Contribute to 🤗 Transformers

Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.

It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you.

However you choose to contribute, please be mindful and respect our code of conduct.

This guide was heavily inspired by the awesome scikit-learn guide to contributing.

Ways to contribute

There are several ways you can contribute to 🤗 Transformers:

Fix outstanding issues with the existing code.
Submit issues related to bugs or desired new features.
Implement new models.
Contribute to the examples or to the documentation.

If you don't know where to start, there is a special Good First Issue listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over.

For something slightly more challenging, you can also take a look at the Good Second Issue list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀

All contributions are equally valuable to the community. 🥰

Fixing outstanding issues

If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!

Submitting a bug-related issue or feature request

Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

Did you find a bug?

The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter.

Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the forum or on our discord first. This helps us respond quicker to fixing issues related to the library versus general questions.

Tip

We have a docs bot, and we highly encourage you to ask all your questions there. There is always a chance your bug can be fixed with a simple flag 👾🔫

Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it:

Your OS type and version and Python, and PyTorch versions when applicable.
A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s.
The full traceback if an exception is raised.
Attach any other additional information, like screenshots, you think may help.

To get the OS and software versions automatically, run the following command:

transformers env

You can also run the same command from the root of the repository:

python src/transformers/commands/transformers_cli.py env

Do you want a new feature?

If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe:

What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community?

Whatever it is, we'd love to hear about it!
Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
Provide a code snippet that demonstrates the features usage.
If the feature is related to a paper, please include a link.

If your issue is well written we're already 80% of the way there by the time you create it.

We have added templates to help you get started with your issue.

Do you want to implement a new model?

New models are constantly released and if you want to implement a new model, please provide the following information:

A short description of the model and a link to the paper.
Link to the implementation if it is open-sourced.
Link to the model weights if they are available.

If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers!

We have a technical guide for how to add a model to 🤗 Transformers.

Vision-Language Model Contribution Checklist

If you're contributing a vision-language model (or any multimodal model that processes images/videos), please follow this checklist. Maintainers will use this to review your PR, and completing these steps will significantly increase the likelihood of your PR being merged quickly.

Required checklist for all vision-language model contributions:

☐ 1. Implement a modular file

All new models should use the modular architecture pattern. Create a modular_<model_name>.py file using the modular model converter:

Use the CLI, transformers add-new-model-like to generate a modular skeleton and get started
All code should be in the modular file if possible. Modeling must be in it, it's better if configuration is in it as well.
Reuse existing patterns from similar models as much as possible

To verify your modular file is correct, run:

python utils/modular_model_converter.py <model_name>

This will generate the separate files (modeling_*.py, configuration_*.py, etc.) from your modular file. The CI will enforce that these generated files match your modular file.

☐ 2. Add a fast image processor (for image models)

If your model processes images, implement a fast image processor that uses torch and torchvision instead of PIL/numpy for better inference performance:

See the detailed guide in #36978
Fast processors inherit from BaseImageProcessorFast
Examples: LlavaOnevisionImageProcessorFast, Idefics2ImageProcessorFast

☐ 3. Create a weight conversion script

Add a convert_<model_name>_to_hf.py script that converts the original model weights to the HuggingFace format:

Script should handle checkpoint loading, key mapping, and saving in HF format
Include usage examples and documentation in the script
Examples: convert_llava_onevision_weights_to_hf.py, convert_idefics2_weights_to_hf.py

☐ 4. Add integration tests with exact output matching

At minimum, add an IntegrationTest class that tests end-to-end generation (processing and modelling) with exact output matching:

For generative models: test that generated text matches expected output exactly
For non-generative models: test that output logits match expected values
Tests should use real checkpoints (load in 4-bit or half precision if the checkpoint is too big to fit in our CI runners) and real inputs
Example pattern:

class MyModelIntegrationTest(unittest.TestCase):
    @slow
    def test_model_integration(self):
        model = MyModelForConditionalGeneration.from_pretrained("org/model-name")
        processor = AutoProcessor.from_pretrained("org/model-name")

        inputs = processor(images=image, text=prompt, return_tensors="pt")
        output = model.generate(**inputs, max_new_tokens=20)

        EXPECTED_TEXT = "exact expected output"
        self.assertEqual(processor.decode(output[0]), EXPECTED_TEXT)

See tests/models/llava_onevision/test_modeling_llava_onevision.py for complete examples.

☐ 5. Update documentation

Add or update model documentation:

Create if the cli hasn't docs/source/en/model_doc/<model_name>.md with usage examples
Include model description, paper link, and basic usage with Pipeline and AutoModel
Add the model to the appropriate TOC files

☐ 6. Look for reusable patterns

The library has 400+ models with many established patterns:

Search for similar models (e.g., other vision-language models)
Reuse attention mechanisms, layer implementations, and processing patterns
Check models like LLaVA, Idefics2, Fuyu for vision-language patterns
Use provided decorators like (auto_docstring, can_return_tuple, check_model_inputs and _can_record_outputs) where relevant.
Don't reinvent the wheel

☐ 7. Run quality checks and read the output

Before submitting your PR, install quality dependencies and run the full check suite:

pip install -e ".[quality]"
make fixup

Important: Take time to read the output of make fixup. It will:

Lint and format your code automatically
Run consistency checks (imports, docstrings, etc.)
Show any remaining issues that need manual fixes

All checks must pass before your PR can be merged.

If this checklist is complete, your PR has a very high likelihood of being merged! Following these steps makes the maintainers' work much easier and will reduce the number of review iterations, getting your important work out there faster.

Copy-pastable checklist for maintainers

Here's a condensed version maintainers can copy into PRs:

## Multimodal Model Addition Checklist

Please ensure your PR completes all following items. See the [full checklist](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#vision-language-model-contribution-checklist) for details.

- [ ] **Modular file**: `modular_<model_name>.py` implemented and verified with `python utils/modular_model_converter.py <model_name>`
- [ ] **Fast image processor**: Implemented using `BaseImageProcessorFast` (see [#36978](https://github.com/huggingface/transformers/issues/36978))
- [ ] **Conversion script**: `convert_<model_name>_to_hf.py` added with usage examples
- [ ] **Integration tests**: End-to-end tests with exact output matching (text or logits)
- [ ] **Documentation**: Model docs added/updated in `docs/source/en/model_doc/`
- [ ] **Pattern reuse**: Verified against similar models (LLaVA, Idefics2, etc.)
- [ ] **Quality checks**: `make fixup` passes with no errors

Do you want to add documentation?

We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested!

For more details about how to generate, build, and write the documentation, take a look at the documentation README.

Create a Pull Request

Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.

You will need basic git proficiency to contribute to 🤗 Transformers. While git is not the easiest tool to use, it has the greatest manual. Type git --help in a shell and enjoy! If you prefer books, Pro Git is a very good reference.

You'll need Python 3.9 or above to contribute to 🤗 Transformers. Follow the steps below to start contributing:

Fork the repository by clicking on the Fork button on the repository's page. This creates a copy of the code under your GitHub user account.

Clone your fork to your local disk, and add the base repository as a remote:

git clone git@github.com:<your Github handle>/transformers.git
cd transformers
git remote add upstream https://github.com/huggingface/transformers.git

Create a new branch to hold your development changes:
```
git checkout -b a-descriptive-name-for-my-changes
```
🚨 Do not work on the main branch!
Set up a development environment by running the following command in a virtual environment:
```
pip install -e ".[dev]"
```
If 🤗 Transformers was already installed in the virtual environment, remove it with pip uninstall transformers before reinstalling it in editable mode with the -e flag.

Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a failure with this command. If that's the case make sure to install Pytorch then do:
```
pip install -e ".[quality]"
```
which should be enough for most use cases.
Develop the features in your branch.

As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this:
```
pytest tests/<TEST_TO_RUN>.py
```
For more information about tests, check out the Testing guide.

🤗 Transformers relies on black and ruff to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with:
```
make fixup
```
This target is also optimized to only work with files modified by the PR you're working on.

If you prefer to run the checks one after the other, the following command applies the style corrections:
```
make style
```
🤗 Transformers also uses ruff and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with:
```
make quality
```
Finally, we have a lot of scripts to make sure we don't forget to update some files when adding a new model. You can run these scripts with:
```
make repo-consistency
```
To learn more about those checks and how to fix any issues with them, check out the Checks on a Pull Request guide.

If you're modifying documents under the docs/source directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder.
```
pip install hf-doc-builder
```
Run the following command from the root of the repository:
```
doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build
```
This will build the documentation in the ~/tmp/test-build folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request.

Once you're happy with your changes, add the changed files with git add and record your changes locally with git commit:
```
git add modified_file.py
git commit
```
Please remember to write good commit messages to clearly communicate the changes you made!

To keep your copy of the code up to date with the original repository, rebase your branch on upstream/branch before you open a pull request or if requested by a maintainer:
```
git fetch upstream
git rebase upstream/main
```
Push your changes to your branch:
```
git push -u origin a-descriptive-name-for-my-changes
```
If you've already opened a pull request, you'll need to force push with the --force flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally.
Now you can go to your fork of the repository on GitHub and click on Pull Request to open a pull request. Make sure you tick off all the boxes on our checklist below. When you're ready, you can send your changes to the project maintainers for review.
It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.

Pull request checklist

☐ The pull request title should summarize your contribution.
☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).
☐ To indicate a work in progress please prefix the title with [WIP]. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
☐ Make sure existing tests pass.
☐ If adding a new feature, also add tests for it.

If you are adding a new model, make sure you use ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...) to trigger the common tests.
If you are adding new @slow tests, make sure they pass using RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py.
If you are adding a new tokenizer, write tests and make sure RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py passes.
CircleCI does not run the slow tests, but GitHub Actions does every night!

☐ All public methods must have informative docstrings (see modeling_bert.py for an example).
☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as hf-internal-testing to host these files and reference them by URL. We recommend placing documentation related images in the following repository: huggingface/documentation-images. You can open a PR on this dataset repository and ask a Hugging Face member to merge it.

For more information about the checks run on a pull request, take a look at our Checks on a Pull Request guide.

Tests

An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder and examples tests in the examples folder.

We like pytest and pytest-xdist because it's faster. From the root of the repository, specify a path to a subfolder or a test file to run the test:

python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model

Similarly, for the examples directory, specify a path to a subfolder or test file to run the test. For example, the following command tests the text classification subfolder in the PyTorch examples directory:

pip install -r examples/xxx/requirements.txt  # only needed the first time
python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification

In fact, this is actually how our make test and make test-examples commands are implemented (not including the pip install)!

You can also specify a smaller set of tests in order to test only the feature you're working on.

By default, slow tests are skipped but you can set the RUN_SLOW environment variable to yes to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience!

Remember to specify a path to a subfolder or a test file to run the test. Otherwise, you'll run all the tests in the tests or examples folder, which will take a very long time!

RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model
RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification

Like the slow tests, there are other environment variables available which are not enabled by default during testing:

RUN_CUSTOM_TOKENIZERS: Enables tests for custom tokenizers.

More environment variables and additional information can be found in the testing_utils.py.

🤗 Transformers uses pytest as a test runner only. It doesn't use any pytest-specific features in the test suite itself.

This means unittest is fully supported. Here's how to run tests with unittest:

python -m unittest discover -s tests -t . -v
python -m unittest discover -s examples -t examples -v

Style guide

For documentation strings, 🤗 Transformers follows the Google Python Style Guide. Check our documentation writing guide for more information.

Develop on Windows

On Windows (unless you're working in Windows Subsystem for Linux or WSL), you need to configure git to transform Windows CRLF line endings to Linux LF line endings:

git config core.autocrlf input

One way to run the make command on Windows is with MSYS2:

Download MSYS2, and we assume it's installed in C:\msys64.
Open the command line C:\msys64\msys2.exe (it should be available from the Start menu).
Run in the shell: pacman -Syu and install make with pacman -S make.
Add C:\msys64\usr\bin to your PATH environment variable.

You can now use make from any terminal (PowerShell, cmd.exe, etc.)! 🎉

Sync a forked repository with upstream main (the Hugging Face repository)

When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs.

When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.

If a PR is absolutely necessary, use the following steps after checking out your branch:

git checkout -b your-branch-for-syncing
git pull --squash --no-commit upstream main
git commit -m '<your message without GitHub references>'
git push --set-upstream origin your-branch-for-syncing

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