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* Update README.md The default behaviour of where the convert.py saved files was wrong. It also was inconsistent with how the later script test.py is trying to use them (and assuming naming convention). I don't actually see a quick way to automate this since--as written--the target directory is set directly by an argument. It would probably be best to rewrite it so that the argument is used as an override variable, but the default behaviour is to construct a file path based on set and unset arugments. This also is complex because "defaults" are assumed in the naming convention as well. * Update README.md Created an actual script that'll run and do this correctly. * Update README.md Typo fix: mlx-models should have been mlx_models. This conforms with standard later in the mlx-examples/whisper code. * Update README.md Removed the larger script and changed it back to the simpler script as before. * nits in readme --------- Co-authored-by: Awni Hannun <awni@apple.com>
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 are listed below.
Text Models
- Transformer language model training.
- Large scale text generation with LLaMA, Mistral, Phi-2, and more in the LLMs directory.
- A mixture-of-experts (MoE) language model with Mixtral 8x7B.
- Parameter efficient fine-tuning with LoRA or QLoRA.
- Text-to-text multi-task Transformers with T5.
- Bidirectional language understanding with BERT.
Image Models
- Image classification using ResNets on CIFAR-10.
- Generating images with Stable Diffusion.
- Convolutional variational autoencoder (CVAE) on MNIST.
Audio Models
- Speech recognition with OpenAI's Whisper.
Multimodal models
Other Models
- Semi-supervised learning on graph-structured data with GCN.
- Real NVP normalizing flow for density estimation and sampling.
Hugging Face
Note: You can now directly download a few converted checkpoints from the MLX Community organization on Hugging Face. We encourage you to join the community and contribute new models.
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 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},
}