Can I use models directly from HuggingFace?

Yes, the skill supports thousands of pre-converted models from the mlx-community on HuggingFace and provides commands to convert any standard transformer model.

Does it support fine-tuning?

Yes, it provides guidance and commands for performing LoRA and QLoRA fine-tuning on your local datasets using the mlx_lm.lora module.

What hardware is required for this skill?

This skill is specifically designed for Mac computers equipped with Apple Silicon (M1, M2, M3, or M4 chips) to leverage the MLX framework's unified memory architecture.

How do I make the local model accessible to other apps?

You can use the mlx_lm.server command to launch an OpenAI-compatible HTTP API, allowing local models to act as drop-in replacements for external LLM providers.

MLX LM Integration

Name: MLX LM Integration
Author: zeero

byzeero

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데이터 과학 및 ML

Simplifies running, converting, and serving Large Language Models on Apple Silicon using the MLX framework.

This skill provides comprehensive support for the MLX LM library, enabling Claude to assist with LLM inference, fine-tuning, and model conversion specifically optimized for Mac hardware. It bridges the gap between Claude's coding capabilities and the MLX ecosystem, helping users generate text via CLI or Python, set up OpenAI-compatible servers, and perform 4-bit quantization to run large models locally with high efficiency. It is ideal for developers looking to leverage the power of Apple Silicon for local AI development and deployment.