Phi-3-Vision on Apple Silicon: MLX Porting Guide

This tutorial series presents a comprehensive guide to porting and optimizing Microsoft's Phi-3-Vision, a compact yet powerful vision-language model, to Apple's MLX framework for efficient execution on Apple Silicon. The series covers a range of advanced techniques for model adaptation and performance enhancement, including: 1) Basic implementation of Phi-3-Vision in MLX, 2) Integration of Su-scaled Rotary Position Embeddings (SuRoPE) for handling long contexts, 3) Implementation of efficient batching techniques, 4) Development of caching mechanisms for accelerated text generation, 5) Exploration of advanced decoding strategies for guided outputs, 6) Implementation of Low-Rank Adaptation (LoRA) for efficient fine-tuning, and 7) Creation of an Agent class with a flexible toolchain system for complex AI workflows. Additionally, the series demonstrates the broader applicability of these techniques by extending them to port Google's PaliGemma model. This work contributes to the growing field of optimizing large language models for consumer-grade hardware, potentially broadening access to sophisticated AI capabilities.