse_args()
-
-    merge_lora(args)
diff --git a/scripts/v1_5/eval/ai2d.sh b/scripts/v1_5/eval/ai2d.sh
new file mode 100755
index 00000000..f0589343
--- /dev/null
+++ b/scripts/v1_5/eval/ai2d.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.evaluate_vqa \
+    --model-path $MODEL_PATH \
+    --image-folder ./playground/data/eval/ai2d \
+    --dataset ai2diagram_test \
+    --conv-mode $CONV_MODE \
+    --answers-file ./eval_output/$CKPT/ai2d/answers/merge.jsonl
+
+python -m llava.eval.evaluate_vqa_score --answers-file ./eval_output/$CKPT/ai2d/answers/merge.jsonl  --dataset ai2diagram_test
\ No newline at end of file
diff --git a/scripts/v1_5/eval/ai2d_sharded.sh b/scripts/v1_5/eval/ai2d_sharded.sh
new file mode 100755
index 00000000..13212df0
--- /dev/null
+++ b/scripts/v1_5/eval/ai2d_sharded.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.evaluate_vqa \
+        --model-path $MODEL_PATH \
+        --image-folder ./playground/data/eval/ai2d \
+        --dataset ai2diagram_test \
+        --answers-file ./eval_output/$CKPT/ai2d/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/ai2d/$SPLIT/answers/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/ai2d/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python -m llava.eval.evaluate_vqa_score --answers-file $output_file  --dataset ai2diagram_test
\ No newline at end of file
diff --git a/scripts/v1_5/eval/chartqa.sh b/scripts/v1_5/eval/chartqa.sh
new file mode 100755
index 00000000..10ffdc17
--- /dev/null
+++ b/scripts/v1_5/eval/chartqa.sh
@@ -0,0 +1,25 @@
+#!/bin/bash
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.evaluate_vqa \
+    --model-path $MODEL_PATH \
+    --image-folder ./playground/data/eval/chartqa \
+    --dataset  chartqa_test_human \
+    --conv-mode $CONV_MODE \
+    --answers-file ./eval_output/$CKPT/chartqa/answers1/merge.jsonl
+
+python -m llava.eval.evaluate_vqa_score --answers-file ./eval_output/$CKPT/chartqa/answers1/merge.jsonl --dataset  chartqa_test_human
+
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.evaluate_vqa \
+    --model-path $MODEL_PATH \
+    --image-folder ./playground/data/eval/chartqa \
+    --dataset  chartqa_test_augmented \
+    --conv-mode $CONV_MODE \
+    --answers-file ./eval_output/$CKPT/chartqa/answers2/merge.jsonl
+
+python -m llava.eval.evaluate_vqa_score --answers-file --answers-file ./eval_output/$CKPT/chartqa/answers2/merge.jsonl --dataset  chartqa_test_human
\ No newline at end of file
diff --git a/scripts/v1_5/eval/chartqa_sharded.sh b/scripts/v1_5/eval/chartqa_sharded.sh
new file mode 100755
index 00000000..ffc4c369
--- /dev/null
+++ b/scripts/v1_5/eval/chartqa_sharded.sh
@@ -0,0 +1,73 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.evaluate_vqa \
+        --model-path $MODEL_PATH \
+        --image-folder ./playground/data/eval/chartqa \
+        --dataset  chartqa_test_human \
+        --answers-file ./eval_output/$CKPT/chartqa/$SPLIT/answers1/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/chartqa/$SPLIT/answers1/merge.jsonl 
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/chartqa/$SPLIT/answers1/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+
+python -m llava.eval.evaluate_vqa_score --answers-file $output_file --dataset  chartqa_test_human
+
+
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.evaluate_vqa \
+        --model-path $MODEL_PATH \
+        --image-folder ./playground/data/eval/chartqa \
+        --dataset  chartqa_test_augmented \
+        --answers-file ./eval_output/$CKPT/chartqa/$SPLIT/answers2/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode hermes-2 &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/chartqa/$SPLIT/answers2/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/chartqa/$SPLIT/answers2/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python -m llava.eval.evaluate_vqa_score --answers-file $output_file --dataset  chartqa_test_augmented
\ No newline at end of file
diff --git a/scripts/v1_5/eval/copy_predictions.py b/scripts/v1_5/eval/copy_predictions.py
new file mode 100755
index 00000000..4a1a3562
--- /dev/null
+++ b/scripts/v1_5/eval/copy_predictions.py
@@ -0,0 +1,51 @@
+# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import shutil
+import sys
+
+def main(model_name):
+    # Define the dataset and prediction file mapping
+    datasets = {
+        "vqav2": "playground/data/eval/vqav2/answers_upload/llava_vqav2_mscoco_test-dev2015/{}.json".format(model_name),
+        "vizwiz": "playground/data/eval/vizwiz/answers_upload/{}.json".format(model_name),
+        "mmbench": "playground/data/eval/mmbench/answers_upload/mmbench_dev_20230712/{}.xlsx".format(model_name),
+        "mmbench_cn": "playground/data/eval/mmbench_cn/answers_upload/mmbench_dev_cn_20231003/{}.xlsx".format(model_name),
+        "mmmu": "playground/data/eval/MMMU/test_results/{}.json".format(model_name),
+    }
+
+    # Create the base directory
+    base_dir = "playground/data/predictions_upload/{}".format(model_name)
+    os.makedirs(base_dir, exist_ok=True)
+
+    # Copy each prediction file to its new location
+    for dataset, pred_file in datasets.items():
+        # Create the directory for the dataset
+        dataset_dir = os.path.join(base_dir, dataset)
+        os.makedirs(dataset_dir, exist_ok=True)
+
+        # Copy the prediction file
+        dest_file = os.path.join(dataset_dir, os.path.basename(pred_file))
+        shutil.copy(pred_file, dest_file)
+        print("Copied {} to {}".format(pred_file, dest_file))
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python script.py <model_name>")
+    else:
+        model_name = sys.argv[1]
+        main(model_name)
diff --git a/scripts/v1_5/eval/docvqa.sh b/scripts/v1_5/eval/docvqa.sh
new file mode 100755
index 00000000..28c7aa1d
--- /dev/null
+++ b/scripts/v1_5/eval/docvqa.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.evaluate_vqa \
+        --model-path $MODEL_PATH \
+        --image-folder ./playground/data/eval/docvqa \
+        --dataset docvqa_test \
+        --answers-file ./playground/data/eval/docvqa/answers/$CKPT/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE    
+done
+wait
\ No newline at end of file
diff --git a/scripts/v1_5/eval/eval_all.sh b/scripts/v1_5/eval/eval_all.sh
index 4cc3310a..7509d489 100755
--- a/scripts/v1_5/eval/eval_all.sh
+++ b/scripts/v1_5/eval/eval_all.sh
@@ -3,20 +3,24 @@
 # Checkpoint path and model name (replace with your actual values)
 checkpoint_path=$1
 model_name=$2
+conv_mode=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    conv_mode="$3"
+fi
 
 # Create output directory if it doesn't exist
 mkdir -p eval_output/$model_name
 
 # Execute the scripts in sequential order, redirecting output
-./scripts/v1_5/eval/vqav2.sh $checkpoint_path $model_name > eval_output/$model_name/vqav2.txt &&
-./scripts/v1_5/eval/gqa.sh $checkpoint_path $model_name > eval_output/$model_name/gqa.txt &&
-./scripts/v1_5/eval/vizwiz.sh $checkpoint_path $model_name > eval_output/$model_name/vizwiz.txt &&
-./scripts/v1_5/eval/sqa.sh $checkpoint_path $model_name > eval_output/$model_name/scienceqa.txt &&
-./scripts/v1_5/eval/textvqa.sh $checkpoint_path $model_name > eval_output/$model_name/textvqa.txt &&
-./scripts/v1_5/eval/pope.sh $checkpoint_path $model_name > eval_output/$model_name/pope.txt &&
-./scripts/v1_5/eval/mme.sh $checkpoint_path $model_name > eval_output/$model_name/mme.txt &&
-./scripts/v1_5/eval/mmbench.sh $checkpoint_path $model_name > eval_output/$model_name/mmbench.txt &&
-./scripts/v1_5/eval/mmbench_cn.sh $checkpoint_path $model_name > eval_output/$model_name/mmbench_cn.txt &&
-./scripts/v1_5/eval/seed.sh $checkpoint_path $model_name > eval_output/$model_name/seed.txt &&
-./scripts/v1_5/eval/mmmu.sh $checkpoint_path $model_name > eval_output/$model_name/mmmu.txt 
+./scripts/v1_5/eval/vqav2.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/vqav2.txt &&
+./scripts/v1_5/eval/gqa.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/gqa.txt &&
+./scripts/v1_5/eval/vizwiz.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/vizwiz.txt &&
+./scripts/v1_5/eval/sqa.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/scienceqa.txt &&
+./scripts/v1_5/eval/textvqa.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/textvqa.txt &&
+./scripts/v1_5/eval/pope.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/pope.txt &&
+./scripts/v1_5/eval/mme.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/mme.txt &&
+./scripts/v1_5/eval/mmbench.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/mmbench.txt &&
+./scripts/v1_5/eval/mmbench_cn.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/mmbench_cn.txt &&
+./scripts/v1_5/eval/seed.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/seed.txt &&
+./scripts/v1_5/eval/mmmu.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/mmmu.txt 
 
diff --git a/scripts/v1_5/eval/gqa.sh b/scripts/v1_5/eval/gqa.sh
index 91566ee6..bb902e48 100755
--- a/scripts/v1_5/eval/gqa.sh
+++ b/scripts/v1_5/eval/gqa.sh
@@ -10,35 +10,37 @@ SPLIT="llava_gqa_testdev_balanced"
 GQADIR="./playground/data/eval/gqa"
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-mkdir ./playground/data/eval/gqa/answers/$SPLIT
-mkdir ./playground/data/eval/gqa/answers/$SPLIT/$CKPT
+mkdir ./eval_output/$CKPT/gqa
 
 for IDX in $(seq 0 $((CHUNKS-1))); do
     CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.model_vqa_loader \
         --model-path $MODEL_PATH \
         --question-file ./playground/data/eval/gqa/$SPLIT.jsonl \
         --image-folder ./playground/data/eval/gqa/images \
-        --answers-file ./playground/data/eval/gqa/answers/$SPLIT/$CKPT/${CHUNKS}_${IDX}.jsonl \
+        --answers-file ./eval_output/$CKPT/gqa/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl \
         --num-chunks $CHUNKS \
         --chunk-idx $IDX \
         --temperature 0 \
-        --conv-mode vicuna_v1 &
+        --conv-mode $CONV_MODE &
 done
 
 wait
 
-output_file=./playground/data/eval/gqa/answers/$SPLIT/$CKPT/merge.jsonl
+output_file=./eval_output/$CKPT/gqa/$SPLIT/answers/merge.jsonl
 
 # Clear out the output file if it exists.
 > "$output_file"
 
 # Loop through the indices and concatenate each file.
 for IDX in $(seq 0 $((CHUNKS-1))); do
-    cat ./playground/data/eval/gqa/answers/$SPLIT/$CKPT/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+    cat ./eval_output/$CKPT/gqa/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
 done
 
-python scripts/convert_gqa_for_eval.py --src $output_file --dst $GQADIR/testdev_balanced_predictions.json
+python scripts/convert_gqa_for_eval.py --src $output_file --dst ./eval_output/$CKPT/gqa/$SPLIT/testdev_balanced_predictions.json
 
-cd $GQADIR
-python eval.py --tier testdev_balanced
+python $GQADIR/eval.py --tier $GQADIR/testdev_balanced --predictions ./eval_output/$CKPT/gqa/$SPLIT/testdev_balanced_predictions.json
diff --git a/scripts/v1_5/eval/gqa_sharded.sh b/scripts/v1_5/eval/gqa_sharded.sh
new file mode 100755
index 00000000..33d4ebea
--- /dev/null
+++ b/scripts/v1_5/eval/gqa_sharded.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+SPLIT="llava_gqa_testdev_balanced"
+GQADIR="./playground/data/eval/gqa"
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+mkdir ./eval_output/$CKPT/gqa
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/gqa/$SPLIT.jsonl \
+        --image-folder ./playground/data/eval/gqa/images \
+        --answers-file ./eval_output/$CKPT/gqa/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/gqa/$SPLIT/answers/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/gqa/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python scripts/convert_gqa_for_eval.py --src $output_file --dst ./eval_output/$CKPT/gqa/$SPLIT/testdev_balanced_predictions.json
+
+python $GQADIR/eval.py --tier $GQADIR/testdev_balanced --predictions ./eval_output/$CKPT/gqa/$SPLIT/testdev_balanced_predictions.json
\ No newline at end of file
diff --git a/scripts/v1_5/eval/llavabench.sh b/scripts/v1_5/eval/llavabench.sh
index 1ce804a0..3607564e 100755
--- a/scripts/v1_5/eval/llavabench.sh
+++ b/scripts/v1_5/eval/llavabench.sh
@@ -1,14 +1,20 @@
 #!/bin/bash
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa \
+echo "$MODEL_PATH $CKPT"
+
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/llava-bench-in-the-wild/questions.jsonl \
     --image-folder ./playground/data/eval/llava-bench-in-the-wild/images \
-    --answers-file ./playground/data/eval/llava-bench-in-the-wild/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/llava-bench-in-the-wild/answers.jsonl \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
 mkdir -p playground/data/eval/llava-bench-in-the-wild/reviews
 
@@ -18,8 +24,8 @@ python llava/eval/eval_gpt_review_bench.py \
     --rule llava/eval/table/rule.json \
     --answer-list \
         playground/data/eval/llava-bench-in-the-wild/answers_gpt4.jsonl \
-        playground/data/eval/llava-bench-in-the-wild/answers/$CKPT.jsonl \
+        ./eval_output/$CKPT/llava-bench-in-the-wild/answers.jsonl \
     --output \
-        playground/data/eval/llava-bench-in-the-wild/reviews/$CKPT.jsonl
+        ./eval_output/$CKPT/llava-bench-in-the-wild/reviews.jsonl
 
-python llava/eval/summarize_gpt_review.py -f playground/data/eval/llava-bench-in-the-wild/reviews/$CKPT.jsonl
+python llava/eval/summarize_gpt_review.py -f ./eval_output/$CKPT/llava-bench-in-the-wild/reviews.jsonl
diff --git a/scripts/v1_5/eval/llavabench_sharded.sh b/scripts/v1_5/eval/llavabench_sharded.sh
new file mode 100755
index 00000000..67476daf
--- /dev/null
+++ b/scripts/v1_5/eval/llavabench_sharded.sh
@@ -0,0 +1,55 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/llava-bench-in-the-wild/questions.jsonl \
+        --image-folder ./playground/data/eval/llava-bench-in-the-wild/images \
+        --answers-file ./eval_output/$CKPT/llava-bench-in-the-wild/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/llava-bench-in-the-wild/answers.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/llava-bench-in-the-wild/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+
+mkdir -p playground/data/eval/llava-bench-in-the-wild/reviews
+
+python llava/eval/eval_gpt_review_bench.py \
+    --question playground/data/eval/llava-bench-in-the-wild/questions.jsonl \
+    --context playground/data/eval/llava-bench-in-the-wild/context.jsonl \
+    --rule llava/eval/table/rule.json \
+    --answer-list \
+        playground/data/eval/llava-bench-in-the-wild/answers_gpt4.jsonl \
+        ./eval_output/$CKPT/llava-bench-in-the-wild/answers.jsonl \
+    --output \
+        ./eval_output/$CKPT/llava-bench-in-the-wild/reviews.jsonl
+
+python llava/eval/summarize_gpt_review.py -f ./eval_output/$CKPT/llava-bench-in-the-wild/reviews.jsonl
\ No newline at end of file
diff --git a/scripts/v1_5/eval/mmbench.sh b/scripts/v1_5/eval/mmbench.sh
index 3aaebecd..9aec4d02 100755
--- a/scripts/v1_5/eval/mmbench.sh
+++ b/scripts/v1_5/eval/mmbench.sh
@@ -3,19 +3,23 @@
 SPLIT="mmbench_dev_20230712"
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
 python -m llava.eval.model_vqa_mmbench \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/mmbench/$SPLIT.tsv \
-    --answers-file ./playground/data/eval/mmbench/answers/$SPLIT/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/mmbench/$SPLIT.jsonl \
     --single-pred-prompt \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
 mkdir -p playground/data/eval/mmbench/answers_upload/$SPLIT
 
 python scripts/convert_mmbench_for_submission.py \
     --annotation-file ./playground/data/eval/mmbench/$SPLIT.tsv \
-    --result-dir ./playground/data/eval/mmbench/answers/$SPLIT \
-    --upload-dir ./playground/data/eval/mmbench/answers_upload/$SPLIT \
-    --experiment $CKPT
+    --result-dir ./eval_output/$CKPT/mmbench \
+    --upload-dir ./eval_output/$CKPT/mmbench \
+    --experiment $SPLIT
diff --git a/scripts/v1_5/eval/mmbench_cn.sh b/scripts/v1_5/eval/mmbench_cn.sh
index b2334a5f..45107d19 100755
--- a/scripts/v1_5/eval/mmbench_cn.sh
+++ b/scripts/v1_5/eval/mmbench_cn.sh
@@ -3,20 +3,22 @@
 SPLIT="mmbench_dev_cn_20231003"
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
 python -m llava.eval.model_vqa_mmbench \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/mmbench_cn/$SPLIT.tsv \
-    --answers-file ./playground/data/eval/mmbench_cn/answers/$SPLIT/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/mmbench_cn/$SPLIT.jsonl \
     --lang cn \
     --single-pred-prompt \
     --temperature 0 \
-    --conv-mode vicuna_v1
-
-mkdir -p playground/data/eval/mmbench_cn/answers_upload/$SPLIT
+    --conv-mode $CONV_MODE
 
 python scripts/convert_mmbench_for_submission.py \
     --annotation-file ./playground/data/eval/mmbench_cn/$SPLIT.tsv \
-    --result-dir ./playground/data/eval/mmbench_cn/answers/$SPLIT \
-    --upload-dir ./playground/data/eval/mmbench_cn/answers_upload/$SPLIT \
-    --experiment $CKPT
+    --result-dir ./eval_output/$CKPT/mmbench_cn \
+    --upload-dir ./eval_output/$CKPT/mmbench_cn \
+    --experiment $SPLIT
diff --git a/scripts/v1_5/eval/mmbench_cn_sharded.sh b/scripts/v1_5/eval/mmbench_cn_sharded.sh
new file mode 100755
index 00000000..88450236
--- /dev/null
+++ b/scripts/v1_5/eval/mmbench_cn_sharded.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+SPLIT="mmbench_dev_cn_20231003"
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_mmbench \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/mmbench_cn/$SPLIT.tsv \
+        --answers-file ./eval_output/$CKPT/mmbench_cn/${CHUNKS}_${IDX}.jsonl \
+        --lang cn \
+        --single-pred-prompt \
+        --temperature 0 \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE  &
+done
+
+wait
+
+
+output_file=./eval_output/$CKPT/mmbench_cn/$SPLIT.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/mmbench_cn/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python scripts/convert_mmbench_for_submission.py \
+    --annotation-file ./playground/data/eval/mmbench_cn/$SPLIT.tsv \
+    --result-dir ./eval_output/$CKPT/mmbench_cn \
+    --upload-dir ./eval_output/$CKPT/mmbench_cn \
+    --experiment $SPLIT
diff --git a/scripts/v1_5/eval/mmbench_sharded.sh b/scripts/v1_5/eval/mmbench_sharded.sh
new file mode 100755
index 00000000..3002f7a5
--- /dev/null
+++ b/scripts/v1_5/eval/mmbench_sharded.sh
@@ -0,0 +1,53 @@
+#!/bin/bash
+
+SPLIT="mmbench_dev_20230712"
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_mmbench \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/mmbench/$SPLIT.tsv \
+        --answers-file ./eval_output/$CKPT/mmbench/${CHUNKS}_${IDX}.jsonl \
+        --single-pred-prompt \
+        --temperature 0 \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE  &
+done
+
+wait
+
+mkdir -p playground/data/eval/mmbench/answers_upload/$SPLIT
+
+
+output_file=./eval_output/$CKPT/mmbench/$SPLIT.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/mmbench/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+
+python scripts/convert_mmbench_for_submission.py \
+    --annotation-file ./playground/data/eval/mmbench/$SPLIT.tsv \
+    --result-dir ./eval_output/$CKPT/mmbench \
+    --upload-dir ./eval_output/$CKPT/mmbench \
+    --experiment $SPLIT
diff --git a/scripts/v1_5/eval/mme.sh b/scripts/v1_5/eval/mme.sh
index 9a1a79e5..d61d8e80 100755
--- a/scripts/v1_5/eval/mme.sh
+++ b/scripts/v1_5/eval/mme.sh
@@ -1,22 +1,23 @@
 #!/bin/bash
 MODEL_PATH=$1
 CKPT=$2
+MMEDIR="./playground/data/eval/MME"
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
 mkdir -p ./playground/data/eval/MME/answers/$CKPT
 
-python -m llava.eval.model_vqa_loader \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_loader \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/MME/llava_mme.jsonl \
     --image-folder ./playground/data/eval/MME/MME_Benchmark_release_version \
-    --answers-file ./playground/data/eval/MME/answers/$CKPT/mme.jsonl \
+    --answers-file ./eval_output/$CKPT/MME/mme.jsonl \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
-cd ./playground/data/eval/MME
+python $MMEDIR/convert_answer_to_mme.py --experiment ./eval_output/$CKPT/MME/mme.jsonl
 
-python convert_answer_to_mme.py --experiment answers/$CKPT/mme.jsonl
-
-cd eval_tool
-
-python calculation.py --results_dir ../answers/$CKPT/mme_results
+python $MMEDIR/eval_tool/calculation.py --results_dir ./eval_output/$CKPT/MME/mme_results
 
diff --git a/scripts/v1_5/eval/mme_sharded.sh b/scripts/v1_5/eval/mme_sharded.sh
new file mode 100755
index 00000000..7e297849
--- /dev/null
+++ b/scripts/v1_5/eval/mme_sharded.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+MMEDIR="./playground/data/eval/MME"
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/MME/llava_mme.jsonl \
+        --image-folder ./playground/data/eval/MME/MME_Benchmark_release_version \
+        --answers-file ./eval_output/$CKPT/MME/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/MME/mme.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/MME/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+
+python $MMEDIR/convert_answer_to_mme.py --experiment $output_file
+
+python $MMEDIR/eval_tool/calculation.py --results_dir ./eval_output/$CKPT/MME/mme_results
\ No newline at end of file
diff --git a/scripts/v1_5/eval/mmmu.sh b/scripts/v1_5/eval/mmmu.sh
index d24c1bcc..bfc88fd8 100755
--- a/scripts/v1_5/eval/mmmu.sh
+++ b/scripts/v1_5/eval/mmmu.sh
@@ -2,14 +2,19 @@
 MODEL_PATH=$1
 CKPT=$2
 SPLIT=$3
-
-mkdir -p ./playground/data/eval/MMMU/test_results
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 4 ]; then
+    CONV_MODE="$4"
+fi
 
 CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_mmmu \
     --model_path $MODEL_PATH \
     --data_path ./playground/data/eval/MMMU \
-    --conv-mode vicuna_v1 \
+    --conv-mode $CONV_MODE \
     --config_path llava/eval/mmmu_utils/configs/llava1.5.yaml \
-    --output_path ./playground/data/eval/MMMU/test_results/$CKPT.json \
+    --output_path ./eval_output/$CKPT/MMMU/${SPLIT}_answers.json \
     --split $SPLIT
 
+if [ "$SPLIT" = "validation" ]; then
+    python llava/eval/eval_mmmu.py --output_path ./eval_output/$CKPT/MMMU/${SPLIT}_answers.json --answer_path ./playground/data/eval/MMMU/answer_dict_val.json
+fi
\ No newline at end of file
diff --git a/scripts/v1_5/eval/mmmu_sharded.sh b/scripts/v1_5/eval/mmmu_sharded.sh
new file mode 100755
index 00000000..63bfde70
--- /dev/null
+++ b/scripts/v1_5/eval/mmmu_sharded.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+# Sample usage: ./scripts/v1_5/eval/mmmu_sharded.sh checkpoints/llava-v1.5-7b-sft-vflan+sharegpt4v-sharegpt4v-pretrain-siglip llava-v1.5-7b-sft-vflan+sharegpt4v-sharegpt4v-pretrain-siglip --validation
+# The script uses two GPUs (with pipeline parallelism) for each chunk.
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+MODEL_PATH=$1
+CKPT=$2
+SPLIT=$3
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 4 ]; then
+    CONV_MODE="$4"
+fi
+# Input Validation
+if [[ "$SPLIT" != "validation" && "$SPLIT" != "test" ]]; then
+    echo "Error: SPLIT must be either 'validation' or 'test'"
+    exit 1 
+fi
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  GPU_IDX1=$((IDX * 2))  # First GPU index
+  GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+  CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_mmmu_sharded \
+    --model_path $MODEL_PATH \
+    --data_path ./playground/data/eval/MMMU \
+    --conv-mode $CONV_MODE \
+    --config_path llava/eval/mmmu_utils/configs/llava1.5.yaml \
+    --output_path ./eval_output/$CKPT/MMMU/$SPLIT.json \
+    --num-chunks $CHUNKS \
+    --chunk-idx $IDX \
+    --split $SPLIT &
+done
+
+wait
+
+python llava/eval/mmmu_utils/merge_jsons.py --prediction-path ./eval_output/$CKPT/MMMU/$SPLIT --num-chunks $CHUNKS
+
+if [ "$SPLIT" = "validation" ]; then
+  python llava/eval/eval_mmmu.py  --output_path ./eval_output/$CKPT/MMMU/$SPLIT.json --answer_path llava/eval/mmmu_utils/answer_dict_val.json
+fi
diff --git a/scripts/v1_5/eval/mmvet.sh b/scripts/v1_5/eval/mmvet.sh
index 3ccc7448..be073c53 100755
--- a/scripts/v1_5/eval/mmvet.sh
+++ b/scripts/v1_5/eval/mmvet.sh
@@ -1,19 +1,21 @@
 #!/bin/bash
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/mm-vet/llava-mm-vet.jsonl \
     --image-folder ./playground/data/eval/mm-vet/images \
-    --answers-file ./playground/data/eval/mm-vet/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/mm-vet/answers.json \
     --temperature 0 \
-    --conv-mode vicuna_v1
-
-mkdir -p ./playground/data/eval/mm-vet/results
+    --conv-mode $CONV_MODE
 
 python scripts/convert_mmvet_for_eval.py \
-    --src ./playground/data/eval/mm-vet/answers/$CKPT.jsonl \
-    --dst ./playground/data/eval/mm-vet/results/$CKPT.json
+    --src ./eval_output/$CKPT/mm-vet/answers.json \
+    --dst ./eval_output/$CKPT/mm-vet/results.json
 
-python llava/eval/eval_mmvet.py --results_file playground/data/eval/mm-vet/results/$CKPT.json
+python llava/eval/eval_mmvet.py --results_file ./eval_output/$CKPT/mm-vet/results.json
diff --git a/scripts/v1_5/eval/mmvet_sharded.sh b/scripts/v1_5/eval/mmvet_sharded.sh
new file mode 100755
index 00000000..f8becda3
--- /dev/null
+++ b/scripts/v1_5/eval/mmvet_sharded.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/mm-vet/llava-mm-vet.jsonl \
+        --image-folder ./playground/data/eval/mm-vet/images \
+        --answers-file ./eval_output/$CKPT/mm-vet/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/mm-vet/answers.json
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/mm-vet/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+mkdir -p ./playground/data/eval/mm-vet/results
+
+python scripts/convert_mmvet_for_eval.py \
+    --src ./eval_output/$CKPT/mm-vet/answers.json \
+    --dst ./eval_output/$CKPT/mm-vet/results.json
+
+python llava/eval/eval_mmvet.py --results_file ./eval_output/$CKPT/mm-vet/results.json
diff --git a/scripts/v1_5/eval/pope.sh b/scripts/v1_5/eval/pope.sh
index 1011e923..a9169333 100755
--- a/scripts/v1_5/eval/pope.sh
+++ b/scripts/v1_5/eval/pope.sh
@@ -1,16 +1,20 @@
 #!/bin/bash
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa_loader \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_loader \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/pope/llava_pope_test.jsonl \
     --image-folder ./playground/data/eval/pope/val2014 \
-    --answers-file ./playground/data/eval/pope/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/pope/answers.jsonl \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
-python llava/eval/eval_pope.py \
+CUDA_VISIBLE_DEVICES=0 python llava/eval/eval_pope.py \
     --annotation-dir ./playground/data/eval/pope/coco \
     --question-file ./playground/data/eval/pope/llava_pope_test.jsonl \
-    --result-file ./playground/data/eval/pope/answers/$CKPT.jsonl
+    --result-file ./eval_output/$CKPT/pope/answers.jsonl
diff --git a/scripts/v1_5/eval/pope_sharded.sh b/scripts/v1_5/eval/pope_sharded.sh
new file mode 100755
index 00000000..fb5e1453
--- /dev/null
+++ b/scripts/v1_5/eval/pope_sharded.sh
@@ -0,0 +1,45 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/pope/llava_pope_test.jsonl \
+        --image-folder ./playground/data/eval/pope/val2014 \
+        --answers-file ./eval_output/$CKPT/pope/${CHUNKS}_${IDX}.jsonl \
+        --temperature 0 \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/pope/answers.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/pope/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python llava/eval/eval_pope.py \
+    --annotation-dir ./playground/data/eval/pope/coco \
+    --question-file ./playground/data/eval/pope/llava_pope_test.jsonl \
+    --result-file $output_file
\ No newline at end of file
diff --git a/scripts/v1_5/eval/qbench.sh b/scripts/v1_5/eval/qbench.sh
old mode 100644
new mode 100755
index 46b8e029..7421fb36
--- a/scripts/v1_5/eval/qbench.sh
+++ b/scripts/v1_5/eval/qbench.sh
@@ -9,7 +9,7 @@ else
     exit 1
 fi
 
-python -m llava.eval.model_vqa_qbench \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_qbench \
     --model-path liuhaotian/llava-v1.5-13b \
     --image-folder ./playground/data/eval/qbench/images_llvisionqa/ \
     --questions-file ./playground/data/eval/qbench/llvisionqa_$1.json \
diff --git a/scripts/v1_5/eval/qbench_zh.sh b/scripts/v1_5/eval/qbench_zh.sh
old mode 100644
new mode 100755
diff --git a/scripts/v1_5/eval/seed.sh b/scripts/v1_5/eval/seed.sh
index 1b906f0f..0cc2f121 100755
--- a/scripts/v1_5/eval/seed.sh
+++ b/scripts/v1_5/eval/seed.sh
@@ -8,34 +8,38 @@ IFS=',' read -ra GPULIST <<< "$gpu_list"
 CHUNKS=${#GPULIST[@]}
 
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
 for IDX in $(seq 0 $((CHUNKS-1))); do
     CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.model_vqa_loader \
         --model-path $MODEL_PATH \
         --question-file ./playground/data/eval/seed_bench/llava-seed-bench.jsonl \
         --image-folder ./playground/data/eval/seed_bench \
-        --answers-file ./playground/data/eval/seed_bench/answers/$CKPT/${CHUNKS}_${IDX}.jsonl \
+        --answers-file ./eval_output/$CKPT/seed_bench/answers/${CHUNKS}_${IDX}.jsonl \
         --num-chunks $CHUNKS \
         --chunk-idx $IDX \
         --temperature 0 \
-        --conv-mode vicuna_v1 &
+        --conv-mode $CONV_MODE &
 done
 
 wait
 
-output_file=./playground/data/eval/seed_bench/answers/$CKPT/merge.jsonl
+output_file=./eval_output/$CKPT/seed_bench/answers/merge.jsonl
 
 # Clear out the output file if it exists.
 > "$output_file"
 
 # Loop through the indices and concatenate each file.
 for IDX in $(seq 0 $((CHUNKS-1))); do
-    cat ./playground/data/eval/seed_bench/answers/$CKPT/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+    cat ./eval_output/$CKPT/seed_bench/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
 done
 
 # Evaluate
 python scripts/convert_seed_for_submission.py \
     --annotation-file ./playground/data/eval/seed_bench/SEED-Bench.json \
     --result-file $output_file \
-    --result-upload-file ./playground/data/eval/seed_bench/answers_upload/$CKPT.jsonl
+    --result-upload-file ./eval_output/$CKPT/seed_bench/answers_upload.jsonl
 
diff --git a/scripts/v1_5/eval/seed_sharded.sh b/scripts/v1_5/eval/seed_sharded.sh
new file mode 100755
index 00000000..370a8f9f
--- /dev/null
+++ b/scripts/v1_5/eval/seed_sharded.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+MODEL_PATH=$1
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/seed_bench/llava-seed-bench.jsonl \
+        --image-folder ./playground/data/eval/seed_bench \
+        --answers-file ./eval_output/$CKPT/seed_bench/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/seed_bench/answers/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/seed_bench/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+# Evaluate
+python scripts/convert_seed_for_submission.py \
+    --annotation-file ./playground/data/eval/seed_bench/SEED-Bench.json \
+    --result-file $output_file \
+    --result-upload-file ./eval_output/$CKPT/seed_bench/answers_upload.jsonl
diff --git a/scripts/v1_5/eval/sqa.sh b/scripts/v1_5/eval/sqa.sh
index 725f147c..aafe4423 100755
--- a/scripts/v1_5/eval/sqa.sh
+++ b/scripts/v1_5/eval/sqa.sh
@@ -39,21 +39,24 @@
 #     --output-result ./playground/data/eval/scienceqa/answers/$CKPT_result.json
 
 
-
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa_science \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_science \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/scienceqa/llava_test_CQM-A.json \
     --image-folder ./playground/data/eval/scienceqa/images/test \
-    --answers-file ./playground/data/eval/scienceqa/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/scienceqa/answers.jsonl \
     --single-pred-prompt \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
-python llava/eval/eval_science_qa.py \
+CUDA_VISIBLE_DEVICES=0 python llava/eval/eval_science_qa.py \
     --base-dir ./playground/data/eval/scienceqa \
-    --result-file ./playground/data/eval/scienceqa/answers/$CKPT.jsonl \
-    --output-file ./playground/data/eval/scienceqa/answers/$CKPT_output.jsonl \
-    --output-result ./playground/data/eval/scienceqa/answers/$CKPT_result.json
+    --result-file ./eval_output/$CKPT/scienceqa/answers.jsonl \
+    --output-file ./eval_output/$CKPT/scienceqa/outputs.jsonl \
+    --output-result ./eval_output/$CKPT/scienceqa/results.json
diff --git a/scripts/v1_5/eval/sqa_sharded.sh b/scripts/v1_5/eval/sqa_sharded.sh
new file mode 100755
index 00000000..fb5f7429
--- /dev/null
+++ b/scripts/v1_5/eval/sqa_sharded.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+# CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+# gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+# IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+# CHUNKS=${#GPULIST[@]}
+
+# MODEL_PATH=$1
+# CKPT=$2
+
+
+# for IDX in $(seq 0 $((CHUNKS-1))); do
+#     CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.model_vqa_science \
+#         --model-path $MODEL_PATH \
+#         --question-file ./playground/data/eval/scienceqa/llava_test_CQM-A.json \
+#         --image-folder ./playground/data/eval/scienceqa/images/test \
+#         --answers-file ./playground/data/eval/scienceqa/answers/${CHUNKS}_${IDX}.jsonl \
+#         --num-chunks $CHUNKS \
+#         --chunk-idx $IDX \
+#         --temperature 0 \
+#         --conv-mode vicuna_v1 &
+# done
+# wait
+
+# output_file=./playground/data/eval/scienceqa/answers/$CKPT.jsonl
+
+# # Clear out the output file if it exists.
+# > "$output_file"
+
+# # Loop through the indices and concatenate each file.
+# for IDX in $(seq 0 $((CHUNKS-1))); do
+#     cat ./playground/data/eval/scienceqa/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+# done
+
+# python llava/eval/eval_science_qa.py \
+#     --base-dir ./playground/data/eval/scienceqa \
+#     --result-file ./playground/data/eval/scienceqa/answers/$CKPT.jsonl \
+#     --output-file ./playground/data/eval/scienceqa/answers/$CKPT_output.jsonl \
+#     --output-result ./playground/data/eval/scienceqa/answers/$CKPT_result.json
+
+
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_science \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/scienceqa/llava_test_CQM-A.json \
+        --image-folder ./playground/data/eval/scienceqa/images/test \
+        --answers-file ./eval_output/$CKPT/scienceqa/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --single-pred-prompt \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/scienceqa/answers.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/scienceqa/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python llava/eval/eval_science_qa.py \
+    --base-dir ./playground/data/eval/scienceqa \
+    --result-file ./eval_output/$CKPT/scienceqa/answers.jsonl \
+    --output-file ./eval_output/$CKPT/scienceqa/outputs.jsonl \
+    --output-result ./eval_output/$CKPT/scienceqa/results.json
\ No newline at end of file
diff --git a/scripts/v1_5/eval/textvqa.sh b/scripts/v1_5/eval/textvqa.sh
index acaf956e..b192536d 100755
--- a/scripts/v1_5/eval/textvqa.sh
+++ b/scripts/v1_5/eval/textvqa.sh
@@ -1,15 +1,19 @@
 #!/bin/bash
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa_loader \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_loader \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/textvqa/llava_textvqa_val_v051_ocr.jsonl \
     --image-folder ./playground/data/eval/textvqa/train_images \
-    --answers-file ./playground/data/eval/textvqa/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/textvqa/answers.jsonl \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
-python -m llava.eval.eval_textvqa \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.eval_textvqa \
     --annotation-file ./playground/data/eval/textvqa/TextVQA_0.5.1_val.json \
-    --result-file ./playground/data/eval/textvqa/answers/$CKPT.jsonl
+    --result-file ./eval_output/$CKPT/textvqa/answers.jsonl
diff --git a/scripts/v1_5/eval/textvqa_sharded.sh b/scripts/v1_5/eval/textvqa_sharded.sh
new file mode 100755
index 00000000..be0b61fe
--- /dev/null
+++ b/scripts/v1_5/eval/textvqa_sharded.sh
@@ -0,0 +1,46 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+    --model-path $MODEL_PATH \
+    --question-file ./playground/data/eval/textvqa/llava_textvqa_val_v051_ocr.jsonl \
+    --image-folder ./playground/data/eval/textvqa/train_images \
+    --answers-file ./eval_output/$CKPT/textvqa/answers/${CHUNKS}_${IDX}.jsonl \
+    --num-chunks $CHUNKS \
+    --chunk-idx $IDX \
+    --temperature 0 \
+    --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/textvqa/answers/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/textvqa/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+
+python -m llava.eval.eval_textvqa \
+    --annotation-file ./playground/data/eval/textvqa/TextVQA_0.5.1_val.json \
+    --result-file $output_file
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_all.sh b/scripts/v1_5/eval/video_chatgpt/eval_all.sh
new file mode 100755
index 00000000..299a6d7b
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_all.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+source ~/.bashrc
+conda activate multi-modal
+which python
+
+cd ~/workspace/multi-modality-research/VILA
+
+model_name=$1
+
+echo activity_net
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_activitynet.sh ${model_name}
+echo msvd
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_msvd.sh ${model_name}
+echo msrvtt
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_msrvtt.sh ${model_name}
+echo tgif
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_tgif.sh ${model_name}
+echo nextqa
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_nextqa.sh ${model_name}
+echo perception_test
+bash ./scripts/v1_5/eval/video_chatgpt/eval_qa_perception.sh ${model_name}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_benchmark_1_correctness.sh b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_1_correctness.sh
new file mode 100755
index 00000000..6e0432ba
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_1_correctness.sh
@@ -0,0 +1,17 @@
+
+CKPT_NAME="checkpoints/Video-LLaVA-7B"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+pred_path="${Video_5_Benchmark}/${CKPT_NAME}/correctness_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}/gpt3/correctness"
+output_json="${Video_5_Benchmark}/${CKPT_NAME}/results/correctness_qa.json"
+api_key=""
+api_base=""
+num_tasks=8
+
+python3 llava/eval/video/eval_benchmark_1_correctness.py \
+    --pred_path  ${pred_path} \
+    --output_dir  ${output_dir} \
+    --output_json  ${output_json} \
+    --api_key ${api_key} \
+    --api_base ${api_base} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_benchmark_2_detail.sh b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_2_detail.sh
new file mode 100755
index 00000000..b59f71be
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_2_detail.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="checkpoints/Video-LLaVA-7B"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+pred_path="${Video_5_Benchmark}/${CKPT_NAME}/detail_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}/gpt3/detail"
+output_json="${Video_5_Benchmark}/${CKPT_NAME}/results/detail_qa.json"
+api_key=""
+api_base=""
+num_tasks=8
+
+python3 llava/eval/video/eval_benchmark_2_detailed_orientation.py \
+    --pred_path  ${pred_path} \
+    --output_dir  ${output_dir} \
+    --output_json  ${output_json} \
+    --api_key ${api_key} \
+    --api_base ${api_base} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_benchmark_3_contextual.sh b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_3_contextual.sh
new file mode 100755
index 00000000..33996835
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_3_contextual.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="checkpoints/Video-LLaVA-7B"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+pred_path="${Video_5_Benchmark}/${CKPT_NAME}/contextual_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}/gpt3/contextual"
+output_json="${Video_5_Benchmark}/${CKPT_NAME}/results/contextual_qa.json"
+api_key=""
+api_base=""
+num_tasks=8
+
+python3 llava/eval/video/eval_benchmark_3_context.py \
+    --pred_path  ${pred_path} \
+    --output_dir  ${output_dir} \
+    --output_json  ${output_json} \
+    --api_key ${api_key} \
+    --api_base ${api_base} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_benchmark_4_temporal.sh b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_4_temporal.sh
new file mode 100755
index 00000000..9c3430ae
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_4_temporal.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="checkpoints/Video-LLaVA-7B"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+pred_path="${Video_5_Benchmark}/${CKPT_NAME}/temporal_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}/gpt3/temporal"
+output_json="${Video_5_Benchmark}/${CKPT_NAME}/results/temporal_qa.json"
+api_key=""
+api_base=""
+num_tasks=8
+
+python3 llava/eval/video/eval_benchmark_4_temporal.py \
+    --pred_path  ${pred_path} \
+    --output_dir  ${output_dir} \
+    --output_json  ${output_json} \
+    --api_key ${api_key} \
+    --api_base ${api_base} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_benchmark_5_consistency.sh b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_5_consistency.sh
new file mode 100755
index 00000000..ca3389d4
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_benchmark_5_consistency.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="checkpoints/Video-LLaVA-7B"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+pred_path="${Video_5_Benchmark}/${CKPT_NAME}/consistency_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}/gpt3/consistency"
+output_json="${Video_5_Benchmark}/${CKPT_NAME}/results/consistency_qa.json"
+api_key=""
+api_base=""
+num_tasks=8
+
+python3 llava/eval/video/eval_benchmark_5_consistency.py \
+    --pred_path  ${pred_path} \
+    --output_dir  ${output_dir} \
+    --output_json  ${output_json} \
+    --api_key ${api_key} \
+    --api_base ${api_base} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_activitynet.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_activitynet.sh
new file mode 100755
index 00000000..24bac702
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_activitynet.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/Activitynet_Zero_Shot_QA/merge.jsonl"
+output_dir="${GPT_Zero_Shot_QA}/${output_name}/Activitynet_Zero_Shot_QA/gpt4-turbo"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/Activitynet_Zero_Shot_QA/results.json"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_qa.py \
+    --pred_path ${pred_path} \
+    --output_dir ${output_dir} \
+    --output_json ${output_json} \
+    --num_tasks ${num_tasks}
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_msrvtt.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_msrvtt.sh
new file mode 100755
index 00000000..0d2e9d6e
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_msrvtt.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/MSRVTT_Zero_Shot_QA/merge.jsonl"
+output_dir="${GPT_Zero_Shot_QA}/${output_name}/MSRVTT_Zero_Shot_QA/gpt"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/MSRVTT_Zero_Shot_QA/results.json"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_qa.py \
+    --pred_path ${pred_path} \
+    --output_dir ${output_dir} \
+    --output_json ${output_json} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_msvd.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_msvd.sh
new file mode 100755
index 00000000..8bbbe61c
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_msvd.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/MSVD_Zero_Shot_QA/merge.jsonl"
+output_dir="${GPT_Zero_Shot_QA}/${output_name}/MSVD_Zero_Shot_QA/gpt4-turbo"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/MSVD_Zero_Shot_QA/results.json"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_qa.py \
+    --pred_path ${pred_path} \
+    --output_dir ${output_dir} \
+    --output_json ${output_json} \
+    --num_tasks ${num_tasks}
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_nextqa.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_nextqa.sh
new file mode 100755
index 00000000..10c58c98
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_nextqa.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/NextQA_Zero_Shot_QA/merge.jsonl"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/NextQA_Zero_Shot_QA/results.json"
+NEXTQA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/nextqa"
+stopwords_file="${NEXTQA}/stopwords.txt"
+gt_file="${NEXTQA}/test_data_nextoe/test.csv"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_nextqa.py \
+    --pred_path ${pred_path} \
+    --output_json ${output_json} \
+    --gt_file ${gt_file} \
+    --stopwords_file ${stopwords_file} \
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_perception.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_perception.sh
new file mode 100755
index 00000000..c2e264ea
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_perception.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/PerceptionTest_Zero_Shot_QA/merge.jsonl"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/PerceptionTest_Zero_Shot_QA/results.json"
+DATA_DIR="/lustre/fsw/portfolios/nvr/projects/nvr_aialgo_robogptagent/loragen_workspace/video_datasets_v2/perception_test"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_perception.py \
+    --pred_path ${pred_path} \
+    --output_json ${output_json} \
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/eval_qa_tgif.sh b/scripts/v1_5/eval/video_chatgpt/eval_qa_tgif.sh
new file mode 100755
index 00000000..5fc91112
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/eval_qa_tgif.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+GPT_Zero_Shot_QA="./eval_output"
+output_name=$1
+pred_path="${GPT_Zero_Shot_QA}/${output_name}/TGIF_Zero_Shot_QA/merge.jsonl"
+output_dir="${GPT_Zero_Shot_QA}/${output_name}/TGIF_Zero_Shot_QA/gpt3.5-0.0"
+output_json="${GPT_Zero_Shot_QA}/${output_name}/TGIF_Zero_Shot_QA/results.json"
+num_tasks=8
+
+python3 llava/eval/video/eval_video_qa.py \
+    --pred_path ${pred_path} \
+    --output_dir ${output_dir} \
+    --output_json ${output_json} \
+    --num_tasks ${num_tasks}
diff --git a/scripts/v1_5/eval/video_chatgpt/run_all.sh b/scripts/v1_5/eval/video_chatgpt/run_all.sh
new file mode 100644
index 00000000..98150565
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_all.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# Checkpoint path and model name (replace with your actual values)
+checkpoint_path=$1
+model_name=$2
+conv_mode=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    conv_mode="$3"
+fi
+
+# Create output directory if it doesn't exist
+mkdir -p eval_output/$model_name
+
+./scripts/v1_5/eval/video_chatgpt/run_qa_activitynet.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/activitynet.txt &&
+./scripts/v1_5/eval/video_chatgpt/run_qa_msvd.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/msvd.txt &&
+./scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/msrvtt.txt &&
+./scripts/v1_5/eval/video_chatgpt/run_qa_tgif.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/tgif.txt &&
+./scripts/v1_5/eval/video_chatgpt/run_qa_perception.sh $checkpoint_path $model_name $conv_mode > eval_output/$model_name/perception.txt &&
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_benchmark_1_correctness.sh b/scripts/v1_5/eval/video_chatgpt/run_benchmark_1_correctness.sh
new file mode 100755
index 00000000..ceaa3128
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_benchmark_1_correctness.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="Video-LLaVA-7B"
+model_path="checkpoints/${CKPT_NAME}"
+cache_dir="./cache_dir"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+video_dir="${Video_5_Benchmark}/Test_Videos"
+gt_file="${Video_5_Benchmark}/Benchmarking_QA/generic_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}"
+output_name="correctness_qa"
+
+python3 llava/eval/video/run_inference_benchmark_general.py \
+    --model_path ${model_path} \
+    --cache_dir ${cache_dir} \
+    --video_dir ${video_dir} \
+    --gt_file ${gt_file} \
+    --output_dir ${output_dir} \
+    --output_name ${output_name}
diff --git a/scripts/v1_5/eval/video_chatgpt/run_benchmark_2_detail.sh b/scripts/v1_5/eval/video_chatgpt/run_benchmark_2_detail.sh
new file mode 100755
index 00000000..1cea248e
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_benchmark_2_detail.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="Video-LLaVA-7B"
+model_path="checkpoints/${CKPT_NAME}"
+cache_dir="./cache_dir"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+video_dir="${Video_5_Benchmark}/Test_Videos"
+gt_file="${Video_5_Benchmark}/Benchmarking_QA/generic_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}"
+output_name="detail_qa"
+
+python3 llava/eval/video/run_inference_benchmark_general.py \
+    --model_path ${model_path} \
+    --cache_dir ${cache_dir} \
+    --video_dir ${video_dir} \
+    --gt_file ${gt_file} \
+    --output_dir ${output_dir} \
+    --output_name ${output_name}
diff --git a/scripts/v1_5/eval/video_chatgpt/run_benchmark_3_contextual.sh b/scripts/v1_5/eval/video_chatgpt/run_benchmark_3_contextual.sh
new file mode 100755
index 00000000..dd568392
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_benchmark_3_contextual.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="Video-LLaVA-7B"
+model_path="checkpoints/${CKPT_NAME}"
+cache_dir="./cache_dir"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+video_dir="${Video_5_Benchmark}/Test_Videos"
+gt_file="${Video_5_Benchmark}/Benchmarking_QA/generic_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}"
+output_name="contextual_qa"
+
+python3 llava/eval/video/run_inference_benchmark_general.py \
+    --model_path ${model_path} \
+    --cache_dir ${cache_dir} \
+    --video_dir ${video_dir} \
+    --gt_file ${gt_file} \
+    --output_dir ${output_dir} \
+    --output_name ${output_name}
diff --git a/scripts/v1_5/eval/video_chatgpt/run_benchmark_4_temporal.sh b/scripts/v1_5/eval/video_chatgpt/run_benchmark_4_temporal.sh
new file mode 100755
index 00000000..1c5f44a7
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_benchmark_4_temporal.sh
@@ -0,0 +1,17 @@
+
+CKPT_NAME="Video-LLaVA-7B"
+model_path="checkpoints/${CKPT_NAME}"
+cache_dir="./cache_dir"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+video_dir="${Video_5_Benchmark}/Test_Videos"
+gt_file="${Video_5_Benchmark}/Benchmarking_QA/temporal_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}"
+output_name="temporal_qa"
+
+python3 llava/eval/video/run_inference_benchmark_general.py \
+    --model_path ${model_path} \
+    --cache_dir ${cache_dir} \
+    --video_dir ${video_dir} \
+    --gt_file ${gt_file} \
+    --output_dir ${output_dir} \
+    --output_name ${output_name}
diff --git a/scripts/v1_5/eval/video_chatgpt/run_benchmark_5_consistency.sh b/scripts/v1_5/eval/video_chatgpt/run_benchmark_5_consistency.sh
new file mode 100755
index 00000000..624139a6
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_benchmark_5_consistency.sh
@@ -0,0 +1,18 @@
+
+
+CKPT_NAME="Video-LLaVA-7B"
+model_path="checkpoints/${CKPT_NAME}"
+cache_dir="./cache_dir"
+Video_5_Benchmark="eval/Video_5_Benchmark"
+video_dir="${Video_5_Benchmark}/Test_Videos"
+gt_file="${Video_5_Benchmark}/Benchmarking_QA/consistency_qa.json"
+output_dir="${Video_5_Benchmark}/${CKPT_NAME}"
+output_name="consistency_qa"
+
+python3 llava/eval/video/run_inference_benchmark_consistency.py \
+    --model_path ${model_path} \
+    --cache_dir ${cache_dir} \
+    --video_dir ${video_dir} \
+    --gt_file ${gt_file} \
+    --output_dir ${output_dir} \
+    --output_name ${output_name}
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet.sh
new file mode 100755
index 00000000..dc7d31c1
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/all_test"
+gt_file_question="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/Activitynet_Zero_Shot_QA"
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet_sharded.sh
new file mode 100755
index 00000000..95eafb44
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_activitynet_sharded.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/all_test"
+gt_file_question="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/Activitynet_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/Activitynet_Zero_Shot_QA"
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+> "$output_file"
+
+CHUNKS=4
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> $output_file
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt.sh
new file mode 100755
index 00000000..6bf22c36
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/videos/all"
+gt_file_question="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/MSRVTT_Zero_Shot_QA"
+
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_$((IDX)) \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt_sharded.sh
new file mode 100755
index 00000000..46d74cd5
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_msrvtt_sharded.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/videos/all"
+gt_file_question="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/MSRVTT_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/MSRVTT_Zero_Shot_QA"
+
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 4096 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_$((IDX)) \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_msvd.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_msvd.sh
new file mode 100755
index 00000000..0bbbc075
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_msvd.sh
@@ -0,0 +1,49 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/videos"
+gt_file_question="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/MSVD_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_msvd_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_msvd_sharded.sh
new file mode 100755
index 00000000..d808d874
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_msvd_sharded.sh
@@ -0,0 +1,52 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/videos"
+gt_file_question="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/MSVD_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/MSVD_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 4096 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa.sh
new file mode 100755
index 00000000..d4724573
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+NEXTQA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/nextqa"
+video_dir="${NEXTQA}/NExTVideo"
+gt_file="${NEXTQA}/test_data_nextoe/test.csv"
+output_dir="./eval_output/${CKPT_NAME}/NextQA_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_nextqa.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file ${gt_file} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa_sharded.sh
new file mode 100755
index 00000000..bc28bc18
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_nextqa_sharded.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+NEXTQA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/nextqa"
+video_dir="${NEXTQA}/NExTVideo"
+gt_file="${NEXTQA}/test_data_nextoe/test.csv"
+output_dir="./eval_output/${CKPT_NAME}/NextQA_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_nextqa.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 4096 \
+      --gt_file ${gt_file} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_perception.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_perception.sh
new file mode 100755
index 00000000..90777e76
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_perception.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+DATA_DIR="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/perception_test/"
+video_dir="${DATA_DIR}/videos"
+gt_file="${DATA_DIR}/mc_question_valid.json"
+output_dir="./eval_output/${CKPT_NAME}/PerceptionTest_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_videoperception.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file ${gt_file} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_perception_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_perception_sharded.sh
new file mode 100755
index 00000000..a1c87346
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_perception_sharded.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+DATA_DIR="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/perception_test/"
+video_dir="${DATA_DIR}/videos"
+gt_file="${DATA_DIR}/mc_question_valid.json"
+output_dir="./eval_output/${CKPT_NAME}/PerceptionTest_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_videoperception.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 4096 \
+      --gt_file ${gt_file} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+if [ -f "$output_file" ]; then
+    > "$output_file"
+fi
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_tgif.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_tgif.sh
new file mode 100755
index 00000000..62c0cecd
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_tgif.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/mp4"
+gt_file_question="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/TGIF_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+  CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 8192 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/video_chatgpt/run_qa_tgif_sharded.sh b/scripts/v1_5/eval/video_chatgpt/run_qa_tgif_sharded.sh
new file mode 100755
index 00000000..a3879a3b
--- /dev/null
+++ b/scripts/v1_5/eval/video_chatgpt/run_qa_tgif_sharded.sh
@@ -0,0 +1,50 @@
+#!/bin/bash
+
+model_path=$1
+CKPT_NAME=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+GPT_Zero_Shot_QA="/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/dataset/video_datasets_v2/GPT_Zero_Shot_QA"
+video_dir="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/mp4"
+gt_file_question="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/test_q.json"
+gt_file_answers="${GPT_Zero_Shot_QA}/TGIF_Zero_Shot_QA/test_a.json"
+output_dir="./eval_output/${CKPT_NAME}/TGIF_Zero_Shot_QA"
+
+
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python3 llava/eval/model_vqa_video.py \
+      --model-path ${model_path} \
+      --video_dir ${video_dir} \
+      --model_max_length 4096 \
+      --gt_file_question ${gt_file_question} \
+      --gt_file_answers ${gt_file_answers} \
+      --output_dir ${output_dir} \
+      --output_name ${CHUNKS}_${IDX} \
+      --num-chunks $CHUNKS \
+      --chunk-idx $IDX \
+      --conv-mode $CONV_MODE \
+      --temperature 0 &
+done
+
+wait
+
+output_file=${output_dir}/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ${output_dir}/${CHUNKS}_${IDX}.json >> "$output_file"
+done
\ No newline at end of file
diff --git a/scripts/v1_5/eval/vizwiz.sh b/scripts/v1_5/eval/vizwiz.sh
index e88480e9..f35c85a2 100755
--- a/scripts/v1_5/eval/vizwiz.sh
+++ b/scripts/v1_5/eval/vizwiz.sh
@@ -2,19 +2,23 @@
 
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
-python -m llava.eval.model_vqa_loader \
+CUDA_VISIBLE_DEVICES=0 python -m llava.eval.model_vqa_loader \
     --model-path $MODEL_PATH \
     --question-file ./playground/data/eval/vizwiz/llava_test.jsonl \
     --image-folder ./playground/data/eval/vizwiz/test \
-    --answers-file ./playground/data/eval/vizwiz/answers/$CKPT.jsonl \
+    --answers-file ./eval_output/$CKPT/vizwiz/answers.jsonl \
     --temperature 0 \
-    --conv-mode vicuna_v1
+    --conv-mode $CONV_MODE
 
 python scripts/convert_vizwiz_for_submission.py \
     --annotation-file ./playground/data/eval/vizwiz/llava_test.jsonl \
-    --result-file ./playground/data/eval/vizwiz/answers/$CKPT.jsonl \
-    --result-upload-file ./playground/data/eval/vizwiz/answers_upload/$CKPT.json
+    --result-file ./eval_output/$CKPT/vizwiz/answers.jsonl \
+    --result-upload-file ./eval_output/$CKPT/vizwiz/answers_upload.json
 
 # #!/bin/bash
 # CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
diff --git a/scripts/v1_5/eval/vizwiz_multigpu.sh b/scripts/v1_5/eval/vizwiz_multigpu.sh
new file mode 100755
index 00000000..4d8dc0ee
--- /dev/null
+++ b/scripts/v1_5/eval/vizwiz_multigpu.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=${#GPULIST[@]}
+
+CKPT="llava-v1.5-13b"
+SPLIT="llava_gqa_testdev_balanced"
+GQADIR="./playground/data/eval/gqa"
+MODEL_PATH=$1
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 2 ]; then
+    CONV_MODE="$2"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/vizwiz/llava_test.jsonl \
+        --image-folder ./playground/data/eval/vizwiz/test \
+        --answers-file ./playground/data/eval/vizwiz/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+wait
+
+output_file=./playground/data/eval/vizwiz/answers/llava-v1.5-13b.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./playground/data/eval/vizwiz/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python scripts/convert_vizwiz_for_submission.py \
+    --annotation-file ./playground/data/eval/vizwiz/llava_test.jsonl \
+    --result-file ./playground/data/eval/vizwiz/answers/llava-v1.5-13b.jsonl \
+    --result-upload-file ./playground/data/eval/vizwiz/answers_upload/llava-v1.5-13b.json
diff --git a/scripts/v1_5/eval/vizwiz_sharded.sh b/scripts/v1_5/eval/vizwiz_sharded.sh
new file mode 100755
index 00000000..0e057669
--- /dev/null
+++ b/scripts/v1_5/eval/vizwiz_sharded.sh
@@ -0,0 +1,44 @@
+#!/bin/bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/vizwiz/llava_test.jsonl \
+        --image-folder ./playground/data/eval/vizwiz/test \
+        --answers-file ./eval_output/$CKPT/vizwiz/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+wait
+
+output_file=./eval_output/$CKPT/vizwiz/answers/$CKPT.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/vizwiz/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+python scripts/convert_vizwiz_for_submission.py \
+    --annotation-file ./playground/data/eval/vizwiz/llava_test.jsonl \
+    --result-file ./eval_output/$CKPT/vizwiz/answers/$CKPT.jsonl \
+    --result-upload-file ./eval_output/$CKPT/vizwiz/answers_upload/$CKPT.json
diff --git a/scripts/v1_5/eval/vqav2.sh b/scripts/v1_5/eval/vqav2.sh
index 5e0cb7c7..4539186a 100755
--- a/scripts/v1_5/eval/vqav2.sh
+++ b/scripts/v1_5/eval/vqav2.sh
@@ -8,30 +8,36 @@ CHUNKS=${#GPULIST[@]}
 SPLIT="llava_vqav2_mscoco_test-dev2015"
 MODEL_PATH=$1
 CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
 
 for IDX in $(seq 0 $((CHUNKS-1))); do
     CUDA_VISIBLE_DEVICES=${GPULIST[$IDX]} python -m llava.eval.model_vqa_loader \
         --model-path $MODEL_PATH \
         --question-file ./playground/data/eval/vqav2/$SPLIT.jsonl \
         --image-folder ./playground/data/eval/vqav2/test2015 \
-        --answers-file ./playground/data/eval/vqav2/answers/$SPLIT/$CKPT/${CHUNKS}_${IDX}.jsonl \
+        --answers-file ./eval_output/$CKPT/vqav2/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl \
         --num-chunks $CHUNKS \
         --chunk-idx $IDX \
         --temperature 0 \
-        --conv-mode vicuna_v1 &
+        --conv-mode $CONV_MODE &
 done
 
 wait
 
-output_file=./playground/data/eval/vqav2/answers/$SPLIT/$CKPT/merge.jsonl
+output_file=./eval_output/$CKPT/vqav2/$SPLIT/answers/merge.jsonl
 
 # Clear out the output file if it exists.
 > "$output_file"
 
 # Loop through the indices and concatenate each file.
 for IDX in $(seq 0 $((CHUNKS-1))); do
-    cat ./playground/data/eval/vqav2/answers/$SPLIT/$CKPT/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+    cat ./eval_output/$CKPT/vqav2/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
 done
 
-python scripts/convert_vqav2_for_submission.py --split $SPLIT --ckpt $CKPT
+cp ./playground/data/eval/vqav2/llava_vqav2_mscoco_test2015.jsonl ./eval_output/$CKPT/vqav2/
+
+python scripts/convert_vqav2_for_submission.py --dir ./eval_output/$CKPT/vqav2/ --split $SPLIT
 
diff --git a/scripts/v1_5/eval/vqav2_sharded.sh b/scripts/v1_5/eval/vqav2_sharded.sh
new file mode 100755
index 00000000..a249884a
--- /dev/null
+++ b/scripts/v1_5/eval/vqav2_sharded.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+SPLIT="llava_vqav2_mscoco_test-dev2015"
+
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+gpu_list="${CUDA_VISIBLE_DEVICES:-0}"
+IFS=',' read -ra GPULIST <<< "$gpu_list"
+
+CHUNKS=$(( ${#GPULIST[@]} / 2 )) # Calculate chunks for 2 GPUs per chunk
+
+MODEL_PATH=$1
+CKPT=$2
+CONV_MODE=vicuna_v1
+if [ "$#" -ge 3 ]; then
+    CONV_MODE="$3"
+fi
+
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    GPU_IDX1=$((IDX * 2))  # First GPU index
+    GPU_IDX2=$((GPU_IDX1 + 1))  # Second GPU index
+
+    CUDA_VISIBLE_DEVICES=${GPULIST[$GPU_IDX1]},${GPULIST[$GPU_IDX2]} python -m llava.eval.model_vqa_loader \
+        --model-path $MODEL_PATH \
+        --question-file ./playground/data/eval/vqav2/$SPLIT.jsonl \
+        --image-folder ./playground/data/eval/vqav2/test2015 \
+        --answers-file ./eval_output/$CKPT/vqav2/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl \
+        --num-chunks $CHUNKS \
+        --chunk-idx $IDX \
+        --temperature 0 \
+        --conv-mode $CONV_MODE &
+done
+
+wait
+
+output_file=./eval_output/$CKPT/vqav2/$SPLIT/answers/merge.jsonl
+
+# Clear out the output file if it exists.
+> "$output_file"
+
+# Loop through the indices and concatenate each file.
+for IDX in $(seq 0 $((CHUNKS-1))); do
+    cat ./eval_output/$CKPT/vqav2/$SPLIT/answers/${CHUNKS}_${IDX}.jsonl >> "$output_file"
+done
+
+cp ./playground/data/eval/vqav2/llava_vqav2_mscoco_test2015.jsonl ./eval_output/$CKPT/vqav2/
+
+python scripts/convert_vqav2_for_submission.py --dir ./eval_output/$CKPT/vqav2/ --split $SPLIT
+
diff --git a/scripts/v1_5/paper/1_mm_align.sh b/scripts/v1_5/release/13b/1_mm_align.sh
old mode 100755
new mode 100644
similarity index 78%
rename from scripts/v1_5/paper/1_mm_align.sh
rename to scripts/v1_5/release/13b/1_mm_align.sh
index 3d07755f..72d6cf27
--- a/scripts/v1_5/paper/1_mm_align.sh
+++ b/scripts/v1_5/release/13b/1_mm_align.sh
@@ -13,9 +13,9 @@ bs=${BATCH_SIZE:-32}
 acc_step=${ACC_STEP:-1}
 
 # for example, lmsys/vicuna-7b-v1.5
-BASE_MODEL_PATH=${1:-"lmsys/vicuna-7b-v1.5"}
+BASE_MODEL_PATH=${1:-"lmsys/vicuna-13b-v1.5"}
 # for example, llava-v1.5-7b-mm-align
-OUTPUT=${2:-"llava-v1.5-7b-mm-align"}
+OUTPUT=${2:-"vila-v1.5-mm-align"}
 
 MNAME=$(echo $BASE_MODEL_PATH | rev | cut -d "/" -f 1 | rev)
 
@@ -26,12 +26,16 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --model_name_or_path $BASE_MODEL_PATH \
     --version plain \
     --data_mixture llava_1_5_mm_align \
-    --vision_tower openai/clip-vit-large-patch14-336 \
-    --mm_projector_type mlp2x_gelu \
-    --tune_mm_mlp_adapter True \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model False \
     --mm_vision_select_layer -2 \
     --mm_use_im_start_end False \
     --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
     --bf16 True \
     --output_dir ./checkpoints/$OUTPUT \
     --num_train_epochs 1 \
@@ -48,8 +52,8 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --lr_scheduler_type "cosine" \
     --logging_steps 1 \
     --tf32 True \
-    --model_max_length 2048 \
+    --model_max_length 4096 \
     --gradient_checkpointing True \
-    --dataloader_num_workers 4 \
+    --dataloader_num_workers 8 \
     --lazy_preprocess True \
     --report_to wandb
diff --git a/scripts/v1_5/paper/2_pretrain_mmc4_coyo.sh b/scripts/v1_5/release/13b/2_pretrain.sh
old mode 100755
new mode 100644
similarity index 79%
rename from scripts/v1_5/paper/2_pretrain_mmc4_coyo.sh
rename to scripts/v1_5/release/13b/2_pretrain.sh
index 1f317fb2..9703db8f
--- a/scripts/v1_5/paper/2_pretrain_mmc4_coyo.sh
+++ b/scripts/v1_5/release/13b/2_pretrain.sh
@@ -9,13 +9,10 @@ n_node=${SLURM_JOB_NUM_NODES:-1}
 echo "MASTER_ADDR="$MASTER_ADDR
 echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
 
-CODE_PATH=$1
-# for example, lmsys/vicuna-7b-v1.5
-BASE_MODEL_PATH=$2
-# the OUTPUT of stage 1 script
-STAGE1_PATH=$3
+# OUTPUT of stage 1 script
+STAGE1_PATH=$1
 # for example, llava-v1.5-7b-mm-align
-OUTPUT=$4
+OUTPUT=$2
 
 export NCCL_IB_SL=1
 export CUDA_DEVICE_MAX_CONNECTIONS=1
@@ -37,15 +34,17 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --deepspeed ./scripts/zero3.json \
     --model_name_or_path $BASE_MODEL_PATH \
     --version v1 \
-    --data_mixture coyo_25m_mmc4core \
-    --vision_tower openai/clip-vit-large-patch14-336 \
-    --pretrain_mm_mlp_adapter ./checkpoints/$STAGE1_PATH/mm_projector.bin \
-    --mm_projector_type mlp2x_gelu \
+    --data_mixture coyo_25m+mmc4core+sharegpt4v_pretrain \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model True \
     --mm_vision_select_layer -2 \
     --mm_use_im_start_end False \
     --mm_use_im_patch_token False \
-    --image_aspect_ratio pad \
-    --group_by_modality_length True \
+    --image_aspect_ratio resize \
     --bf16 True \
     --output_dir ./checkpoints/$OUTPUT \
     --num_train_epochs 1 \
@@ -54,7 +53,7 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --gradient_accumulation_steps 1 \
     --evaluation_strategy "no" \
     --save_strategy "steps" \
-    --save_steps 50000 \
+    --save_steps 200 \
     --save_total_limit 1 \
     --learning_rate 5e-5 \
     --weight_decay 0. \
diff --git a/scripts/v1_5/paper/3_sft.sh b/scripts/v1_5/release/13b/3_sft.sh
old mode 100755
new mode 100644
similarity index 65%
rename from scripts/v1_5/paper/3_sft.sh
rename to scripts/v1_5/release/13b/3_sft.sh
index 44e822a3..2cd6aef2
--- a/scripts/v1_5/paper/3_sft.sh
+++ b/scripts/v1_5/release/13b/3_sft.sh
@@ -21,28 +21,30 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
     llava/train/train_mem.py \
     --deepspeed ./scripts/zero3.json \
-    --model_name_or_path $STAGE2_PATH \
+    --model_name_or_path $BASE_MODEL_PATH \
     --version v1 \
-    --data_mixture vflan_sharegpt4v_sft \
-    --vflan_no_system_prompt True \
-    --vision_tower openai/clip-vit-large-patch14-336 \
-    --mm_projector_type mlp2x_gelu \
+    --data_mixture sharegpt4v_gpt4_100k+llava_instruct+sharegpt4v_sft+dvqa_train_200k+chartqa_train_18k+ai2d_train_12k+docvqa_train_10k+geoqa+synthdog_en+vflan+shot2story_shotonly+video_chatgpt+youcook2+vatex+sharegpt_video+scienceqa+wit_subset+math+sherlock \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower True \
+    --tune_mm_projector True \
+    --tune_language_model True \
     --mm_vision_select_layer -2 \
     --mm_use_im_start_end False \
     --mm_use_im_patch_token False \
-    --image_aspect_ratio pad \
-    --group_by_modality_length True \
+    --image_aspect_ratio resize \
     --bf16 True \
     --output_dir ./checkpoints/$OUTPUT \
     --num_train_epochs 1 \
     --per_device_train_batch_size $bs \
     --per_device_eval_batch_size 4 \
-    --gradient_accumulation_steps 1 \
+    --gradient_accumulation_steps 2 \
     --evaluation_strategy "no" \
     --save_strategy "steps" \
-    --save_steps 50000 \
+    --save_steps 100 \
     --save_total_limit 1 \
-    --learning_rate 2e-5 \
+    --learning_rate 1e-4 \
     --weight_decay 0. \
     --warmup_ratio 0.03 \
     --lr_scheduler_type "cosine" \
@@ -50,6 +52,7 @@ torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
     --tf32 True \
     --model_max_length 4096 \
     --gradient_checkpointing True \
-    --dataloader_num_workers 4 \
+    --dataloader_num_workers 16 \
     --lazy_preprocess True \
+    --vflan_no_system_prompt True \
     --report_to wandb
diff --git a/scripts/v1_5/release/3b-s2/1_mm_align.sh b/scripts/v1_5/release/3b-s2/1_mm_align.sh
new file mode 100644
index 00000000..ef50d530
--- /dev/null
+++ b/scripts/v1_5/release/3b-s2/1_mm_align.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+bs=${BATCH_SIZE:-32}
+acc_step=${ACC_STEP:-1}
+
+# for example, lmsys/vicuna-7b-v1.5
+BASE_MODEL_PATH=${1:-"princeton-nlp/Sheared-LLaMA-2.7B"}
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=${2:-"vila-v1.5-mm-align"}
+
+MNAME=$(echo $BASE_MODEL_PATH | rev | cut -d "/" -f 1 | rev)
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero2.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version plain \
+    --data_mixture llava_1_5_mm_align \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --s2 True \
+    --s2_scales "384,768" \
+    --s2_max_split_size 384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model False \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 24000 \
+    --save_total_limit 1 \
+    --learning_rate 1e-3 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 8 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/3b-s2/2_pretrain.sh b/scripts/v1_5/release/3b-s2/2_pretrain.sh
new file mode 100644
index 00000000..60fe2fff
--- /dev/null
+++ b/scripts/v1_5/release/3b-s2/2_pretrain.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+# OUTPUT of stage 1 script
+STAGE1_PATH=$1
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=$2
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+#export NCCL_DEBUG=INFO
+export NCCL_ASYNC_ERROR_HANDLING=1
+#export CUDA_LAUNCH_BLOCKING=1
+
+
+
+n_node=$SLURM_JOB_NUM_NODES
+bs=$((128 / n_node))
+echo "number of nodes:" $n_node
+echo "per device batch size:" $bs
+echo "node rank:" $SLURM_PROCID
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version v1 \
+    --data_mixture coyo_25m+mmc4core+sharegpt4v_pretrain \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --s2 True \
+    --s2_scales "384,768" \
+    --s2_max_split_size 384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 200 \
+    --save_total_limit 1 \
+    --learning_rate 5e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/3b-s2/3_sft.sh b/scripts/v1_5/release/3b-s2/3_sft.sh
new file mode 100644
index 00000000..56cb6e12
--- /dev/null
+++ b/scripts/v1_5/release/3b-s2/3_sft.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+n_nodes=1
+bs=16
+# OUTPUT of stage 2 script
+STAGE2_PATH=$1
+# Final output checkpoint path
+OUTPUT=$2
+
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version v1 \
+    --data_mixture sharegpt4v_gpt4_100k+llava_instruct+sharegpt4v_sft+dvqa_train_200k+chartqa_train_18k+ai2d_train_12k+docvqa_train_10k+geoqa+synthdog_en+vflan+shot2story_shotonly+video_chatgpt+youcook2+vatex+sharegpt_video+scienceqa+wit_subset+math+sherlock \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --s2 True \
+    --s2_scales "384,768" \
+    --s2_max_split_size 384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower True \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 2 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 1 \
+    --learning_rate 1e-4 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 16 \
+    --lazy_preprocess True \
+    --vflan_no_system_prompt True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/3b/1_mm_align.sh b/scripts/v1_5/release/3b/1_mm_align.sh
new file mode 100644
index 00000000..e12ca706
--- /dev/null
+++ b/scripts/v1_5/release/3b/1_mm_align.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+bs=${BATCH_SIZE:-32}
+acc_step=${ACC_STEP:-1}
+
+# for example, lmsys/vicuna-7b-v1.5
+BASE_MODEL_PATH=${1:-"princeton-nlp/Sheared-LLaMA-2.7B"}
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=${2:-"vila-v1.5-mm-align"}
+
+MNAME=$(echo $BASE_MODEL_PATH | rev | cut -d "/" -f 1 | rev)
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero2.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version plain \
+    --data_mixture llava_1_5_mm_align \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model False \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 24000 \
+    --save_total_limit 1 \
+    --learning_rate 1e-3 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 8 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/3b/2_pretrain.sh b/scripts/v1_5/release/3b/2_pretrain.sh
new file mode 100644
index 00000000..9703db8f
--- /dev/null
+++ b/scripts/v1_5/release/3b/2_pretrain.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+# OUTPUT of stage 1 script
+STAGE1_PATH=$1
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=$2
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+#export NCCL_DEBUG=INFO
+export NCCL_ASYNC_ERROR_HANDLING=1
+#export CUDA_LAUNCH_BLOCKING=1
+
+
+
+n_node=$SLURM_JOB_NUM_NODES
+bs=$((128 / n_node))
+echo "number of nodes:" $n_node
+echo "per device batch size:" $bs
+echo "node rank:" $SLURM_PROCID
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version v1 \
+    --data_mixture coyo_25m+mmc4core+sharegpt4v_pretrain \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 200 \
+    --save_total_limit 1 \
+    --learning_rate 5e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/3b/3_sft.sh b/scripts/v1_5/release/3b/3_sft.sh
new file mode 100644
index 00000000..2cd6aef2
--- /dev/null
+++ b/scripts/v1_5/release/3b/3_sft.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+n_nodes=1
+bs=16
+# OUTPUT of stage 2 script
+STAGE2_PATH=$1
+# Final output checkpoint path
+OUTPUT=$2
+
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version v1 \
+    --data_mixture sharegpt4v_gpt4_100k+llava_instruct+sharegpt4v_sft+dvqa_train_200k+chartqa_train_18k+ai2d_train_12k+docvqa_train_10k+geoqa+synthdog_en+vflan+shot2story_shotonly+video_chatgpt+youcook2+vatex+sharegpt_video+scienceqa+wit_subset+math+sherlock \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower True \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 2 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 1 \
+    --learning_rate 1e-4 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 16 \
+    --lazy_preprocess True \
+    --vflan_no_system_prompt True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/40b/1_mm_align.sh b/scripts/v1_5/release/40b/1_mm_align.sh
new file mode 100644
index 00000000..20c14c99
--- /dev/null
+++ b/scripts/v1_5/release/40b/1_mm_align.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+bs=${BATCH_SIZE:-32}
+acc_step=${ACC_STEP:-1}
+
+# for example, lmsys/vicuna-7b-v1.5
+BASE_MODEL_PATH=${1:-"NousResearch/Nous-Hermes-2-Yi-34B"}
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=${2:-"vila-v1.5-mm-align"}
+
+MNAME=$(echo $BASE_MODEL_PATH | rev | cut -d "/" -f 1 | rev)
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version plain \
+    --data_mixture llava_1_5_mm_align \
+    --vision_tower OpenGVLab/InternViT-6B-448px-V1-2 \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model False \
+    --mm_vision_select_layer -1 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 50 \
+    --save_total_limit 1 \
+    --learning_rate 1e-3 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb
\ No newline at end of file
diff --git a/scripts/v1_5/release/40b/2_pretrain.sh b/scripts/v1_5/release/40b/2_pretrain.sh
new file mode 100644
index 00000000..7ed34850
--- /dev/null
+++ b/scripts/v1_5/release/40b/2_pretrain.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+# OUTPUT of stage 1 script
+STAGE1_PATH=$1
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=$2
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+#export NCCL_DEBUG=INFO
+export NCCL_ASYNC_ERROR_HANDLING=1
+#export CUDA_LAUNCH_BLOCKING=1
+
+
+
+n_node=$SLURM_JOB_NUM_NODES
+bs=$((128 / n_node))
+echo "number of nodes:" $n_node
+echo "per device batch size:" $bs
+echo "node rank:" $SLURM_PROCID
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version hermes-2 \
+    --data_mixture coyo_25m+mmc4core+sharegpt4v_pretrain \
+    --vision_tower OpenGVLab/InternViT-6B-448px-V1-2 \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -1 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 50 \
+    --save_total_limit 1 \
+    --learning_rate 5e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb
\ No newline at end of file
diff --git a/scripts/v1_5/release/40b/3_sft.sh b/scripts/v1_5/release/40b/3_sft.sh
new file mode 100644
index 00000000..9b4f0002
--- /dev/null
+++ b/scripts/v1_5/release/40b/3_sft.sh
@@ -0,0 +1,57 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+n_nodes=1
+bs=16
+# OUTPUT of stage 2 script
+STAGE2_PATH=$1
+# Final output checkpoint path
+OUTPUT=$2
+
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version hermes-2 \
+    --data_mixture sharegpt4v_gpt4_100k+llava_instruct+sharegpt4v_sft+dvqa_train_200k+chartqa_train_18k+ai2d_train_12k+docvqa_train_10k+geoqa+synthdog_en+vflan+shot2story_shotonly+video_chatgpt+youcook2+vatex+sharegpt_video+scienceqa+wit_subset+math+sherlock \
+    --vision_tower OpenGVLab/InternViT-6B-448px-V1-2 \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower True \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -1 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 50 \
+    --save_total_limit 1 \
+    --learning_rate 2e-5 \
+    --weight_decay 0.05 \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 16 \
+    --lazy_preprocess True \
+    --vflan_no_system_prompt True \
+    --report_to wandb
\ No newline at end of file
diff --git a/scripts/v1_5/release/8b/1_mm_align.sh b/scripts/v1_5/release/8b/1_mm_align.sh
new file mode 100644
index 00000000..4c7aba3c
--- /dev/null
+++ b/scripts/v1_5/release/8b/1_mm_align.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+bs=${BATCH_SIZE:-32}
+acc_step=${ACC_STEP:-1}
+
+# for example, lmsys/vicuna-7b-v1.5
+BASE_MODEL_PATH=${1:-"meta-llama/Meta-Llama-3-8B-Instruct"}
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=${2:-"vila-v1.5-mm-align"}
+
+MNAME=$(echo $BASE_MODEL_PATH | rev | cut -d "/" -f 1 | rev)
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero2.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version plain \
+    --data_mixture llava_1_5_mm_align \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model False \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 24000 \
+    --save_total_limit 1 \
+    --learning_rate 1e-3 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 8 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/8b/2_pretrain.sh b/scripts/v1_5/release/8b/2_pretrain.sh
new file mode 100644
index 00000000..6d13fb25
--- /dev/null
+++ b/scripts/v1_5/release/8b/2_pretrain.sh
@@ -0,0 +1,68 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+# OUTPUT of stage 1 script
+STAGE1_PATH=$1
+# for example, llava-v1.5-7b-mm-align
+OUTPUT=$2
+
+export NCCL_IB_SL=1
+export CUDA_DEVICE_MAX_CONNECTIONS=1
+#export NCCL_DEBUG=INFO
+export NCCL_ASYNC_ERROR_HANDLING=1
+#export CUDA_LAUNCH_BLOCKING=1
+
+
+
+n_node=$SLURM_JOB_NUM_NODES
+bs=$((128 / n_node))
+echo "number of nodes:" $n_node
+echo "per device batch size:" $bs
+echo "node rank:" $SLURM_PROCID
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version llama_3 \
+    --data_mixture coyo_25m+mmc4core+sharegpt4v_pretrain \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower False \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 1 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 200 \
+    --save_total_limit 1 \
+    --learning_rate 5e-5 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 4 \
+    --lazy_preprocess True \
+    --report_to wandb
diff --git a/scripts/v1_5/release/8b/3_sft.sh b/scripts/v1_5/release/8b/3_sft.sh
new file mode 100644
index 00000000..28525d08
--- /dev/null
+++ b/scripts/v1_5/release/8b/3_sft.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+
+master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_ADDR=${master_addr:-"127.0.0.1"}
+export CURRENT_RANK=${SLURM_PROCID:-"0"}
+worker_list=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | tr '\n' ' ')
+n_node=${SLURM_JOB_NUM_NODES:-1}
+
+echo "MASTER_ADDR="$MASTER_ADDR
+echo "JobID: $SLURM_JOB_ID | Full list: $worker_list"
+
+n_nodes=1
+bs=16
+# OUTPUT of stage 2 script
+STAGE2_PATH=$1
+# Final output checkpoint path
+OUTPUT=$2
+
+
+torchrun --nnodes=$n_node --nproc_per_node=8 --master_port=25001 \
+    --master_addr $MASTER_ADDR --node_rank=$CURRENT_RANK \
+    llava/train/train_mem.py \
+    --deepspeed ./scripts/zero3.json \
+    --model_name_or_path $BASE_MODEL_PATH \
+    --version llama_3 \
+    --data_mixture sharegpt4v_gpt4_100k+llava_instruct+sharegpt4v_sft+dvqa_train_200k+chartqa_train_18k+ai2d_train_12k+docvqa_train_10k+geoqa+synthdog_en+vflan+shot2story_shotonly+video_chatgpt+youcook2+vatex+sharegpt_video+scienceqa+wit_subset+math+sherlock \
+    --vision_tower google/siglip-so400m-patch14-384 \
+    --mm_vision_select_feature cls_patch \
+    --mm_projector mlp_downsample \
+    --tune_vision_tower True \
+    --tune_mm_projector True \
+    --tune_language_model True \
+    --mm_vision_select_layer -2 \
+    --mm_use_im_start_end False \
+    --mm_use_im_patch_token False \
+    --image_aspect_ratio resize \
+    --bf16 True \
+    --output_dir ./checkpoints/$OUTPUT \
+    --num_train_epochs 1 \
+    --per_device_train_batch_size $bs \
+    --per_device_eval_batch_size 4 \
+    --gradient_accumulation_steps 2 \
+    --evaluation_strategy "no" \
+    --save_strategy "steps" \
+    --save_steps 100 \
+    --save_total_limit 1 \
+    --learning_rate 1e-4 \
+    --weight_decay 0. \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --tf32 True \
+    --model_max_length 4096 \
+    --gradient_checkpointing True \
+    --dataloader_num_workers 16 \
+    --lazy_preprocess True \
+    --vflan_no_system_prompt True \
+    --report_to wandb