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* initial commit * style fixes * update of ACKNOWLEDGMENTS * fixed comment * minor refactoring; removed unused imports * added cifar and cvae to top-level README.md * removed mention of cuda/mps in argparse * fixed training status output * load_weights() with strict=True * pretrained model update * fixed imports and style * requires mlx>=0.0.9 * updated with results using mlx 0.0.9 * removed mention of private repo * simplify and combine to one file, more consistency with other exmaples * few more nits * nits * spell * format --------- Co-authored-by: Awni Hannun <awni@apple.com>
71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
# MLX Examples
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This repo contains a variety of standalone examples using the [MLX
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framework](https://github.com/ml-explore/mlx).
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The [MNIST](mnist) example is a good starting point to learn how to use MLX.
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Some more useful examples are listed below.
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### Text Models
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- [Transformer language model](transformer_lm) training.
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- Large scale text generation with [LLaMA](llms/llama),
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[Mistral](llms/mistral), [Phi-2](llms/phi2), and more in the [LLMs](llms)
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directory.
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- A mixture-of-experts (MoE) language model with [Mixtral 8x7B](llms/mixtral).
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- Parameter efficient fine-tuning with [LoRA or QLoRA](lora).
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- Text-to-text multi-task Transformers with [T5](t5).
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- Bidirectional language understanding with [BERT](bert).
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### Image Models
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- Image classification using [ResNets on CIFAR-10](cifar).
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- Generating images with [Stable Diffusion](stable_diffusion).
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- Convolutional variational autoencoder [(CVAE) on MNIST](cvae).
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### Audio Models
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- Speech recognition with [OpenAI's Whisper](whisper).
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### Multimodal models
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- Joint text and image embeddings with [CLIP](clip).
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### Other Models
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- Semi-supervised learning on graph-structured data with [GCN](gcn).
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- Real NVP [normalizing flow](normalizing_flow) for density estimation and
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sampling.
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### Hugging Face
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Note: You can now directly download a few converted checkpoints from the [MLX
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Community](https://huggingface.co/mlx-community) organization on Hugging Face.
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We encourage you to join the community and [contribute new
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models](https://github.com/ml-explore/mlx-examples/issues/155).
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## Contributing
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We are grateful for all of [our
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contributors](ACKNOWLEDGMENTS.md#Individual-Contributors). If you contribute
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to MLX Examples and wish to be acknowledged, please add your name to the list in your
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pull request.
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## Citing MLX Examples
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The MLX software suite was initially developed with equal contribution by Awni
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Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find
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MLX Examples useful in your research and wish to cite it, please use the following
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BibTex entry:
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```
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@software{mlx2023,
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author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
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title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
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url = {https://github.com/ml-explore},
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version = {0.0},
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year = {2023},
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}
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```
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