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Examples in the MLX framework
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| bert | ||
| cifar | ||
| gcn | ||
| llama | ||
| llms/qwen | ||
| lora | ||
| mistral | ||
| mixtral | ||
| mnist | ||
| phi2 | ||
| speechcommands | ||
| stable_diffusion | ||
| t5 | ||
| transformer_lm | ||
| whisper | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| ACKNOWLEDGMENTS.md | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
MLX Examples
This repo contains a variety of standalone examples using the MLX framework.
The MNIST example is a good starting point to learn how to use MLX.
Some more useful examples include:
- Transformer language model training.
- Large scale text generation with LLaMA, Mistral or Phi.
- Mixture-of-experts (MoE) language model with Mixtral 8x7B
- Parameter efficient fine-tuning with LoRA.
- Generating images with Stable Diffusion.
- Speech recognition with OpenAI's Whisper.
- Bidirectional language understanding with BERT
- Semi-supervised learning on graph-structured data with GCN.
Note: You can now directly download a few converted checkpoints from the MLX Community organisation on Hugging Face.
Contributing
We are grateful for all of our contributors. If you contribute to MLX Examples and wish to be acknowledged, please add your name to to the list in your pull request.
Citing MLX Examples
The MLX software suite was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find MLX Examples useful in your research and wish to cite it, please use the following BibTex entry:
@software{mlx2023,
author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
url = {https://github.com/ml-explore},
version = {0.0},
year = {2023},
}