
* current changes
* nit
* Add cross_attenttion_mask to processor
* multi-image fixed
* Add cross_attenttion_mask to processor
* cross attn works in all cases
* WIP refactoring function for image processor
* WIP refactoring image processor functions
* Refactor preprocess to use global loops instead of list nested list comps
* Docstrings
* Add channels unification
* fix dtype issues
* Update docsrings and format
* Consistent max_image_tiles
* current script
* updates
* Add convert to rgb
* Add image processor tests
* updates!
* update
* god damn it I am dumb sometimes
* Precompute aspect ratios
* now this works, full match
* fix 😉
* nits
* style
* fix model and conversion
* nit
* nit
* kinda works
* hack for sdpa non-contiguous bias
* nits here and there
* latest c hanges
* merge?
* run forward
* Add aspect_ratio_mask
* vision attention mask
* update script and config variable names
* nit
* nits
* be able to load
* style
* nits
* there
* nits
* make forward run
* small update
* enable generation multi-turn
* nit
* nit
* Clean up a bit for errors and typos
* A bit more constant fixes
* 90B keys and shapes match
* Fix for 11B model
* Fixup, remove debug part
* Docs
* Make max_aspect_ratio_id to be minimal
* Update image processing code to match new implementation
* Adjust conversion for final checkpoint state
* Change dim in repeat_interleave (accordig to meta code)
* tmp fix for num_tiles
* Fix for conversion (gate<->up, q/k_proj rope permute)
* nits
* codestyle
* Vision encoder fixes
* pass cross attn mask further
* Refactor aspect ratio mask
* Disable text-only generation
* Fix cross attention layers order, remove q/k norm rotation for cross atention layers
* Refactor gated position embeddings
* fix bugs but needs test with new weights
* rope scaling should be llama3
* Fix rope scaling name
* Remove debug for linear layer
* fix copies
* Make mask prepare private func
* Remove linear patch embed
* Make precomputed embeddings as nn.Embedding module
* MllamaPrecomputedAspectRatioEmbedding with config init
* Remove unused self.output_dim
* nit, intermediate layers
* Rename ln and pos_embed
* vision_chunk_size -> image_size
* return_intermediate -> intermediate_layers_indices
* vision_input_dim -> hidden_size
* Fix copied from statements
* fix most tests
* Fix more copied from
* layer_id->layer_idx
* Comment
* Fix tests for processor
* Copied from for _prepare_4d_causal_attention_mask_with_cache_position
* Style fix
* Add MllamaForCausalLM
* WIP fixing tests
* Remove duplicated layers
* Remove dummy file
* Fix style
* Fix consistency
* Fix some TODOs
* fix language_model instantiation, add docstring
* Move docstring, remove todos for precomputed embeds (we cannot init them properly)
* Add initial docstrings
* Fix
* fix some tests
* lets skip these
* nits, remove print, style
* Add one more copied from
* Improve test message
* Make validate func private
* Fix dummy objects
* Refactor `data_format` a bit + add comment
* typos/nits
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
* fix dummy objects and imports
* Add chat template config json
* remove num_kv_heads from vision attention
* fix
* move some commits and add more tests
* fix test
* Remove `update_key_name` from modeling utils
* remove num-kv-heads again
* some prelimiary docs
* Update chat template + tests
* nit, conversion script max_num_tiles from params
* Fix warning for text-only generation
* Update conversion script for instruct models
* Update chat template in converstion + test
* add tests for CausalLM model
* model_max_length, avoid null chat_template
* Refactor conversion script
* Fix forward
* Fix integration tests
* Refactor vision config + docs
* Fix default
* Refactor text config
* Doc fixes
* Remove unused args, fix docs example
* Squashed commit of the following:
commit b51ce5a2efffbecdefbf6fc92ee87372ec9d8830
Author: qubvel <qubvel@gmail.com>
Date: Wed Sep 18 13:39:15 2024 +0000
Move model + add output hidden states and output attentions
* Fix num_channels
* Add mllama text and mllama vision models
* Fixing repo consistency
* Style fix
* Fixing repo consistency
* Fixing unused config params
* Fix failed tests after refactoring
* hidden_activation -> hidden_act for text mlp
* Remove from_pretrained from sub-configs
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/mllama/convert_mllama_weights_to_hf.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Reuse lambda in conversion script
* Remove run.py
* Update docs/source/en/model_doc/mllama.md
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/mllama/processing_mllama.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove unused LlamaTokenizerFast
* Fix logging
* Refactor gating
* Remove cycle for collecting intermediate states
* Refactor text-only check, add integration test for text-only
* Revert from pretrained to configs
* Fix example
* Add auto `bos_token` adding in processor
* Fix tips
* Update src/transformers/models/auto/tokenization_auto.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Enable supports_gradient_checkpointing model flag
* add eager/sdpa options
* don't skip attn tests and bring back GC skips (did i really remove those?)
* Fix signature, but get error with None gradient
* Fix output attention tests
* Disable GC back
* Change no split modules
* Fix dropout
* Style
* Add Mllama to sdpa list
* Add post init for vision model
* Refine config for MllamaForCausalLMModelTest and skipped tests for CausalLM model
* if skipped, say it, don't pass
* Clean vision tester config
* Doc for args
* Update tests/models/mllama/test_modeling_mllama.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add cross_attention_mask to test
* typehint
* Remove todo
* Enable gradient checkpointing
* Docstring
* Style
* Fixing and skipping some tests for new cache
* Mark flaky test
* Skip `test_sdpa_can_compile_dynamic` test
* Fixing some offload tests
* Add direct GenerationMixin inheritance
* Remove unused code
* Add initializer_range to vision config
* update the test to make sure we show if split
* fix gc?
* Fix repo consistency
* Undo modeling utils debug changes
* Fix link
* mllama -> Mllama
* [mllama] -> [Mllama]
* Enable compile test for CausalLM model (text-only)
* Fix TextModel prefix
* Update doc
* Docs for forward, type hints, and vision model prefix
* make sure to reset
* fix init
* small script refactor and styling
* nit
* updates!
* some nits
* Interpolate embeddings for 560 size and update integration tests
* nit
* does not suppor static cache!
* update
* fix
* nit2
* this?
* Fix conversion
* Style
* 4x memory improvement with image cache AFAIK
* Token decorator for tests
* Skip failing tests
* update processor errors
* fix split issues
* style
* weird
* style
* fix failing tests
* update
* nit fixing the whisper tests
* fix path
* update
---------
Co-authored-by: raushan <raushan@huggingface.co>
Co-authored-by: pavel <ubuntu@ip-10-90-0-11.ec2.internal>
Co-authored-by: qubvel <qubvel@gmail.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
44 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

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.