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

\n

\n
• mlx-lm - A Python package for LLM text generation and fine-tuning with MLX.
\n
• mlx-swift-examples – a Swift package to run MLX models.
\n
• mlx-vlm – A Python package for inference and fine-tuning of Vision Language Models (VLMs) using MLX.
\n
• mlx-audio - A Python and Swift Package for text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) for MLX.
\n

\n

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

\n

Quick start for LLMs

\n

Install mlx-lm:

\n

pip install mlx-lm\n

\n

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

\n

mlx_lm.generate --model mlx-community/Qwen3-4B-Instruct-2507-4bit --prompt \"hello\"\n

\n

This will download a Qwen3 4B model from the Hugging Face Hub and generate\ntext using the given prompt.

\n

To chat with an LLM use:

\n

mlx_lm.chat\n

\n

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

\n

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

\n

mlx_lm.chat --help\n

\n

Conversion and Quantization

\n

To quantize a model from the command line run:

\n

mlx_lm.convert --model Qwen/Qwen3-4B-Instruct-2507 -q \n

\n

For more options run:

\n

mlx_lm.convert --help\n

\n

You can upload new models to Hugging Face by specifying --upload-repo to\nconvert. For example, to upload a quantized Qwen3 4B model to the \nMLX Hugging Face community you can do:

\n

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

\n

Models can also be converted and quantized directly in the\nmlx-my-repo Hugging\nFace Space.

\n

For more details on the API checkout the full README

\n

Other Examples:

\n

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

\n

\n
• Image generation with Flux and Stable Diffusion
\n
• Parameter efficient fine tuning with LoRA
\n
• Speech recognition with Whisper
\n
• Multimodal models such as CLIP and LLaVA
\n
• Many other examples of different machine learning applications and algorithms
\n

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