mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-20 04:58:22 +06:00
3f20877da9
* Add jamba arch * apply "make fix-copies" changes * fix link to model in JambaConfig docstring * Add n_ctx in modeling file because repo-consistency wants that * Add jamba to flash attention and sdpa documentation * mamba dt_proj quant fix now works for LoRA as well * override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers * add jamba to tokenization auto * fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24) * simple PR fixes * remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer * remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530) * Add copied comment on JambaMLP (it's the same as MixtralMLP) * remove padding_mask warnings. It's not supported anymore * fix docstring. Float instead of int * A few more minor PR fixes * (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass * Return None attention weights from mamba layers. Append to all attentions only if not None. * remove some leftover jamba archive lists * Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel * no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers * Add Jamba paper on READMEs * (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes) * Add copied from comment * remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms * clearer docstring for _convert_to_standard_cache * style fixes * Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs * rename test so it still overrides what its meant to override * draft * oups * nit * remove more complexe logic * fix names used in config * fix fix fix * style * fix some more failing tests * generate did not init the cache 🙃 * more small nits * typo * config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes * fix init of pkv with torch.tensor() * empty tensor * fix some init issues * stupid changes required by generate because it does not even support it's own DynamicCache class * more fixes * fix general assisted gen cache_position bug * tests passing * Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py * fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache * no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore * fix docstrings and typehints for past_key_values * style fixes * fix docs * change typehint due to copy from Mixtral * forgot import * import order * Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward) * Add integration test with tiny tandom Jamba model on hub * fix flash attention cache shapes * bring back forgotten hidden states * rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model * align integration test after modeling fixes * bugfix - mamba can use precomputed states only of forward pass is on a single token * bugfix - mamba can use precomputed states only if they match the batch size * typo * remove making _prepare_4d_causal_attention_mask a leaf function * stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Joao Gante <joao@huggingface.co>
337 lines
40 KiB
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337 lines
40 KiB
Markdown
<!--Copyright 2020 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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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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rendered properly in your Markdown viewer.
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-->
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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 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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📝 **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, 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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🤗 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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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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## If you are looking for custom support from the Hugging Face team
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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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## Contents
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The documentation is organized into five sections:
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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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- **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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| [Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
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| [Autoformer](model_doc/autoformer) | ✅ | ❌ | ❌ |
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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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| [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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| [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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| [Data2VecAudio](model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecText](model_doc/data2vec) | ✅ | ❌ | ❌ |
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| [Data2VecVision](model_doc/data2vec) | ✅ | ✅ | ❌ |
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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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| [DETA](model_doc/deta) | ✅ | ❌ | ❌ |
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| [DETR](model_doc/detr) | ✅ | ❌ | ❌ |
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| [DialoGPT](model_doc/dialogpt) | ✅ | ✅ | ✅ |
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| [DiNAT](model_doc/dinat) | ✅ | ❌ | ❌ |
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| [DINOv2](model_doc/dinov2) | ✅ | ❌ | ❌ |
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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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| [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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| [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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| [GIT](model_doc/git) | ✅ | ❌ | ❌ |
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| [GLPN](model_doc/glpn) | ✅ | ❌ | ❌ |
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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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| [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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| [HerBERT](model_doc/herbert) | ✅ | ✅ | ✅ |
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| [Hubert](model_doc/hubert) | ✅ | ✅ | ❌ |
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| [I-BERT](model_doc/ibert) | ✅ | ❌ | ❌ |
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| [IDEFICS](model_doc/idefics) | ✅ | ❌ | ❌ |
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| [Idefics2](model_doc/idefics2) | ✅ | ❌ | ❌ |
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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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| [Jamba](model_doc/jamba) | ✅ | ❌ | ❌ |
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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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| [LLaVa](model_doc/llava) | ✅ | ❌ | ❌ |
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| [LLaVA-NeXT](model_doc/llava_next) | ✅ | ❌ | ❌ |
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| [Longformer](model_doc/longformer) | ✅ | ✅ | ❌ |
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| [LongT5](model_doc/longt5) | ✅ | ❌ | ✅ |
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| [LUKE](model_doc/luke) | ✅ | ❌ | ❌ |
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| [LXMERT](model_doc/lxmert) | ✅ | ✅ | ❌ |
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| [M-CTC-T](model_doc/mctct) | ✅ | ❌ | ❌ |
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| [M2M100](model_doc/m2m_100) | ✅ | ❌ | ❌ |
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| [MADLAD-400](model_doc/madlad-400) | ✅ | ✅ | ✅ |
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| [Mamba](model_doc/mamba) | ✅ | ❌ | ❌ |
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| [Marian](model_doc/marian) | ✅ | ✅ | ✅ |
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| [MarkupLM](model_doc/markuplm) | ✅ | ❌ | ❌ |
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| [Mask2Former](model_doc/mask2former) | ✅ | ❌ | ❌ |
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| [MaskFormer](model_doc/maskformer) | ✅ | ❌ | ❌ |
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| [MatCha](model_doc/matcha) | ✅ | ❌ | ❌ |
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| [mBART](model_doc/mbart) | ✅ | ✅ | ✅ |
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| [mBART-50](model_doc/mbart50) | ✅ | ✅ | ✅ |
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| [MEGA](model_doc/mega) | ✅ | ❌ | ❌ |
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| [Megatron-BERT](model_doc/megatron-bert) | ✅ | ❌ | ❌ |
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| [Megatron-GPT2](model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
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| [MGP-STR](model_doc/mgp-str) | ✅ | ❌ | ❌ |
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| [Mistral](model_doc/mistral) | ✅ | ❌ | ✅ |
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| [Mixtral](model_doc/mixtral) | ✅ | ❌ | ❌ |
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| [mLUKE](model_doc/mluke) | ✅ | ❌ | ❌ |
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| [MMS](model_doc/mms) | ✅ | ✅ | ✅ |
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| [MobileBERT](model_doc/mobilebert) | ✅ | ✅ | ❌ |
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| [MobileNetV1](model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
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| [MobileNetV2](model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
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| [MobileViT](model_doc/mobilevit) | ✅ | ✅ | ❌ |
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| [MobileViTV2](model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
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| [MPNet](model_doc/mpnet) | ✅ | ✅ | ❌ |
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| [MPT](model_doc/mpt) | ✅ | ❌ | ❌ |
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| [MRA](model_doc/mra) | ✅ | ❌ | ❌ |
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| [MT5](model_doc/mt5) | ✅ | ✅ | ✅ |
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| [MusicGen](model_doc/musicgen) | ✅ | ❌ | ❌ |
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| [MusicGen Melody](model_doc/musicgen_melody) | ✅ | ❌ | ❌ |
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| [MVP](model_doc/mvp) | ✅ | ❌ | ❌ |
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| [NAT](model_doc/nat) | ✅ | ❌ | ❌ |
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| [Nezha](model_doc/nezha) | ✅ | ❌ | ❌ |
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| [NLLB](model_doc/nllb) | ✅ | ❌ | ❌ |
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| [NLLB-MOE](model_doc/nllb-moe) | ✅ | ❌ | ❌ |
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| [Nougat](model_doc/nougat) | ✅ | ✅ | ✅ |
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| [Nyströmformer](model_doc/nystromformer) | ✅ | ❌ | ❌ |
|
|
| [OLMo](model_doc/olmo) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [PhoBERT](model_doc/phobert) | ✅ | ✅ | ✅ |
|
|
| [Pix2Struct](model_doc/pix2struct) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [Qwen2MoE](model_doc/qwen2_moe) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ✅ | ✅ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ✅ | ✅ |
|
|
| [Time Series Transformer](model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
|
|
| [TimeSformer](model_doc/timesformer) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
| [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) | ✅ | ❌ | ❌ |
|
|
|
|
<!-- End table-->
|