This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows.
The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external model formats into MLX-compatible versions and merging adapter weights for standalone deployment.
The examples cover a broad range of capabilities, including natural language processing with decoder-only and mixture-of-experts architectures, computer vision for image classification and segmentation, and audio processing for speech-to-text and music generation. Additionally, it demonstrates generative AI workflows for text-to-image and text-to-video synthesis, alongside graph-based neural networks and multimodal systems that utilize shared embedding spaces.
