This project provides a user-friendly chat interface for running Large Language Models (LLMs) on Apple Silicon Macs, leveraging the MLX framework. It targets developers and power users seeking a private, on-device AI solution, enabling seamless integration of various HuggingFace and MLX-compatible models.

How It Works

The application utilizes Apple's MLX framework, an array library designed for machine learning on Apple Silicon. MLX offers familiar NumPy-like APIs, composable function transformations for automatic differentiation and optimization, and lazy computation with dynamic graph construction. Its unified memory model allows operations across CPU and GPU without data transfer, contributing to efficient on-device LLM execution.

Quick Start & Requirements

• Install: pip install chat-with-mlx
• Prerequisites: Apple Silicon Mac, Python 3.11+.
• Usage: Run chat-with-mlx from the terminal.
• Docs: HERE

Highlighted Details

• Supports popular LLMs including Llama-3, Phi-3, Mistral, Mixtral, and more.
• Enables privacy-enhanced AI by running models locally.
• Facilitates RAG (Retrieval-Augmented Generation) for chatting with custom data.
• Offers easy integration of HuggingFace and MLX-compatible models.

Maintenance & Community

The project is actively maintained by qnguyen3. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Suitable for commercial use and integration with closed-source applications due to the permissive MIT license.

Limitations & Caveats

Model downloads require manual interruption (Ctrl+C) if switching files. Indexing documents requires explicit dataset mode selection to avoid database corruption. Streaming is not supported for Phi-3-small models.