mlx-examples

mirror of https://github.com/ml-explore/mlx-examples.git synced 2025-09-01 04:14:38 +08:00

Files

madroid 85dc76f6e0 Server: support stream_options (#913 )

* Server: support stream_options

see https://x.com/OpenAIDevs/status/1787573348496773423

* Server: support stream_options

* Server: check None type

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examples

Example of response generation with optional arguments (#853 )

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models

Unify attention mask in LLMs (#911 )

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tuner

Add GPT-neox model (#863 )

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__init__.py

mlx_lm: Add Streaming Capability to Generate Function (#807 )

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convert.py

Create executables for generate, lora, server, merge, convert (#682 )

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fuse.py

Block sparse MM MoEs (#782 )

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generate.py

mlx_lm: Add Streaming Capability to Generate Function (#807 )

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gguf.py

fix(mlx-lm): type hints in gguf.py (#621 )

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LORA.md

Configuration-based use of HF hub-hosted datasets for training (#701 )

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lora.py

Pass use_dora parameter to linear_to_lora_layers (#885 )

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MANAGE.md

Add model management functionality for local caches (#736 )

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manage.py

Add model management functionality for local caches (#736 )

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MERGE.md

Create executables for generate, lora, server, merge, convert (#682 )

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merge.py

Create executables for generate, lora, server, merge, convert (#682 )

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py.typed

Add py.typed to support PEP-561 (type-hinting) (#389 )

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README.md

feat: move lora into mlx-lm (#337 )

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requirements.txt

Example of response generation with optional arguments (#853 )

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sample_utils.py

Use async eval (#670 )

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SERVER.md

Logprobs info to completion API (#806 )

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server.py

Server: support stream_options (#913 )

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tokenizer_utils.py

fix yi (#852 )

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UPLOAD.md

Mlx llm package (#301 )

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utils.py

support load model by custom get_model_classes (#899 )

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version.py

Configuration-based use of HF hub-hosted datasets for training (#701 )

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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.