Below are some tips to port Vision LLMs available on Hugging Face to MLX.
Next, 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_vlm/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
llava.
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.
Additionally, add a test for the new modle type to the model tests.
From the src/
directory, you can run the tests with:
python -m unittest discover tests/
-
Fork and submit pull requests to the repo.
-
If you've added code that should be tested, add tests.
-
Every PR should have passing tests and at least one review.
-
For code formatting install
pre-commit
using something likepip install pre-commit
and runpre-commit install
. This should install hooks for runningblack
andclang-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.
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.
By contributing to mlx-examples, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree.