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Contributing to MLX LM

We want to make contributing to this project as easy and transparent as possible.

Pull Requests

  1. Fork and submit pull requests to the repo.

  2. If you've added code that should be tested, add tests.

  3. Every PR should have passing tests and at least one review.

  4. For code formatting install pre-commit using something like pip install pre-commit and run pre-commit install. This should install hooks for running black and clang-format to ensure consistent style for C++ and python code.

    You can also run the formatters manually as follows on individual files:

    clang-format -i file.cpp
    black file.py

    or,

    # single file
pre-commit run --files file1.py 

# specific files
pre-commit run --files file1.py file2.py

    or run pre-commit run --all-files to check all files in the repo.

Issues

We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue.

License

By contributing to mlx-lm, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree.

Adding New Models

Below are some tips to port LLMs available on Hugging Face to MLX.

From this directory, do an editable install:

pip install -e .

Then check if the model has weights in the safetensors format. If not follow instructions to convert it.

After that, add the model file to the mlx_lm/models directory. You can see other examples there. We recommend starting from a model that is similar to the model you are porting.

Make sure the name of the new model file is the same as the model_type in the config.json, for example starcoder2.

To determine the model layer names, we suggest either:

  • Refer to the Transformers implementation if you are familiar with the codebase.
  • Load the model weights and check the weight names which will tell you about the model structure.
  • Look at the names of the weights by inspecting model.safetensors.index.json in the Hugging Face repo.

To add LoRA support edit mlx_lm/tuner/utils.py

Finally, add a test for the new modle type to the model tests.

You can run the tests with:

python -m unittest discover tests/