MLX Community

A community org for MLX model weights that run on Apple Silicon. This organization hosts ready-to-use models compatible with:

• mlx-examples – a Python and CLI to run multiple types of models, including LLMs, image models, audio models, and more.
• mlx-swift-examples – a Swift package to run MLX models.
• mlx-vlm – package for inference and fine-tuning of Vision Language Models (VLMs) using MLX.

These are pre-converted weights, ready to use in the example scripts or integrate in your apps.

Quick start for LLMs

Install mlx-lm:

pip install mlx-lm

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

mlx_lm.generate --model mlx-community/Mistral-7B-Instruct-v0.3-4bit --prompt "hello"

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

To chat with an LLM use:

mlx_lm.chat

This will give you a chat REPL that you can use to interact with the LLM. The chat context is preserved during the lifetime of the REPL.

For a full list of options run --help on the command of your interest, for example:

mlx_lm.chat --help

Conversion and Quantization

To quantize a model from the command line run:

mlx_lm.convert --hf-path mistralai/Mistral-7B-Instruct-v0.3 -q 

For more options run:

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:

mlx_lm.convert \
    --hf-path mistralai/Mistral-7B-Instruct-v0.3 \
    -q \
    --upload-repo mlx-community/my-4bit-mistral

Models can also be converted and quantized directly in the mlx-my-repo Hugging Face Space.

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:

• Image generation with Flux and Stable Diffusion

• Parameter efficient fine tuning with LoRA

• Speech recognition with Whisper

• Multimodal models such as CLIP or LLaVA

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

For comprehensive support of VLMs, check mlx-vlm, and to integrate MLX natively in your apps use mlx-swift-examples.