Merge branch 'main' into fix_hqq_tests

Author: MekkCyber

docker/transformers-all-latest-gpu/Dockerfile

@@ -65,6 +65,9 @@ RUN python3 -m pip install --no-cache-dir python-Levenshtein
 # For `FastSpeech2ConformerTokenizer` tokenizer
 RUN python3 -m pip install --no-cache-dir g2p-en
 
+# For Some bitsandbytes tests
+RUN python3 -m pip install --no-cache-dir einops
+
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docs/source/ar/_toctree.yml

@@ -35,8 +35,8 @@
     sections:
 #     - local: tasks/sequence_classification
 #       title: تصنيف النصوص
-#     - local: tasks/token_classification
-#       title: تصنيف الرموز
+    - local: tasks/token_classification
+      title: تصنيف الرموز
     - local: tasks/question_answering
       title: الإجابة على الأسئلة
 #     - local: tasks/language_modeling

docs/source/ar/tasks/token_classification.md

@@ -452,6 +452,8 @@
         title: Granite
       - local: model_doc/granitemoe
         title: GraniteMoe
+      - local: model_doc/helium
+        title: Helium
       - local: model_doc/herbert
         title: HerBERT
       - local: model_doc/ibert

docs/source/en/_toctree.yml

@@ -173,6 +173,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                    [Graphormer](model_doc/graphormer)                    |       ✅        |         ❌         |      ❌      |
 |                [Grounding DINO](model_doc/grounding-dino)                |       ✅        |         ❌         |      ❌      |
 |                      [GroupViT](model_doc/groupvit)                      |       ✅        |         ✅         |      ❌      |
+|                        [Helium](model_doc/helium)                        |       ✅        |         ❌         |      ❌      |
 |                       [HerBERT](model_doc/herbert)                       |       ✅        |         ✅         |      ✅      |
 |                         [Hiera](model_doc/hiera)                         |       ✅        |         ❌         |      ❌      |
 |                        [Hubert](model_doc/hubert)                        |       ✅        |         ✅         |      ❌      |

docs/source/en/index.md

@@ -0,0 +1,158 @@
+<!--Copyright 2024 Kyutai 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Helium
+
+
+## Overview
+
+Helium was proposed in [Announcing Helium-1 Preview](https://kyutai.org/2025/01/13/helium.html) by the Kyutai Team.
+
+
+Helium-1 preview is a lightweight language model with 2B parameters, targeting edge and mobile devices.
+It supports the following languages: English, French, German, Italian, Portuguese, Spanish.
+
+- **Developed by:** Kyutai
+- **Model type:** Large Language Model
+- **Language(s) (NLP):** English, French, German, Italian, Portuguese, Spanish
+- **License:** CC-BY 4.0
+
+
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+The model was evaluated on MMLU, TriviaQA, NaturalQuestions, ARC Easy & Challenge, Open Book QA, Common Sense QA, 
+Physical Interaction QA, Social Interaction QA, HellaSwag, WinoGrande, Multilingual Knowledge QA, FLORES 200.
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+We report accuracy on MMLU, ARC, OBQA, CSQA, PIQA, SIQA, HellaSwag, WinoGrande.
+We report exact match on TriviaQA, NQ and MKQA.
+We report BLEU on FLORES.
+
+### English Results
+
+| Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
+|--------------|--------|--------|--------|--------|--------|
+| | | | | | |
+| MMLU | 51.2 | 50.4 | 53.1 | 56.6 | 61.0 |
+| NQ   | 17.3 | 15.1 | 17.7 | 22.0 | 13.1 |
+| TQA  | 47.9 | 45.4 | 49.9 | 53.6 | 35.9 |
+| ARC E | 80.9 | 81.8 | 81.1 | 84.6 | 89.7 |
+| ARC C | 62.7 | 64.7 | 66.0 | 69.0 | 77.2 |
+| OBQA | 63.8 | 61.4 | 64.6 | 68.4 | 73.8 |
+| CSQA | 65.6 | 59.0 | 64.4 | 65.4 | 72.4 |
+| PIQA | 77.4 | 77.7 | 79.8 | 78.9 | 76.0 |
+| SIQA | 64.4 | 57.5 | 61.9 | 63.8 | 68.7 |
+| HS | 69.7 | 73.2 | 74.7 | 76.9 | 67.5 |
+| WG | 66.5 | 65.6 | 71.2 | 72.0 | 64.8 |
+| | | | | | |
+| Average | 60.7 | 59.3 | 62.2 | 64.7 | 63.6 |
+
+#### Multilingual Results
+
+| Language | Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
+|-----|--------------|--------|--------|--------|--------|--------|
+| | | | | | | |
+|German| MMLU | 45.6 | 35.3 | 45.0 | 47.5 | 49.5 |
+|| ARC C | 56.7 | 38.4 | 54.7 | 58.3 | 60.2 |
+|| HS | 53.5 | 33.9 | 53.4 | 53.7 | 42.8 |
+|| MKQA | 16.1 | 7.1 | 18.9 | 20.2 | 10.4 |
+| | | | | | | |
+|Spanish| MMLU | 46.5 | 38.9 | 46.2 | 49.6 | 52.8 |
+|| ARC C | 58.3 | 43.2 | 58.8 | 60.0 | 68.1 |
+|| HS | 58.6 | 40.8 | 60.5 | 61.1 | 51.4 |
+|| MKQA | 16.0 | 7.9 | 18.5 | 20.6 | 10.6 |
+
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+| Hyperparameter | Value |
+|--------------|--------|
+| Layers | 24 |
+| Heads  | 20 |
+| Model dimension | 2560 |
+| MLP dimension | 7040 |
+| Context size | 4096 |
+| Theta RoPE | 100,000 |
+
+Tips:
+
+- This model was contributed by [Laurent Mazare](https://huggingface.co/lmz)
+
+  
+## Usage tips
+
+`Helium` can be found on the [Huggingface Hub](https://huggingface.co/collections/kyutai/helium-1-preview)
+
+In the following, we demonstrate how to use `helium-1-preview` for the inference. 
+
+```python
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer
+>>> device = "cuda" # the device to load the model onto
+
+>>> model = AutoModelForCausalLM.from_pretrained("helium-1-preview", device_map="auto")
+>>> tokenizer = AutoTokenizer.from_pretrained("helium-1-preview")
+
+>>> prompt = "Give me a short introduction to large language model."
+
+>>> messages = [{"role": "user", "content": prompt}]
+
+>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+>>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
+
+>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+
+>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## HeliumConfig
+
+[[autodoc]] HeliumConfig
+
+## HeliumModel
+
+[[autodoc]] HeliumModel
+    - forward
+
+## HeliumForCausalLM
+
+[[autodoc]] HeliumForCausalLM
+    - forward
+
+## HeliumForSequenceClassification
+
+[[autodoc]] HeliumForSequenceClassification
+    - forward
+
+## HeliumForTokenClassification
+
+[[autodoc]] HeliumForTokenClassification
+    - forward

docs/source/en/model_doc/helium.md

@@ -102,7 +102,7 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP.
 
-- [Zero-shot image classification task guide](../tasks/zero_shot_image_classification_md)
+- [Zero-shot image classification task guide](../tasks/zero_shot_image_classification)
 - Demo notebooks for SigLIP can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SigLIP). 🌎
 
 If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

docs/source/en/model_doc/siglip.md

@@ -109,6 +109,7 @@ FlashAttention-2 is currently supported for the following architectures:
 * [SigLIP](https://huggingface.co/docs/transformers/model_doc/siglip)
 * [UniSpeech](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech#transformers.UniSpeechModel)
 * [unispeech_sat](https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel)
+* [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
 
 You can request to add FlashAttention-2 support for another model by opening a GitHub Issue or Pull Request.
 
@@ -324,6 +325,7 @@ For now, Transformers supports SDPA inference and training for the following arc
 * [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaModel)
 * [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel)
 * [YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos#transformers.YolosModel)
+* [helium](https://huggingface.co/docs/transformers/main/en/model_doc/heliumtransformers.HeliumModel)
 
 <Tip>
 

docs/source/en/perf_infer_gpu_one.md

@@ -498,6 +498,7 @@
         "GroupViTTextConfig",
         "GroupViTVisionConfig",
     ],
+    "models.helium": ["HeliumConfig"],
     "models.herbert": ["HerbertTokenizer"],
     "models.hiera": ["HieraConfig"],
     "models.hubert": ["HubertConfig"],
@@ -2506,6 +2507,15 @@
             "GroupViTVisionModel",
         ]
     )
+    _import_structure["models.helium"].extend(
+        [
+            "HeliumForCausalLM",
+            "HeliumForSequenceClassification",
+            "HeliumForTokenClassification",
+            "HeliumModel",
+            "HeliumPreTrainedModel",
+        ]
+    )
     _import_structure["models.hiera"].extend(
         [
             "HieraBackbone",
@@ -5529,6 +5539,7 @@
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )
+    from .models.helium import HeliumConfig
     from .models.herbert import HerbertTokenizer
     from .models.hiera import HieraConfig
     from .models.hubert import HubertConfig
@@ -7371,6 +7382,13 @@
             GroupViTTextModel,
             GroupViTVisionModel,
         )
+        from .models.helium import (
+            HeliumForCausalLM,
+            HeliumForSequenceClassification,
+            HeliumForTokenClassification,
+            HeliumModel,
+            HeliumPreTrainedModel,
+        )
         from .models.hiera import (
             HieraBackbone,
             HieraForImageClassification,

src/transformers/__init__.py

@@ -994,8 +994,11 @@ def dict_torch_dtype_to_str(self, d: Dict[str, Any]) -> None:
         converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
         string, which can then be stored in the json format.
         """
-        if d.get("torch_dtype", None) is not None and not isinstance(d["torch_dtype"], str):
-            d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1]
+        if d.get("torch_dtype", None) is not None:
+            if isinstance(d["torch_dtype"], dict):
+                d["torch_dtype"] = {k: str(v).split(".")[-1] for k, v in d["torch_dtype"].items()}
+            elif not isinstance(d["torch_dtype"], str):
+                d["torch_dtype"] = str(d["torch_dtype"]).split(".")[1]
         for value in d.values():
             if isinstance(value, dict):
                 self.dict_torch_dtype_to_str(value)

src/transformers/configuration_utils.py

@@ -1446,6 +1446,95 @@ def pre_tokenizer(self, replacement, add_prefix_space):
         return pre_tokenizers.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme, split=False)
 
 
+class HeliumConverter(SpmConverter):
+    handle_byte_fallback = True
+
+    def __init__(self, vocab_file=None, *args):
+        requires_backends(self, "protobuf")
+
+        Converter.__init__(self, vocab_file)
+
+        model_pb2 = import_protobuf()
+
+        m = model_pb2.ModelProto()
+        with open(vocab_file, "rb") as f:
+            m.ParseFromString(f.read())
+        self.proto = m
+
+    def tokenizer(self, proto):
+        vocab_scores = self.vocab(proto)
+        tokenizer = Tokenizer(
+            Unigram(
+                vocab_scores,
+                unk_id=self.unk_id(proto),
+                byte_fallback=self.handle_byte_fallback,
+            )
+        )
+        # control tokens are special
+        # user defined symbols are not
+        # both user and control tokens are AddedTokens
+        # Add user defined symbols (type == 4) from sentencepiece (https://github.com/google/sentencepiece/blob/6225e08edb2577757163b3f5dbba4c0b670ef445/src/sentencepiece_model.proto#L299C29-L299C33)
+        spm_added_tokens = [
+            (id, p.piece, p.type == 3 or p.piece in self.special_tokens)
+            for id, p in enumerate(proto.pieces)
+            if p.type in [3, 4]
+        ]
+        tokenizer.add_tokens(
+            [
+                AddedToken(token, normalized=False, special=special, single_word=True)
+                for id, token, special in sorted(spm_added_tokens, key=lambda x: x[0])
+            ]
+        )
+        tokenizer.add_tokens([AddedToken("\n", normalized=False, special=False)])
+        tokenizer.enable_padding(pad_token="<pad>", pad_id=3)
+        return tokenizer
+
+    def vocab(self, proto):
+        vocab = []
+        for piece in proto.pieces:
+            if piece.piece == "<0x0A>":
+                vocab += [("\n", piece.score)]
+            else:
+                vocab += [(piece.piece, piece.score)]
+        return vocab
+
+    def unk_id(self, proto):
+        unk_id = 0
+        return unk_id
+
+    def decoder(self, replacement, add_prefix_space):
+        sequence = [
+            decoders.Replace("▁", " "),
+            decoders.ByteFallback(),
+            decoders.Fuse(),
+        ]
+        sequence += [decoders.Strip(content=" ", left=1)]
+        return decoders.Sequence(sequence)
+
+    def normalizer(self, proto):
+        return normalizers.Sequence([normalizers.Prepend(" "), normalizers.Replace(r" ", "▁")])
+
+    def pre_tokenizer(self, replacement, add_prefix_space):
+        return pre_tokenizers.Sequence([pre_tokenizers.Split("\n", "contiguous")])
+
+    def post_processor(self):
+        return processors.TemplateProcessing(
+            single=[
+                "<s>",
+                "$A",
+            ],
+            pair=[
+                "<s>",
+                "$A",
+                "<s>",
+                "$B",
+            ],
+            special_tokens=[
+                ("<s>", 1),
+            ],
+        )
+
+
 # Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
 def bytes_to_unicode():
     """