85c1ff8fd6
* Convert HF weights of PLaMo and load it to a plamo model in mlx * Fix model inference part * Add bos at the beginning of the prompt * Fix convert.py to copy tokenizer.model into the converted dir * Use the required insturction format in generate.py when "--instruct" option is specified * Change filenames and update existing scripts * Add README * Add requirements.txt * Fix plamo.py to stop generation when EOS appears * Add quantization to convert.py * Use mlx>=0.0.9 for mx.core.outer() in PLaMo model * Update acknowledgements.md * Fix card text in upload_to_hub() * Not use prompt template when --instruct is not specified * Ask if you trust_remote_code for loading tokenizer of PLaMo * Check the user trusts the remote code when converting * Remove plamo directory * Update README * Add PLaMo model file * Fix the handling of cache in PLaMo and update README * Ask if trust_remote_code only when the model is PLaMo * Remove resolve_trust_remote_code from convert.py and use the latest transformers * Remove code not to add EOS * Update README to fix an example not to use noncommercial version of the model * Remove unused imports * Remove unnecessary description about the instruct model of PLaMo from README * format, nits in README * typo --------- Co-authored-by: Shunta Saito <shunta@mitmul-mbp.local> Co-authored-by: Awni Hannun <awni@apple.com> |
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| bert | ||
| cifar | ||
| 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.
Other Models
- Semi-supervised learning on graph-structured data with GCN.
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},
}