mirror of https://github.com/huggingface/transformers.git synced 2025-07-20 13:08:21 +06:00

* Add template with add-new-model-like

* Add rough OmDetTurboEncoder and OmDetTurboDecoder

* Add working OmDetTurbo convert to hf

* Change OmDetTurbo encoder to RT-DETR encoder

* Add swin timm backbone as default, add always partition fix for swin timm

* Add labels and tasks caching

* Fix make fix-copies

* Format omdet_turbo

* fix Tokenizer tests

* Fix style and quality

* Reformat omdet_turbo

* Fix quality, style, copies

* Standardize processor kwargs

* Fix style

* Add output_hidden_states and ouput_attentions

* Add personalize multi-head attention, improve docstrings

* Add integrated test and fix copy, style, quality

* Fix unprotected import

* Cleanup comments and fix unprotected imports

* Add fix different prompts in batch (key_padding_mask)

* Add key_padding_mask to custom multi-head attention module

* Replace attention_mask by key_padding_mask

* Remove OmDetTurboModel and refactor

* Refactor processing of classes and abstract use of timm backbone

* Add testing, fix output attentions and hidden states, add cache for anchors generation

* Fix copies, style, quality

* Add documentation, conver key_padding_mask to attention_mask

* revert changes to backbone_utils

* Fic docstrings rst

* Fix unused argument in config

* Fix image link documentation

* Reorder config and cleanup

* Add tokenizer_init_kwargs in merge_kwargs of the processor

* Change AutoTokenizer to CLIPTokenizer in convert

* Fix init_weights

* Add ProcessorMixin tests, Fix convert while waiting on uniform kwargs

* change processor kwargs and make task input optional

* Fix omdet docs

* Remove unnecessary tests for processor kwargs

* Replace nested BatchEncoding output of the processor by a flattened BatchFeature

* Make modifications from Pavel review

* Add changes Amy review

* Remove unused param

* Remove normalize_before param, Modify processor call docstring

* Remove redundant decoder class, add gradient checkpointing for decoder

* Remove commented out code

* Fix inference in fp16 and add fp16 integrated test

* update omdet md doc

* Add OmdetTurboModel

* fix caching and nit

* add OmDetTurboModel to tests

* nit change repeated key test

* Improve inference speed in eager mode

* fix copies

* Fix nit

* remove OmdetTurboModel

* [run-slow] omdet_turbo

* [run-slow] omdet_turbo

* skip dataparallel test

* [run-slow] omdet_turbo

* update weights to new path

* remove unnecessary config in class

---------

Co-authored-by: Ubuntu <ubuntu@ip-172-31-91-248.ec2.internal>

2024-09-25 13:26:28 -04:00

44 KiB

Raw Blame History

🤗 Transformers

State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.

🤗 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:

📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

🤗 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.

Join the growing community on the Hub, forum, or Discord today!

If you are looking for custom support from the Hugging Face team

The documentation is organized into five sections:

GET STARTED provides a quick tour of the library and installation instructions to get up and running.
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.
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.
CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
API describes all classes and functions:
- MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.
- MODELS details the classes and functions related to each model implemented in the library.
- INTERNAL HELPERS details utility classes and functions used internally.

Supported models and frameworks

The table below represents the current support in the library for each of those models, whether they have a Python tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via Flax), PyTorch, and/or TensorFlow.

Model	PyTorch support	TensorFlow support	Flax Support
ALBERT	✅	✅	✅
ALIGN	✅	❌	❌
AltCLIP	✅	❌	❌
Audio Spectrogram Transformer	✅	❌	❌
Autoformer	✅	❌	❌
Bark	✅	❌	❌
BART	✅	✅	✅
BARThez	✅	✅	✅
BARTpho	✅	✅	✅
BEiT	✅	❌	✅
BERT	✅	✅	✅
Bert Generation	✅	❌	❌
BertJapanese	✅	✅	✅
BERTweet	✅	✅	✅
BigBird	✅	❌	✅
BigBird-Pegasus	✅	❌	❌
BioGpt	✅	❌	❌
BiT	✅	❌	❌
Blenderbot	✅	✅	✅
BlenderbotSmall	✅	✅	✅
BLIP	✅	✅	❌
BLIP-2	✅	❌	❌
BLOOM	✅	❌	✅
BORT	✅	✅	✅
BridgeTower	✅	❌	❌
BROS	✅	❌	❌
ByT5	✅	✅	✅
CamemBERT	✅	✅	❌
CANINE	✅	❌	❌
Chameleon	✅	❌	❌
Chinese-CLIP	✅	❌	❌
CLAP	✅	❌	❌
CLIP	✅	✅	✅
CLIPSeg	✅	❌	❌
CLVP	✅	❌	❌
CodeGen	✅	❌	❌
CodeLlama	✅	❌	✅
Cohere	✅	❌	❌
Conditional DETR	✅	❌	❌
ConvBERT	✅	✅	❌
ConvNeXT	✅	✅	❌
ConvNeXTV2	✅	✅	❌
CPM	✅	✅	✅
CPM-Ant	✅	❌	❌
CTRL	✅	✅	❌
CvT	✅	✅	❌
DAC	✅	❌	❌
Data2VecAudio	✅	❌	❌
Data2VecText	✅	❌	❌
Data2VecVision	✅	✅	❌
DBRX	✅	❌	❌
DeBERTa	✅	✅	❌
DeBERTa-v2	✅	✅	❌
Decision Transformer	✅	❌	❌
Deformable DETR	✅	❌	❌
DeiT	✅	✅	❌
DePlot	✅	❌	❌
Depth Anything	✅	❌	❌
DETA	✅	❌	❌
DETR	✅	❌	❌
DialoGPT	✅	✅	✅
DiNAT	✅	❌	❌
DINOv2	✅	❌	✅
DistilBERT	✅	✅	✅
DiT	✅	❌	✅
DonutSwin	✅	❌	❌
DPR	✅	✅	❌
DPT	✅	❌	❌
EfficientFormer	✅	✅	❌
EfficientNet	✅	❌	❌
ELECTRA	✅	✅	✅
EnCodec	✅	❌	❌
Encoder decoder	✅	✅	✅
ERNIE	✅	❌	❌
ErnieM	✅	❌	❌
ESM	✅	✅	❌
FairSeq Machine-Translation	✅	❌	❌
Falcon	✅	❌	❌
FalconMamba	✅	❌	❌
FastSpeech2Conformer	✅	❌	❌
FLAN-T5	✅	✅	✅
FLAN-UL2	✅	✅	✅
FlauBERT	✅	✅	❌
FLAVA	✅	❌	❌
FNet	✅	❌	❌
FocalNet	✅	❌	❌
Funnel Transformer	✅	✅	❌
Fuyu	✅	❌	❌
Gemma	✅	❌	✅
Gemma2	✅	❌	❌
GIT	✅	❌	❌
GLPN	✅	❌	❌
GPT Neo	✅	❌	✅
GPT NeoX	✅	❌	❌
GPT NeoX Japanese	✅	❌	❌
GPT-J	✅	✅	✅
GPT-Sw3	✅	✅	✅
GPTBigCode	✅	❌	❌
GPTSAN-japanese	✅	❌	❌
Granite	✅	❌	❌
GraniteMoeMoe	✅	❌	❌
Graphormer	✅	❌	❌
Grounding DINO	✅	❌	❌
GroupViT	✅	✅	❌
HerBERT	✅	✅	✅
Hiera	✅	❌	❌
Hubert	✅	✅	❌
I-BERT	✅	❌	❌
IDEFICS	✅	✅	❌
Idefics2	✅	❌	❌
ImageGPT	✅	❌	❌
Informer	✅	❌	❌
InstructBLIP	✅	❌	❌
InstructBlipVideo	✅	❌	❌
Jamba	✅	❌	❌
JetMoe	✅	❌	❌
Jukebox	✅	❌	❌
KOSMOS-2	✅	❌	❌
LayoutLM	✅	✅	❌
LayoutLMv2	✅	❌	❌
LayoutLMv3	✅	✅	❌
LayoutXLM	✅	❌	❌
LED	✅	✅	❌
LeViT	✅	❌	❌
LiLT	✅	❌	❌
LLaMA	✅	❌	✅
Llama2	✅	❌	✅
Llama3	✅	❌	✅
LLaVa	✅	❌	❌
LLaVA-NeXT	✅	❌	❌
LLaVa-NeXT-Video	✅	❌	❌
LLaVA-Onevision	✅	❌	❌
Longformer	✅	✅	❌
LongT5	✅	❌	✅
LUKE	✅	❌	❌
LXMERT	✅	✅	❌
M-CTC-T	✅	❌	❌
M2M100	✅	❌	❌
MADLAD-400	✅	✅	✅
Mamba	✅	❌	❌
mamba2	✅	❌	❌
Marian	✅	✅	✅
MarkupLM	✅	❌	❌
Mask2Former	✅	❌	❌
MaskFormer	✅	❌	❌
MatCha	✅	❌	❌
mBART	✅	✅	✅
mBART-50	✅	✅	✅
MEGA	✅	❌	❌
Megatron-BERT	✅	❌	❌
Megatron-GPT2	✅	✅	✅
MGP-STR	✅	❌	❌
Mimi	✅	❌	❌
Mistral	✅	✅	✅
Mixtral	✅	❌	❌
mLUKE	✅	❌	❌
MMS	✅	✅	✅
MobileBERT	✅	✅	❌
MobileNetV1	✅	❌	❌
MobileNetV2	✅	❌	❌
MobileViT	✅	✅	❌
MobileViTV2	✅	❌	❌
MPNet	✅	✅	❌
MPT	✅	❌	❌
MRA	✅	❌	❌
MT5	✅	✅	✅
MusicGen	✅	❌	❌
MusicGen Melody	✅	❌	❌
MVP	✅	❌	❌
NAT	✅	❌	❌
Nemotron	✅	❌	❌
Nezha	✅	❌	❌
NLLB	✅	❌	❌
NLLB-MOE	✅	❌	❌
Nougat	✅	✅	✅
Nyströmformer	✅	❌	❌
OLMo	✅	❌	❌
OLMoE	✅	❌	❌
OmDet-Turbo	✅	❌	❌
OneFormer	✅	❌	❌
OpenAI GPT	✅	✅	❌
OpenAI GPT-2	✅	✅	✅
OpenLlama	✅	❌	❌
OPT	✅	✅	✅
OWL-ViT	✅	❌	❌
OWLv2	✅	❌	❌
PaliGemma	✅	❌	❌
PatchTSMixer	✅	❌	❌
PatchTST	✅	❌	❌
Pegasus	✅	✅	✅
PEGASUS-X	✅	❌	❌
Perceiver	✅	❌	❌
Persimmon	✅	❌	❌
Phi	✅	❌	❌
Phi3	✅	❌	❌
PhoBERT	✅	✅	✅
Pix2Struct	✅	❌	❌
Pixtral	✅	❌	❌
PLBart	✅	❌	❌
PoolFormer	✅	❌	❌
Pop2Piano	✅	❌	❌
ProphetNet	✅	❌	❌
PVT	✅	❌	❌
PVTv2	✅	❌	❌
QDQBert	✅	❌	❌
Qwen2	✅	❌	❌
Qwen2Audio	✅	❌	❌
Qwen2MoE	✅	❌	❌
Qwen2VL	✅	❌	❌
RAG	✅	✅	❌
REALM	✅	❌	❌
RecurrentGemma	✅	❌	❌
Reformer	✅	❌	❌
RegNet	✅	✅	✅
RemBERT	✅	✅	❌
ResNet	✅	✅	✅
RetriBERT	✅	❌	❌
RoBERTa	✅	✅	✅
RoBERTa-PreLayerNorm	✅	✅	✅
RoCBert	✅	❌	❌
RoFormer	✅	✅	✅
RT-DETR	✅	❌	❌
RT-DETR-ResNet	✅	❌	❌
RWKV	✅	❌	❌
SAM	✅	✅	❌
SeamlessM4T	✅	❌	❌
SeamlessM4Tv2	✅	❌	❌
SegFormer	✅	✅	❌
SegGPT	✅	❌	❌
SEW	✅	❌	❌
SEW-D	✅	❌	❌
SigLIP	✅	❌	❌
Speech Encoder decoder	✅	❌	✅
Speech2Text	✅	✅	❌
SpeechT5	✅	❌	❌
Splinter	✅	❌	❌
SqueezeBERT	✅	❌	❌
StableLm	✅	❌	❌
Starcoder2	✅	❌	❌
SuperPoint	✅	❌	❌
SwiftFormer	✅	✅	❌
Swin Transformer	✅	✅	❌
Swin Transformer V2	✅	❌	❌
Swin2SR	✅	❌	❌
SwitchTransformers	✅	❌	❌
T5	✅	✅	✅
T5v1.1	✅	✅	✅
Table Transformer	✅	❌	❌
TAPAS	✅	✅	❌
TAPEX	✅	✅	✅
Time Series Transformer	✅	❌	❌
TimeSformer	✅	❌	❌
Trajectory Transformer	✅	❌	❌
Transformer-XL	✅	✅	❌
TrOCR	✅	❌	❌
TVLT	✅	❌	❌
TVP	✅	❌	❌
UDOP	✅	❌	❌
UL2	✅	✅	✅
UMT5	✅	❌	❌
UniSpeech	✅	❌	❌
UniSpeechSat	✅	❌	❌
UnivNet	✅	❌	❌
UPerNet	✅	❌	❌
VAN	✅	❌	❌
VideoLlava	✅	❌	❌
VideoMAE	✅	❌	❌
ViLT	✅	❌	❌
VipLlava	✅	❌	❌
Vision Encoder decoder	✅	✅	✅
VisionTextDualEncoder	✅	✅	✅
VisualBERT	✅	❌	❌
ViT	✅	✅	✅
ViT Hybrid	✅	❌	❌
VitDet	✅	❌	❌
ViTMAE	✅	✅	❌
ViTMatte	✅	❌	❌
ViTMSN	✅	❌	❌
VITS	✅	❌	❌
ViViT	✅	❌	❌
Wav2Vec2	✅	✅	✅
Wav2Vec2-BERT	✅	❌	❌
Wav2Vec2-Conformer	✅	❌	❌
Wav2Vec2Phoneme	✅	✅	✅
WavLM	✅	❌	❌
Whisper	✅	✅	✅
X-CLIP	✅	❌	❌
X-MOD	✅	❌	❌
XGLM	✅	✅	✅
XLM	✅	✅	❌
XLM-ProphetNet	✅	❌	❌
XLM-RoBERTa	✅	✅	✅
XLM-RoBERTa-XL	✅	❌	❌
XLM-V	✅	✅	✅
XLNet	✅	✅	❌
XLS-R	✅	✅	✅
XLSR-Wav2Vec2	✅	✅	✅
YOLOS	✅	❌	❌
YOSO	✅	❌	❌
ZoeDepth	✅	❌	❌

44 KiB Raw Blame History

🤗 Transformers

If you are looking for custom support from the Hugging Face team

Contents

Supported models and frameworks

44 KiB

Raw Blame History