Update README.md

README.md

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 [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 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. 
+VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling **video understanding** and **multi-image understanding** capabilities. VILA is deployable on the edge by [AWQ](https://arxiv.org/pdf/2306.00978.pdf) 4bit quantization and [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; (4) token compression extends #video frames. VILA unveils appealing capabilities, including: video reasoning, in-context learning, visual chain-of-thought, and better world knowledge. 
 
 
 ## 💡 News
-- [2024/05] We release VILA-1.5, which offers **video understanding support** and comes with four model sizes (3B/8B/13B/40B).
-- [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 offers **video understanding capability**. VILA-1.5 comes with four model sizes: 3B/8B/13B/40B.
+- [2024/05] We release [AWQ](https://arxiv.org/pdf/2306.00978.pdf)-quantized 4bit VILA-1.5 models. VILA-1.5 is efficiently deployable on NVIDIA GPUs (A100, 4090, Orin) by [TinyChat](https://github.com/mit-han-lab/llm-awq/tree/main/tinychat) and [TensorRT-LLM](demo_trt_llm) backends.
 - [2024/03] VILA has been accepted by CVPR 2024!
 - [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.
+- [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