mlx-examples/llms/mlx_lm
Markus Enzweiler 9b387007ab
Example of a Convolutional Variational Autoencoder (CVAE) on MNIST (#264)
* 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

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Co-authored-by: Awni Hannun <awni@apple.com>
2024-02-06 20:02:27 -08:00
..
models Example of a Convolutional Variational Autoencoder (CVAE) on MNIST (#264) 2024-02-06 20:02:27 -08:00
tuner fix(mlx-lm): apply lora layer doesn't update the lora weights (#396) 2024-01-31 11:51:26 -08:00
__init__.py Mlx llm package (#301) 2024-01-12 10:25:56 -08:00
convert.py feat: move lora into mlx-lm (#337) 2024-01-23 08:44:37 -08:00
fuse.py feat(mlx-lm): add de-quant for fuse.py (#365) 2024-01-25 18:59:32 -08:00
generate.py fix the chinese character generation as same as PR #321 (#342) 2024-01-23 12:44:23 -08:00
LORA.md feat: move lora into mlx-lm (#337) 2024-01-23 08:44:37 -08:00
lora.py Olmo in MLX LM (#415) 2024-02-05 21:13:49 -08:00
py.typed Add py.typed to support PEP-561 (type-hinting) (#389) 2024-01-30 21:17:38 -08:00
README.md feat: move lora into mlx-lm (#337) 2024-01-23 08:44:37 -08:00
requirements.txt refactor(qwen): moving qwen into mlx-lm (#312) 2024-01-22 15:00:07 -08:00
UPLOAD.md Mlx llm package (#301) 2024-01-12 10:25:56 -08:00
utils.py chore(mlx-lm): add model weight index in save_weights (#413) 2024-02-06 05:32:15 -08:00

Generate Text with MLX and 🤗 Hugging Face

This an example of large language model text generation that can pull models from the Hugging Face Hub.

For more information on this example, see the README in the parent directory.

This package also supports fine tuning with LoRA or QLoRA. For more information see the LoRA documentation.