mlx-examples

mirror of https://github.com/ml-explore/mlx-examples.git synced 2025-09-27 01:38:08 +08:00

Files

Anchen 3535408c99 chore(mlx-lm): fix tie_word_embeddings for qwen2 (#566 )

* chore: fix tie_word_embeddings for qwen2

* chore: default tie_word_embeddings to True

2024-03-12 21:34:32 -07:00

examples

Some improvements to LoRA (#528 )

2024-03-12 20:02:03 -07:00

models

chore(mlx-lm): fix tie_word_embeddings for qwen2 (#566 )

2024-03-12 21:34:32 -07:00

tuner

Some improvements to LoRA (#528 )

2024-03-12 20:02:03 -07:00

__init__.py

Fix import warning (#479 )

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convert.py

Fix import warning (#479 )

2024-02-27 08:47:56 -08:00

fuse.py

feat(mlx-lm): add de-quant for fuse.py (#365 )

2024-01-25 18:59:32 -08:00

generate.py

chore(mlx-lm): add adapter support in generate.py (#494 )

2024-02-28 07:49:25 -08:00

LORA.md

YAML configuration for mlx_lm.lora (#503 )

2024-03-08 07:57:52 -08:00

lora.py

Some improvements to LoRA (#528 )

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MERGE.md

Support for slerp merging models (#455 )

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merge.py

Refactoring of mlx_lm example (#501 )

2024-03-06 06:24:31 -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

[mlx-lm] Use sdpa in llama / mistral model (#515 )

2024-03-07 17:41:23 -08:00

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

Refactoring of mlx_lm example (#501 )

2024-03-06 06:24:31 -08:00

UPLOAD.md

Mlx llm package (#301 )

2024-01-12 10:25:56 -08:00

utils.py

chore(mlx-lm): fix tie_word_embeddings for qwen2 (#566 )

2024-03-12 21:34:32 -07:00

version.py

[mlx-lm] Use sdpa in llama / mistral model (#515 )

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README.md

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.