transformers/docs/source/en/index.md
Arthur 0fe44059ae
Add recurrent gemma (#30143)
* Fork.

* RecurrentGemma initial commit.

* Updating __init__.py.

* Minor modification to how we initialize the cache.
Changing how the config specifies the architecture.

* Reformat code to 4 spaces.
Fixed a few typos.

* Fixed the forward pass.
Still unclear on the cache?

* Fixed the RecurrentGemmaForCausalLM

* Minor comment that we might not need attention_mask and output_attention arguments.

* Now cache should work as well.

* Adding a temporary example to check whether the model generation works.

* Adding the tests and updating imports.

* Adding the example file missing in the previous commit.

* First working example.

* Removing .gitignore and reverting parts of __init__.

* Re-add .gitignore.

* Addressing comments for configuration.

* Move mask creation to `_prepare_inputs_for_generation`.

* First try at integration tests:
1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'.
2. `cache_position` not passed

* Transfoering between machines.

* Running normal tests.

* Minor fix.

* More fixes.

* Addressing more comments.

* Minor fixes.

* first stab at cleanup

* more refactoring

* fix copies and else

* renaming and get init to work

* fix causal mask creation

* update

* nit

* fix a hell lot of things

* updates

* update conversion script

* make all keys importable

* nits

* add auto mappings

* properly convert ffw_up and down

* add scaling

* fix generations

* for recurrent dtype

* update

* fix going beyong window

* fixup

* add missing files

* current updates to remove last einops

* finish modeling refactor

* TADA

* fix compile

* fix most failing testt ? ?

* update tests

* refactor and update

* update

* nits, fixup and update tests

* more fixup

* nits

* fix imports

* test format

* fixups

* nits

* tuple typing

* fix code quality

* add model card

* fix doc

* skip most generation tests

* nits

* style

* doc fixes

* fix pr and check_copies?

* last nit

* oupsy

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <hi@lysand.re>

* update

* Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* update based on review

* doc nit

* fix quality

* quality

* fix slow test model path

* update default dype

* ignore attributes that can be safely ignored in check config attributes

* 0lallalala come on

* save nit

* style

* remove to dict update

* make sure we can also run in float16

* style

---------

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Aleksandar Botev <botev@google.com>
Co-authored-by: Leonard Berrada <lberrada@users.noreply.github.com>
Co-authored-by: anushanf <anushanf@google.com>
Co-authored-by: botev <botevmg@gmail.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
2024-04-10 16:59:13 +02:00

40 KiB

🤗 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

HuggingFace Expert Acceleration Program

Contents

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
Chinese-CLIP
CLAP
CLIP
CLIPSeg
CLVP
CodeGen
CodeLlama
Cohere
Conditional DETR
ConvBERT
ConvNeXT
ConvNeXTV2
CPM
CPM-Ant
CTRL
CvT
Data2VecAudio
Data2VecText
Data2VecVision
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
FastSpeech2Conformer
FLAN-T5
FLAN-UL2
FlauBERT
FLAVA
FNet
FocalNet
Funnel Transformer
Fuyu
Gemma
GIT
GLPN
GPT Neo
GPT NeoX
GPT NeoX Japanese
GPT-J
GPT-Sw3
GPTBigCode
GPTSAN-japanese
Graphormer
GroupViT
HerBERT
Hubert
I-BERT
IDEFICS
ImageGPT
Informer
InstructBLIP
Jukebox
KOSMOS-2
LayoutLM
LayoutLMv2
LayoutLMv3
LayoutXLM
LED
LeViT
LiLT
LLaMA
Llama2
LLaVa
LLaVA-NeXT
Longformer
LongT5
LUKE
LXMERT
M-CTC-T
M2M100
MADLAD-400
Mamba
Marian
MarkupLM
Mask2Former
MaskFormer
MatCha
mBART
mBART-50
MEGA
Megatron-BERT
Megatron-GPT2
MGP-STR
Mistral
Mixtral
mLUKE
MMS
MobileBERT
MobileNetV1
MobileNetV2
MobileViT
MobileViTV2
MPNet
MPT
MRA
MT5
MusicGen
MusicGen Melody
MVP
NAT
Nezha
NLLB
NLLB-MOE
Nougat
Nyströmformer
OneFormer
OpenAI GPT
OpenAI GPT-2
OpenLlama
OPT
OWL-ViT
OWLv2
PatchTSMixer
PatchTST
Pegasus
PEGASUS-X
Perceiver
Persimmon
Phi
PhoBERT
Pix2Struct
PLBart
PoolFormer
Pop2Piano
ProphetNet
PVT
PVTv2
QDQBert
Qwen2
Qwen2MoE
RAG
REALM
RecurrentGemma
Reformer
RegNet
RemBERT
ResNet
RetriBERT
RoBERTa
RoBERTa-PreLayerNorm
RoCBert
RoFormer
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
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