MLX Community

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

• mlx-lm - A Python package for LLM text generation and fine-tuning with MLX.
• mlx-swift-examples – a Swift package to run MLX models.
• mlx-vlm – A Python package for inference and fine-tuning of Vision Language Models (VLMs) using MLX.
• mlx-audio - A Python and Swift Package for text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) for 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/Qwen3-4B-Instruct-2507-4bit --prompt "hello"

This will download a Qwen3 4B 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 --model Qwen/Qwen3-4B-Instruct-2507 -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 Qwen3 4B model to the MLX Hugging Face community you can do:

mlx_lm.convert \
    --model Qwen/Qwen3-4B-Instruct-2507 \
    -q \
    --upload-repo mlx-community/Qwen3-4B-Instruct-2507-4bit

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 and LLaVA
• Many other examples of different machine learning applications and algorithms