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* Add hf_dataset configuration for using HF hub-hosted datasets for (Q)LoRA training * Pre-commit formatting * Fix YAML config example * Print DS info * Include name * Add hf_dataset parameter default * Remove TextHFDataset and CompletionsHFDataset and use Dataset and CompletionsDataset instead, adding a text_key constructor argument to the former (and changing it to work with a provided data structure instead of just from a JSON file), and prompt_key and completion_key arguments to the latter with defaults for backwards compatibility. * nits * update docs --------- Co-authored-by: Awni Hannun <awni@apple.com> |
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| cifar | ||
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| gcn | ||
| llava | ||
| llms | ||
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| mnist | ||
| normalizing_flow | ||
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| 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 are listed below.
Text Models
- MLX LM a package for LLM text generation, fine-tuning, and more.
- Transformer language model training.
- Minimal examples of large scale text generation with LLaMA, Mistral, 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 or SDXL.
- 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},
}