mlx-examples/llms/mlx_lm
2024-03-30 13:13:58 -07:00
..
examples Configurable LR schedulers (#604) 2024-03-29 13:41:10 -07:00
models Configurable LR schedulers (#604) 2024-03-29 13:41:10 -07:00
tuner Configurable LR schedulers (#604) 2024-03-29 13:41:10 -07:00
__init__.py Fix import warning (#479) 2024-02-27 08:47:56 -08:00
convert.py add dequantize option to mlx_lm/convert.py (#547) 2024-03-19 19:50:08 -07:00
fuse.py feat(mlx-lm): export the GGUF (fp16) format model weights from fuse.py (#555) 2024-03-21 10:34:11 -07:00
generate.py chore(mlx-lm): enable to apply default chat template (#577) 2024-03-20 21:39:39 -07:00
gguf.py fix(mlx-lm): type hints in gguf.py (#621) 2024-03-26 07:56:01 -07:00
LORA.md feat(mlx-lm): export the GGUF (fp16) format model weights from fuse.py (#555) 2024-03-21 10:34:11 -07:00
lora.py Configurable LR schedulers (#604) 2024-03-29 13:41:10 -07:00
MERGE.md Support for slerp merging models (#455) 2024-02-19 20:37:15 -08:00
merge.py feat: add update_config functionality (#531) 2024-03-14 06:36:05 -07:00
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 Switch to fast RMS/LN Norm (#603) 2024-03-23 07:13:51 -07:00
sample_utils.py fix(mlx-lm): sorted probs in top_p implementation. (#610) 2024-03-25 15:07:55 -07:00
SERVER.md Prevent llms/mlx_lm from serving the local directory as a webserver (#498) 2024-02-27 19:40:42 -08:00
server.py Set finish_reason in response (#592) 2024-03-19 20:21:26 -07:00
UPLOAD.md Mlx llm package (#301) 2024-01-12 10:25:56 -08:00
utils.py cleanup whisper a little (#639) 2024-03-30 13:13:58 -07:00
version.py feat(mlx-lm): export the GGUF (fp16) format model weights from fuse.py (#555) 2024-03-21 10:34:11 -07:00

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.