README.md

---
title: README
emoji: 📚
colorFrom: green
colorTo: indigo
sdk: static
pinned: false
---

# MLX Community

A community org for [MLX](https://github.com/ml-explore/mlx) model weights that run on Apple Silicon. This organization hosts ready-to-use models compatible with:
- [mlx-examples](https://github.com/ml-explore/mlx-examples) – a Python and CLI to run multiple types of models, including LLMs, image models, audio models, and more.
- [mlx-swift-examples](https://github.com/ml-explore/mlx-swift-examples) – a Swift package to run MLX models.
- [mlx-vlm](https://github.com/Blaizzy/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:

```bash
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](https://huggingface.co/mlx-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](https://huggingface.co/spaces/mlx-community/mlx-my-repo) Hugging
Face Space.

For more details on the API checkout the full [README](https://github.com/ml-explore/mlx-examples/tree/main/llms)


### Other Examples:

For more examples, visit the [MLX Examples](https://github.com/ml-explore/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](https://github.com/Blaizzy/mlx-vlm), and to integrate MLX natively in your apps use [mlx-swift-examples](https://github.com/ml-explore/mlx-swift-examples).