[docs] Redesign (#31757)
* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
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Steven Liu
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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@@ -13,386 +13,34 @@ specific language governing permissions and limitations under the License.
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rendered properly in your Markdown viewer.
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-->
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# 🤗 Transformers
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# Transformers
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State-of-the-art Machine Learning for [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), and [JAX](https://jax.readthedocs.io/en/latest/).
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Transformers is a library of pretrained natural language processing, computer vision, audio, and multimodal models for inference and training. Use Transformers to train models on your data, build inference applications, and generate text with large language models.
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🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
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Explore the [Hugging Face Hub](https://huggingface.com) today to find a model and use Transformers to help you get started right away.
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📝 **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, code generation, summarization, translation, multiple choice, and text generation.<br>
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🖼️ **Computer Vision**: image classification, object detection, and segmentation.<br>
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🗣️ **Audio**: automatic speech recognition and audio classification.<br>
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🐙 **Multimodal**: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
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## Features
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🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
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Transformers provides everything you need for inference or training with state-of-the-art pretrained models. Some of the main features include:
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Join the growing community on the [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), or [Discord](https://discord.com/invite/JfAtkvEtRb) today!
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- [Pipeline](./pipeline_tutorial): Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more.
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- [Trainer](./trainer): A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models.
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- [generate](./llm_tutorial): Fast text generation with large language models (LLMs) and vision language models (VLMs), including support for streaming and multiple decoding strategies.
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## If you are looking for custom support from the Hugging Face team
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## Design
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<a target="_blank" href="https://huggingface.co/support">
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<img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
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</a>
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> [!TIP]
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> Read our [Philosophy](./philosophy) to learn more about Transformers' design principles.
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## Contents
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Transformers is designed for developers and machine learning engineers and researchers. Its main design principles are:
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The documentation is organized into five sections:
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1. Fast and easy to use: Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training with [`Pipeline`] or [`Trainer`].
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2. Pretrained models: Reduce your carbon footprint, compute cost and time by using a pretrained model instead of training an entirely new one. Each pretrained model is reproduced as closely as possible to the original model and offers state-of-the-art performance.
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- **GET STARTED** provides a quick tour of the library and installation instructions to get up and running.
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- **TUTORIALS** are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.
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- **HOW-TO GUIDES** show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.
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- **CONCEPTUAL GUIDES** offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
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- **API** describes all classes and functions:
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<div class="flex justify-center">
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<a target="_blank" href="https://huggingface.co/support">
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<img alt="HuggingFace Expert Acceleration Program" src="https://hf.co/datasets/huggingface/documentation-images/resolve/81d7d9201fd4ceb537fc4cebc22c29c37a2ed216/transformers/transformers-index.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
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</a>
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</div>
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- **MAIN CLASSES** details the most important classes like configuration, model, tokenizer, and pipeline.
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- **MODELS** details the classes and functions related to each model implemented in the library.
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- **INTERNAL HELPERS** details utility classes and functions used internally.
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## Supported models and frameworks
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The table below represents the current support in the library for each of those models, whether they have a Python
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tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
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Flax), PyTorch, and/or TensorFlow.
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<!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->
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| Model | PyTorch support | TensorFlow support | Flax Support |
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|:------------------------------------------------------------------------:|:---------------:|:------------------:|:------------:|
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| [ALBERT](model_doc/albert) | ✅ | ✅ | ✅ |
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| [ALIGN](model_doc/align) | ✅ | ❌ | ❌ |
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| [AltCLIP](model_doc/altclip) | ✅ | ❌ | ❌ |
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| [Aria](model_doc/aria) | ✅ | ❌ | ❌ |
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| [AriaText](model_doc/aria_text) | ✅ | ❌ | ❌ |
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| [Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
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| [Autoformer](model_doc/autoformer) | ✅ | ❌ | ❌ |
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| [Bamba](model_doc/bamba) | ✅ | ❌ | ❌ |
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| [Bark](model_doc/bark) | ✅ | ❌ | ❌ |
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| [BART](model_doc/bart) | ✅ | ✅ | ✅ |
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| [BARThez](model_doc/barthez) | ✅ | ✅ | ✅ |
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| [BARTpho](model_doc/bartpho) | ✅ | ✅ | ✅ |
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| [BEiT](model_doc/beit) | ✅ | ❌ | ✅ |
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| [BERT](model_doc/bert) | ✅ | ✅ | ✅ |
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| [Bert Generation](model_doc/bert-generation) | ✅ | ❌ | ❌ |
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| [BertJapanese](model_doc/bert-japanese) | ✅ | ✅ | ✅ |
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| [BERTweet](model_doc/bertweet) | ✅ | ✅ | ✅ |
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| [BigBird](model_doc/big_bird) | ✅ | ❌ | ✅ |
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| [BigBird-Pegasus](model_doc/bigbird_pegasus) | ✅ | ❌ | ❌ |
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| [BioGpt](model_doc/biogpt) | ✅ | ❌ | ❌ |
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| [BiT](model_doc/bit) | ✅ | ❌ | ❌ |
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| [Blenderbot](model_doc/blenderbot) | ✅ | ✅ | ✅ |
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| [BlenderbotSmall](model_doc/blenderbot-small) | ✅ | ✅ | ✅ |
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| [BLIP](model_doc/blip) | ✅ | ✅ | ❌ |
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| [BLIP-2](model_doc/blip-2) | ✅ | ❌ | ❌ |
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| [BLOOM](model_doc/bloom) | ✅ | ❌ | ✅ |
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| [BORT](model_doc/bort) | ✅ | ✅ | ✅ |
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| [BridgeTower](model_doc/bridgetower) | ✅ | ❌ | ❌ |
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| [BROS](model_doc/bros) | ✅ | ❌ | ❌ |
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| [ByT5](model_doc/byt5) | ✅ | ✅ | ✅ |
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| [CamemBERT](model_doc/camembert) | ✅ | ✅ | ❌ |
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| [CANINE](model_doc/canine) | ✅ | ❌ | ❌ |
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| [Chameleon](model_doc/chameleon) | ✅ | ❌ | ❌ |
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| [Chinese-CLIP](model_doc/chinese_clip) | ✅ | ❌ | ❌ |
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| [CLAP](model_doc/clap) | ✅ | ❌ | ❌ |
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| [CLIP](model_doc/clip) | ✅ | ✅ | ✅ |
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| [CLIPSeg](model_doc/clipseg) | ✅ | ❌ | ❌ |
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| [CLVP](model_doc/clvp) | ✅ | ❌ | ❌ |
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| [CodeGen](model_doc/codegen) | ✅ | ❌ | ❌ |
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| [CodeLlama](model_doc/code_llama) | ✅ | ❌ | ✅ |
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| [Cohere](model_doc/cohere) | ✅ | ❌ | ❌ |
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| [Cohere2](model_doc/cohere2) | ✅ | ❌ | ❌ |
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| [ColPali](model_doc/colpali) | ✅ | ❌ | ❌ |
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| [Conditional DETR](model_doc/conditional_detr) | ✅ | ❌ | ❌ |
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| [ConvBERT](model_doc/convbert) | ✅ | ✅ | ❌ |
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| [ConvNeXT](model_doc/convnext) | ✅ | ✅ | ❌ |
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| [ConvNeXTV2](model_doc/convnextv2) | ✅ | ✅ | ❌ |
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| [CPM](model_doc/cpm) | ✅ | ✅ | ✅ |
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| [CPM-Ant](model_doc/cpmant) | ✅ | ❌ | ❌ |
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| [CTRL](model_doc/ctrl) | ✅ | ✅ | ❌ |
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| [CvT](model_doc/cvt) | ✅ | ✅ | ❌ |
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| [DAB-DETR](model_doc/dab-detr) | ✅ | ❌ | ❌ |
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| [DAC](model_doc/dac) | ✅ | ❌ | ❌ |
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| [Data2VecAudio](model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecText](model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecVision](model_doc/data2vec) | ✅ | ✅ | ❌ |
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| [DBRX](model_doc/dbrx) | ✅ | ❌ | ❌ |
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| [DeBERTa](model_doc/deberta) | ✅ | ✅ | ❌ |
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| [DeBERTa-v2](model_doc/deberta-v2) | ✅ | ✅ | ❌ |
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| [Decision Transformer](model_doc/decision_transformer) | ✅ | ❌ | ❌ |
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| [Deformable DETR](model_doc/deformable_detr) | ✅ | ❌ | ❌ |
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| [DeiT](model_doc/deit) | ✅ | ✅ | ❌ |
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| [DePlot](model_doc/deplot) | ✅ | ❌ | ❌ |
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| [Depth Anything](model_doc/depth_anything) | ✅ | ❌ | ❌ |
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| [DepthPro](model_doc/depth_pro) | ✅ | ❌ | ❌ |
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| [DETA](model_doc/deta) | ✅ | ❌ | ❌ |
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| [DETR](model_doc/detr) | ✅ | ❌ | ❌ |
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| [DialoGPT](model_doc/dialogpt) | ✅ | ✅ | ✅ |
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| [DiffLlama](model_doc/diffllama) | ✅ | ❌ | ❌ |
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| [DiNAT](model_doc/dinat) | ✅ | ❌ | ❌ |
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| [DINOv2](model_doc/dinov2) | ✅ | ❌ | ✅ |
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| [DINOv2 with Registers](model_doc/dinov2_with_registers) | ✅ | ❌ | ❌ |
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| [DistilBERT](model_doc/distilbert) | ✅ | ✅ | ✅ |
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| [DiT](model_doc/dit) | ✅ | ❌ | ✅ |
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| [DonutSwin](model_doc/donut) | ✅ | ❌ | ❌ |
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| [DPR](model_doc/dpr) | ✅ | ✅ | ❌ |
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| [DPT](model_doc/dpt) | ✅ | ❌ | ❌ |
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| [EfficientFormer](model_doc/efficientformer) | ✅ | ✅ | ❌ |
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| [EfficientNet](model_doc/efficientnet) | ✅ | ❌ | ❌ |
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| [ELECTRA](model_doc/electra) | ✅ | ✅ | ✅ |
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| [Emu3](model_doc/emu3) | ✅ | ❌ | ❌ |
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| [EnCodec](model_doc/encodec) | ✅ | ❌ | ❌ |
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| [Encoder decoder](model_doc/encoder-decoder) | ✅ | ✅ | ✅ |
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| [ERNIE](model_doc/ernie) | ✅ | ❌ | ❌ |
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| [ErnieM](model_doc/ernie_m) | ✅ | ❌ | ❌ |
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| [ESM](model_doc/esm) | ✅ | ✅ | ❌ |
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| [FairSeq Machine-Translation](model_doc/fsmt) | ✅ | ❌ | ❌ |
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| [Falcon](model_doc/falcon) | ✅ | ❌ | ❌ |
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| [Falcon3](model_doc/falcon3) | ✅ | ❌ | ✅ |
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| [FalconMamba](model_doc/falcon_mamba) | ✅ | ❌ | ❌ |
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| [FastSpeech2Conformer](model_doc/fastspeech2_conformer) | ✅ | ❌ | ❌ |
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| [FLAN-T5](model_doc/flan-t5) | ✅ | ✅ | ✅ |
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| [FLAN-UL2](model_doc/flan-ul2) | ✅ | ✅ | ✅ |
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| [FlauBERT](model_doc/flaubert) | ✅ | ✅ | ❌ |
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| [FLAVA](model_doc/flava) | ✅ | ❌ | ❌ |
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| [FNet](model_doc/fnet) | ✅ | ❌ | ❌ |
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| [FocalNet](model_doc/focalnet) | ✅ | ❌ | ❌ |
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| [Funnel Transformer](model_doc/funnel) | ✅ | ✅ | ❌ |
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| [Fuyu](model_doc/fuyu) | ✅ | ❌ | ❌ |
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| [Gemma](model_doc/gemma) | ✅ | ❌ | ✅ |
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| [Gemma2](model_doc/gemma2) | ✅ | ❌ | ❌ |
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| [GIT](model_doc/git) | ✅ | ❌ | ❌ |
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| [GLM](model_doc/glm) | ✅ | ❌ | ❌ |
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| [GLPN](model_doc/glpn) | ✅ | ❌ | ❌ |
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| [GOT-OCR2](model_doc/got_ocr2) | ✅ | ❌ | ❌ |
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| [GPT Neo](model_doc/gpt_neo) | ✅ | ❌ | ✅ |
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| [GPT NeoX](model_doc/gpt_neox) | ✅ | ❌ | ❌ |
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| [GPT NeoX Japanese](model_doc/gpt_neox_japanese) | ✅ | ❌ | ❌ |
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| [GPT-J](model_doc/gptj) | ✅ | ✅ | ✅ |
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| [GPT-Sw3](model_doc/gpt-sw3) | ✅ | ✅ | ✅ |
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| [GPTBigCode](model_doc/gpt_bigcode) | ✅ | ❌ | ❌ |
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| [GPTSAN-japanese](model_doc/gptsan-japanese) | ✅ | ❌ | ❌ |
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| [Granite](model_doc/granite) | ✅ | ❌ | ❌ |
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| [GraniteMoeMoe](model_doc/granitemoe) | ✅ | ❌ | ❌ |
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| [GraniteMoeSharedMoe](model_doc/granitemoeshared) | ✅ | ❌ | ❌ |
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| [Graphormer](model_doc/graphormer) | ✅ | ❌ | ❌ |
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| [Grounding DINO](model_doc/grounding-dino) | ✅ | ❌ | ❌ |
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| [GroupViT](model_doc/groupvit) | ✅ | ✅ | ❌ |
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| [Helium](model_doc/helium) | ✅ | ❌ | ❌ |
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| [HerBERT](model_doc/herbert) | ✅ | ✅ | ✅ |
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| [Hiera](model_doc/hiera) | ✅ | ❌ | ❌ |
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| [Hubert](model_doc/hubert) | ✅ | ✅ | ❌ |
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| [I-BERT](model_doc/ibert) | ✅ | ❌ | ❌ |
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| [I-JEPA](model_doc/ijepa) | ✅ | ❌ | ❌ |
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| [IDEFICS](model_doc/idefics) | ✅ | ✅ | ❌ |
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| [Idefics2](model_doc/idefics2) | ✅ | ❌ | ❌ |
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| [Idefics3](model_doc/idefics3) | ✅ | ❌ | ❌ |
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| [Idefics3VisionTransformer](model_doc/idefics3_vision) | ❌ | ❌ | ❌ |
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| [ImageGPT](model_doc/imagegpt) | ✅ | ❌ | ❌ |
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| [Informer](model_doc/informer) | ✅ | ❌ | ❌ |
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| [InstructBLIP](model_doc/instructblip) | ✅ | ❌ | ❌ |
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| [InstructBlipVideo](model_doc/instructblipvideo) | ✅ | ❌ | ❌ |
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| [Jamba](model_doc/jamba) | ✅ | ❌ | ❌ |
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| [JetMoe](model_doc/jetmoe) | ✅ | ❌ | ❌ |
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| [Jukebox](model_doc/jukebox) | ✅ | ❌ | ❌ |
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| [KOSMOS-2](model_doc/kosmos-2) | ✅ | ❌ | ❌ |
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| [LayoutLM](model_doc/layoutlm) | ✅ | ✅ | ❌ |
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| [LayoutLMv2](model_doc/layoutlmv2) | ✅ | ❌ | ❌ |
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| [LayoutLMv3](model_doc/layoutlmv3) | ✅ | ✅ | ❌ |
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| [LayoutXLM](model_doc/layoutxlm) | ✅ | ❌ | ❌ |
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| [LED](model_doc/led) | ✅ | ✅ | ❌ |
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| [LeViT](model_doc/levit) | ✅ | ❌ | ❌ |
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| [LiLT](model_doc/lilt) | ✅ | ❌ | ❌ |
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| [LLaMA](model_doc/llama) | ✅ | ❌ | ✅ |
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| [Llama2](model_doc/llama2) | ✅ | ❌ | ✅ |
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||||
| [Llama3](model_doc/llama3) | ✅ | ❌ | ✅ |
|
||||
| [LLaVa](model_doc/llava) | ✅ | ❌ | ❌ |
|
||||
| [LLaVA-NeXT](model_doc/llava_next) | ✅ | ❌ | ❌ |
|
||||
| [LLaVa-NeXT-Video](model_doc/llava_next_video) | ✅ | ❌ | ❌ |
|
||||
| [LLaVA-Onevision](model_doc/llava_onevision) | ✅ | ❌ | ❌ |
|
||||
| [Longformer](model_doc/longformer) | ✅ | ✅ | ❌ |
|
||||
| [LongT5](model_doc/longt5) | ✅ | ❌ | ✅ |
|
||||
| [LUKE](model_doc/luke) | ✅ | ❌ | ❌ |
|
||||
| [LXMERT](model_doc/lxmert) | ✅ | ✅ | ❌ |
|
||||
| [M-CTC-T](model_doc/mctct) | ✅ | ❌ | ❌ |
|
||||
| [M2M100](model_doc/m2m_100) | ✅ | ❌ | ❌ |
|
||||
| [MADLAD-400](model_doc/madlad-400) | ✅ | ✅ | ✅ |
|
||||
| [Mamba](model_doc/mamba) | ✅ | ❌ | ❌ |
|
||||
| [mamba2](model_doc/mamba2) | ✅ | ❌ | ❌ |
|
||||
| [Marian](model_doc/marian) | ✅ | ✅ | ✅ |
|
||||
| [MarkupLM](model_doc/markuplm) | ✅ | ❌ | ❌ |
|
||||
| [Mask2Former](model_doc/mask2former) | ✅ | ❌ | ❌ |
|
||||
| [MaskFormer](model_doc/maskformer) | ✅ | ❌ | ❌ |
|
||||
| [MatCha](model_doc/matcha) | ✅ | ❌ | ❌ |
|
||||
| [mBART](model_doc/mbart) | ✅ | ✅ | ✅ |
|
||||
| [mBART-50](model_doc/mbart50) | ✅ | ✅ | ✅ |
|
||||
| [MEGA](model_doc/mega) | ✅ | ❌ | ❌ |
|
||||
| [Megatron-BERT](model_doc/megatron-bert) | ✅ | ❌ | ❌ |
|
||||
| [Megatron-GPT2](model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
|
||||
| [MGP-STR](model_doc/mgp-str) | ✅ | ❌ | ❌ |
|
||||
| [Mimi](model_doc/mimi) | ✅ | ❌ | ❌ |
|
||||
| [Mistral](model_doc/mistral) | ✅ | ✅ | ✅ |
|
||||
| [Mixtral](model_doc/mixtral) | ✅ | ❌ | ❌ |
|
||||
| [Mllama](model_doc/mllama) | ✅ | ❌ | ❌ |
|
||||
| [mLUKE](model_doc/mluke) | ✅ | ❌ | ❌ |
|
||||
| [MMS](model_doc/mms) | ✅ | ✅ | ✅ |
|
||||
| [MobileBERT](model_doc/mobilebert) | ✅ | ✅ | ❌ |
|
||||
| [MobileNetV1](model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
|
||||
| [MobileNetV2](model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
|
||||
| [MobileViT](model_doc/mobilevit) | ✅ | ✅ | ❌ |
|
||||
| [MobileViTV2](model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
|
||||
| [ModernBERT](model_doc/modernbert) | ✅ | ❌ | ❌ |
|
||||
| [Moonshine](model_doc/moonshine) | ✅ | ❌ | ❌ |
|
||||
| [Moshi](model_doc/moshi) | ✅ | ❌ | ❌ |
|
||||
| [MPNet](model_doc/mpnet) | ✅ | ✅ | ❌ |
|
||||
| [MPT](model_doc/mpt) | ✅ | ❌ | ❌ |
|
||||
| [MRA](model_doc/mra) | ✅ | ❌ | ❌ |
|
||||
| [MT5](model_doc/mt5) | ✅ | ✅ | ✅ |
|
||||
| [MusicGen](model_doc/musicgen) | ✅ | ❌ | ❌ |
|
||||
| [MusicGen Melody](model_doc/musicgen_melody) | ✅ | ❌ | ❌ |
|
||||
| [MVP](model_doc/mvp) | ✅ | ❌ | ❌ |
|
||||
| [NAT](model_doc/nat) | ✅ | ❌ | ❌ |
|
||||
| [Nemotron](model_doc/nemotron) | ✅ | ❌ | ❌ |
|
||||
| [Nezha](model_doc/nezha) | ✅ | ❌ | ❌ |
|
||||
| [NLLB](model_doc/nllb) | ✅ | ❌ | ❌ |
|
||||
| [NLLB-MOE](model_doc/nllb-moe) | ✅ | ❌ | ❌ |
|
||||
| [Nougat](model_doc/nougat) | ✅ | ✅ | ✅ |
|
||||
| [Nyströmformer](model_doc/nystromformer) | ✅ | ❌ | ❌ |
|
||||
| [OLMo](model_doc/olmo) | ✅ | ❌ | ❌ |
|
||||
| [OLMo2](model_doc/olmo2) | ✅ | ❌ | ❌ |
|
||||
| [OLMoE](model_doc/olmoe) | ✅ | ❌ | ❌ |
|
||||
| [OmDet-Turbo](model_doc/omdet-turbo) | ✅ | ❌ | ❌ |
|
||||
| [OneFormer](model_doc/oneformer) | ✅ | ❌ | ❌ |
|
||||
| [OpenAI GPT](model_doc/openai-gpt) | ✅ | ✅ | ❌ |
|
||||
| [OpenAI GPT-2](model_doc/gpt2) | ✅ | ✅ | ✅ |
|
||||
| [OpenLlama](model_doc/open-llama) | ✅ | ❌ | ❌ |
|
||||
| [OPT](model_doc/opt) | ✅ | ✅ | ✅ |
|
||||
| [OWL-ViT](model_doc/owlvit) | ✅ | ❌ | ❌ |
|
||||
| [OWLv2](model_doc/owlv2) | ✅ | ❌ | ❌ |
|
||||
| [PaliGemma](model_doc/paligemma) | ✅ | ❌ | ❌ |
|
||||
| [PatchTSMixer](model_doc/patchtsmixer) | ✅ | ❌ | ❌ |
|
||||
| [PatchTST](model_doc/patchtst) | ✅ | ❌ | ❌ |
|
||||
| [Pegasus](model_doc/pegasus) | ✅ | ✅ | ✅ |
|
||||
| [PEGASUS-X](model_doc/pegasus_x) | ✅ | ❌ | ❌ |
|
||||
| [Perceiver](model_doc/perceiver) | ✅ | ❌ | ❌ |
|
||||
| [Persimmon](model_doc/persimmon) | ✅ | ❌ | ❌ |
|
||||
| [Phi](model_doc/phi) | ✅ | ❌ | ❌ |
|
||||
| [Phi3](model_doc/phi3) | ✅ | ❌ | ❌ |
|
||||
| [Phimoe](model_doc/phimoe) | ✅ | ❌ | ❌ |
|
||||
| [PhoBERT](model_doc/phobert) | ✅ | ✅ | ✅ |
|
||||
| [Pix2Struct](model_doc/pix2struct) | ✅ | ❌ | ❌ |
|
||||
| [Pixtral](model_doc/pixtral) | ✅ | ❌ | ❌ |
|
||||
| [PLBart](model_doc/plbart) | ✅ | ❌ | ❌ |
|
||||
| [PoolFormer](model_doc/poolformer) | ✅ | ❌ | ❌ |
|
||||
| [Pop2Piano](model_doc/pop2piano) | ✅ | ❌ | ❌ |
|
||||
| [ProphetNet](model_doc/prophetnet) | ✅ | ❌ | ❌ |
|
||||
| [PVT](model_doc/pvt) | ✅ | ❌ | ❌ |
|
||||
| [PVTv2](model_doc/pvt_v2) | ✅ | ❌ | ❌ |
|
||||
| [QDQBert](model_doc/qdqbert) | ✅ | ❌ | ❌ |
|
||||
| [Qwen2](model_doc/qwen2) | ✅ | ❌ | ❌ |
|
||||
| [Qwen2_5_VL](model_doc/qwen2_5_vl) | ✅ | ❌ | ❌ |
|
||||
| [Qwen2Audio](model_doc/qwen2_audio) | ✅ | ❌ | ❌ |
|
||||
| [Qwen2MoE](model_doc/qwen2_moe) | ✅ | ❌ | ❌ |
|
||||
| [Qwen2VL](model_doc/qwen2_vl) | ✅ | ❌ | ❌ |
|
||||
| [RAG](model_doc/rag) | ✅ | ✅ | ❌ |
|
||||
| [REALM](model_doc/realm) | ✅ | ❌ | ❌ |
|
||||
| [RecurrentGemma](model_doc/recurrent_gemma) | ✅ | ❌ | ❌ |
|
||||
| [Reformer](model_doc/reformer) | ✅ | ❌ | ❌ |
|
||||
| [RegNet](model_doc/regnet) | ✅ | ✅ | ✅ |
|
||||
| [RemBERT](model_doc/rembert) | ✅ | ✅ | ❌ |
|
||||
| [ResNet](model_doc/resnet) | ✅ | ✅ | ✅ |
|
||||
| [RetriBERT](model_doc/retribert) | ✅ | ❌ | ❌ |
|
||||
| [RoBERTa](model_doc/roberta) | ✅ | ✅ | ✅ |
|
||||
| [RoBERTa-PreLayerNorm](model_doc/roberta-prelayernorm) | ✅ | ✅ | ✅ |
|
||||
| [RoCBert](model_doc/roc_bert) | ✅ | ❌ | ❌ |
|
||||
| [RoFormer](model_doc/roformer) | ✅ | ✅ | ✅ |
|
||||
| [RT-DETR](model_doc/rt_detr) | ✅ | ❌ | ❌ |
|
||||
| [RT-DETR-ResNet](model_doc/rt_detr_resnet) | ✅ | ❌ | ❌ |
|
||||
| [RT-DETRv2](model_doc/rt_detr_v2) | ✅ | ❌ | ❌ |
|
||||
| [RWKV](model_doc/rwkv) | ✅ | ❌ | ❌ |
|
||||
| [SAM](model_doc/sam) | ✅ | ✅ | ❌ |
|
||||
| [SeamlessM4T](model_doc/seamless_m4t) | ✅ | ❌ | ❌ |
|
||||
| [SeamlessM4Tv2](model_doc/seamless_m4t_v2) | ✅ | ❌ | ❌ |
|
||||
| [SegFormer](model_doc/segformer) | ✅ | ✅ | ❌ |
|
||||
| [SegGPT](model_doc/seggpt) | ✅ | ❌ | ❌ |
|
||||
| [SEW](model_doc/sew) | ✅ | ❌ | ❌ |
|
||||
| [SEW-D](model_doc/sew-d) | ✅ | ❌ | ❌ |
|
||||
| [SigLIP](model_doc/siglip) | ✅ | ❌ | ❌ |
|
||||
| [SigLIP2](model_doc/siglip2) | ✅ | ❌ | ❌ |
|
||||
| [SmolVLM](model_doc/smolvlm) | ✅ | ❌ | ❌ |
|
||||
| [Speech Encoder decoder](model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
|
||||
| [Speech2Text](model_doc/speech_to_text) | ✅ | ✅ | ❌ |
|
||||
| [SpeechT5](model_doc/speecht5) | ✅ | ❌ | ❌ |
|
||||
| [Splinter](model_doc/splinter) | ✅ | ❌ | ❌ |
|
||||
| [SqueezeBERT](model_doc/squeezebert) | ✅ | ❌ | ❌ |
|
||||
| [StableLm](model_doc/stablelm) | ✅ | ❌ | ❌ |
|
||||
| [Starcoder2](model_doc/starcoder2) | ✅ | ❌ | ❌ |
|
||||
| [SuperGlue](model_doc/superglue) | ✅ | ❌ | ❌ |
|
||||
| [SuperPoint](model_doc/superpoint) | ✅ | ❌ | ❌ |
|
||||
| [SwiftFormer](model_doc/swiftformer) | ✅ | ✅ | ❌ |
|
||||
| [Swin Transformer](model_doc/swin) | ✅ | ✅ | ❌ |
|
||||
| [Swin Transformer V2](model_doc/swinv2) | ✅ | ❌ | ❌ |
|
||||
| [Swin2SR](model_doc/swin2sr) | ✅ | ❌ | ❌ |
|
||||
| [SwitchTransformers](model_doc/switch_transformers) | ✅ | ❌ | ❌ |
|
||||
| [T5](model_doc/t5) | ✅ | ✅ | ✅ |
|
||||
| [T5v1.1](model_doc/t5v1.1) | ✅ | ✅ | ✅ |
|
||||
| [Table Transformer](model_doc/table-transformer) | ✅ | ❌ | ❌ |
|
||||
| [TAPAS](model_doc/tapas) | ✅ | ✅ | ❌ |
|
||||
| [TAPEX](model_doc/tapex) | ✅ | ✅ | ✅ |
|
||||
| [TextNet](model_doc/textnet) | ✅ | ❌ | ❌ |
|
||||
| [Time Series Transformer](model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
|
||||
| [TimeSformer](model_doc/timesformer) | ✅ | ❌ | ❌ |
|
||||
| [TimmWrapperModel](model_doc/timm_wrapper) | ✅ | ❌ | ❌ |
|
||||
| [Trajectory Transformer](model_doc/trajectory_transformer) | ✅ | ❌ | ❌ |
|
||||
| [Transformer-XL](model_doc/transfo-xl) | ✅ | ✅ | ❌ |
|
||||
| [TrOCR](model_doc/trocr) | ✅ | ❌ | ❌ |
|
||||
| [TVLT](model_doc/tvlt) | ✅ | ❌ | ❌ |
|
||||
| [TVP](model_doc/tvp) | ✅ | ❌ | ❌ |
|
||||
| [UDOP](model_doc/udop) | ✅ | ❌ | ❌ |
|
||||
| [UL2](model_doc/ul2) | ✅ | ✅ | ✅ |
|
||||
| [UMT5](model_doc/umt5) | ✅ | ❌ | ❌ |
|
||||
| [UniSpeech](model_doc/unispeech) | ✅ | ❌ | ❌ |
|
||||
| [UniSpeechSat](model_doc/unispeech-sat) | ✅ | ❌ | ❌ |
|
||||
| [UnivNet](model_doc/univnet) | ✅ | ❌ | ❌ |
|
||||
| [UPerNet](model_doc/upernet) | ✅ | ❌ | ❌ |
|
||||
| [VAN](model_doc/van) | ✅ | ❌ | ❌ |
|
||||
| [VideoLlava](model_doc/video_llava) | ✅ | ❌ | ❌ |
|
||||
| [VideoMAE](model_doc/videomae) | ✅ | ❌ | ❌ |
|
||||
| [ViLT](model_doc/vilt) | ✅ | ❌ | ❌ |
|
||||
| [VipLlava](model_doc/vipllava) | ✅ | ❌ | ❌ |
|
||||
| [Vision Encoder decoder](model_doc/vision-encoder-decoder) | ✅ | ✅ | ✅ |
|
||||
| [VisionTextDualEncoder](model_doc/vision-text-dual-encoder) | ✅ | ✅ | ✅ |
|
||||
| [VisualBERT](model_doc/visual_bert) | ✅ | ❌ | ❌ |
|
||||
| [ViT](model_doc/vit) | ✅ | ✅ | ✅ |
|
||||
| [ViT Hybrid](model_doc/vit_hybrid) | ✅ | ❌ | ❌ |
|
||||
| [VitDet](model_doc/vitdet) | ✅ | ❌ | ❌ |
|
||||
| [ViTMAE](model_doc/vit_mae) | ✅ | ✅ | ❌ |
|
||||
| [ViTMatte](model_doc/vitmatte) | ✅ | ❌ | ❌ |
|
||||
| [ViTMSN](model_doc/vit_msn) | ✅ | ❌ | ❌ |
|
||||
| [ViTPose](model_doc/vitpose) | ✅ | ❌ | ❌ |
|
||||
| [ViTPoseBackbone](model_doc/vitpose_backbone) | ✅ | ❌ | ❌ |
|
||||
| [VITS](model_doc/vits) | ✅ | ❌ | ❌ |
|
||||
| [ViViT](model_doc/vivit) | ✅ | ❌ | ❌ |
|
||||
| [Wav2Vec2](model_doc/wav2vec2) | ✅ | ✅ | ✅ |
|
||||
| [Wav2Vec2-BERT](model_doc/wav2vec2-bert) | ✅ | ❌ | ❌ |
|
||||
| [Wav2Vec2-Conformer](model_doc/wav2vec2-conformer) | ✅ | ❌ | ❌ |
|
||||
| [Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme) | ✅ | ✅ | ✅ |
|
||||
| [WavLM](model_doc/wavlm) | ✅ | ❌ | ❌ |
|
||||
| [Whisper](model_doc/whisper) | ✅ | ✅ | ✅ |
|
||||
| [X-CLIP](model_doc/xclip) | ✅ | ❌ | ❌ |
|
||||
| [X-MOD](model_doc/xmod) | ✅ | ❌ | ❌ |
|
||||
| [XGLM](model_doc/xglm) | ✅ | ✅ | ✅ |
|
||||
| [XLM](model_doc/xlm) | ✅ | ✅ | ❌ |
|
||||
| [XLM-ProphetNet](model_doc/xlm-prophetnet) | ✅ | ❌ | ❌ |
|
||||
| [XLM-RoBERTa](model_doc/xlm-roberta) | ✅ | ✅ | ✅ |
|
||||
| [XLM-RoBERTa-XL](model_doc/xlm-roberta-xl) | ✅ | ❌ | ❌ |
|
||||
| [XLM-V](model_doc/xlm-v) | ✅ | ✅ | ✅ |
|
||||
| [XLNet](model_doc/xlnet) | ✅ | ✅ | ❌ |
|
||||
| [XLS-R](model_doc/xls_r) | ✅ | ✅ | ✅ |
|
||||
| [XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2) | ✅ | ✅ | ✅ |
|
||||
| [YOLOS](model_doc/yolos) | ✅ | ❌ | ❌ |
|
||||
| [YOSO](model_doc/yoso) | ✅ | ❌ | ❌ |
|
||||
| [Zamba](model_doc/zamba) | ✅ | ❌ | ❌ |
|
||||
| [Zamba2](model_doc/zamba2) | ✅ | ❌ | ❌ |
|
||||
| [ZoeDepth](model_doc/zoedepth) | ✅ | ❌ | ❌ |
|
||||
|
||||
<!-- End table-->
|
||||
Join us on the Hugging Face [Hub](https://huggingface.co/), [Discord](https://discord.com/invite/JfAtkvEtRb), or [forum](https://discuss.huggingface.co/) to collaborate and build models, datasets, and applications together.
|
||||
|
||||
Reference in New Issue
Block a user