94358219cf
* probably approximatelly correct CLIPTextEncoder * implemented CLIPEncoderLayer as built-in nn.TransformerEncoderLayer * replaced embedding layer with simple matrix * implemented ViT * added ViT tests * fixed tests * added pooler_output for text * implemented complete CLIPModel * implemented init * implemented convert.py and from_pretrained * fixed some minor bugs and added the README.md * removed tokenizer unused comments * removed unused deps * updated ACKNOWLEDGEMENTS.md * Feat: Image Processor for CLIP (#1) @nkasmanoff: * clip image processor * added example usage * refactored image preprocessing * deleted unused image_config.py * removed preprocessing port * added dependency to mlx-data * fixed attribution and moved photos to assets * implemented a simple port of CLIPImageProcessor * review changes * PR review changes * renamed too verbose arg * updated README.md * nits in readme / conversion * simplify some stuff, remove unneeded inits * remove more init stuff * more simplify * make test a unit test * update main readme * readme nits --------- Co-authored-by: Noah Kasmanoff <nkasmanoff@gmail.com> Co-authored-by: Awni Hannun <awni@apple.com> |
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|---|---|---|
| bert | ||
| cifar | ||
| clip | ||
| 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
- Generating images with Stable Diffusion.
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},
}