mlx-fine-tuning

What problem does it solve?

This skill streamlines the process of fine-tuning Large Language Models (LLMs) specifically on Apple Silicon hardware, making advanced AI model customization accessible without expensive external GPUs.

Core Features & Use Cases

• MLX Framework Utilization: Leverages MLX for efficient computation on Apple's unified memory architecture.
• LoRA Fine-Tuning: Focuses on parameter-efficient fine-tuning techniques like LoRA.
• Model Conversion: Supports converting models from HuggingFace format to MLX.
• Hyperparameter Optimization: Provides guidance and tools for tuning model parameters.
• Memory Management: Offers strategies for optimizing memory usage during training.
• Use Case: A developer wants to adapt a pre-trained LLM for a specific customer service chatbot using their own dataset, running the entire fine-tuning process on their MacBook Pro.

Quick Start

Validate your environment by running the provided Python script to ensure MLX and Metal GPU are properly configured.