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* feat(mlx-lm): add openAI like api server * chore: fix sse format * chore: add top_p support * chore: fix the load import * chore: add workground for missing space in stream decoding * chore: fix typo * chore: add error handling for streaming * chore: using slicing instead of replace * chore: set host, port via args and improve handle stream token logic * chore: refactor stop sequence function * chore: rename stopping_criteria * fix: unable to load kernel contiguous_scan_inclusive_sum_bfloat16_bfloat16 * chore: fix the streaming unicode issue * Update llms/mlx_lm/server.py Co-authored-by: Awni Hannun <awni.hannun@gmail.com> * refacotr: move stopping_criteria out of generate func --------- Co-authored-by: Awni Hannun <awni.hannun@gmail.com> |
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| bert | ||
| cifar | ||
| clip | ||
| cvae | ||
| gcn | ||
| llms | ||
| lora | ||
| mnist | ||
| normalizing_flow | ||
| 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 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
- Joint text and image embeddings with CLIP.
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},
}