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https://github.com/ml-explore/mlx-examples
Examples in the MLX framework
https://github.com/ml-explore/mlx-examples
mlx
Last synced: 9 days ago
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Examples in the MLX framework
- Host: GitHub
- URL: https://github.com/ml-explore/mlx-examples
- Owner: ml-explore
- License: mit
- Created: 2023-11-28T23:37:49.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T00:40:08.000Z (10 days ago)
- Last Synced: 2024-10-30T02:52:25.912Z (10 days ago)
- Topics: mlx
- Language: Python
- Homepage:
- Size: 7.01 MB
- Stars: 6,103
- Watchers: 71
- Forks: 868
- Open Issues: 131
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# MLX Examples
This repo contains a variety of standalone examples using the [MLX
framework](https://github.com/ml-explore/mlx).
The [MNIST](mnist) example is a good starting point to learn how to use MLX.
Some more useful examples are listed below.
### Text Models
- [MLX LM](llms/README.md) a package for LLM text generation, fine-tuning, and more.
- [Transformer language model](transformer_lm) training.
- Minimal examples of large scale text generation with [LLaMA](llms/llama),
[Mistral](llms/mistral), and more in the [LLMs](llms) directory.
- A mixture-of-experts (MoE) language model with [Mixtral 8x7B](llms/mixtral).
- Parameter efficient fine-tuning with [LoRA or QLoRA](lora).
- Text-to-text multi-task Transformers with [T5](t5).
- Bidirectional language understanding with [BERT](bert).
### Image Models
- Generating images
- [FLUX](flux)
- [Stable Diffusion or SDXL](stable_diffusion)
- Image classification using [ResNets on CIFAR-10](cifar).
- Convolutional variational autoencoder [(CVAE) on MNIST](cvae).
### Audio Models
- Speech recognition with [OpenAI's Whisper](whisper).
- Audio compression and generation with [Meta's EnCodec](encodec).
### Multimodal models
- Joint text and image embeddings with [CLIP](clip).
- Text generation from image and text inputs with [LLaVA](llava).
- Image segmentation with [Segment Anything (SAM)](segment_anything).
### Other Models
- Semi-supervised learning on graph-structured data with [GCN](gcn).
- Real NVP [normalizing flow](normalizing_flow) for density estimation and
sampling.
### Hugging Face
Note: You can now directly download a few converted checkpoints from the [MLX
Community](https://huggingface.co/mlx-community) organization on Hugging Face.
We encourage you to join the community and [contribute new
models](https://github.com/ml-explore/mlx-examples/issues/155).
## Contributing
We are grateful for all of [our
contributors](ACKNOWLEDGMENTS.md#Individual-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},
}
```