MLX Community

A community org for model weights compatible with mlx-examples powered by MLX.

These are pre-converted weights and ready to be used in the example scripts.

Quick start for LLMs

Install mlx-lm:

pip install mlx-lm

You can use mlx-lm from the command line. For example:

python -m mlx_lm.generate --model mistralai/Mistral-7B-Instruct-v0.1 --prompt "hello"

This will download a Mistral 7B model from the Hugging Face Hub and generate text using the given prompt.

For a full list of options run:

python -m mlx_lm.generate --help

To quantize a model from the command line run:

python -m mlx_lm.convert --hf-path mistralai/Mistral-7B-Instruct-v0.1 -q 

For more options run:

python -m mlx_lm.convert --help

You can upload new models to Hugging Face by specifying --upload-repo to convert. For example, to upload a quantized Mistral-7B model to the MLX Hugging Face community you can do:

python -m mlx_lm.convert \
    --hf-path mistralai/Mistral-7B-Instruct-v0.1 \
    -q \
    --upload-repo mlx-community/my-4bit-mistral

For more details on the API checkout the full README

Other Examples:

For more examples, visit the MLX Examples repo. The repo includes examples of:

• Parameter efficient fine tuning with LoRA

• Speech recognition with Whisper

• Image generation with Stable Diffusion

  and many other examples of different machine learning applications and algorithms.