Fine-Tuning Models for Content Moderation with Apple’s MLX Framework

Hands‑on guide to fine‑tune LLMs and multimodal models on Apple‑silicon Macs with MLX, LoRA adapters, dataset prep, training, evaluation, deployment.

Overview

This talk is a hands-on walkthrough of how to fine-tune large language models—and multi-modal models—locally on Apple-Silicon Macs using Apple’s open-source MLX framework. We’ll:

Give a quick MLX overview and why it’s optimized for the M-series GPU/ANE.
Explain LoRA / QLoRA and why adapter-based fine-tuning is memory-efficient.
Dissect the vision-encoder → adapter → LLM pipeline (SigLIP + Phi-1.5).
Show dataset prep in JSONL, CLI commands (mlx_lm.lora, mlx_lm.fuse) and YAML options.
Live-interpret training logs, validation curves, and evaluation metrics.
End with best-practice checklists for scaling from small experiments to full production runs.

Attendees will leave able to replicate the full workflow—dataset → training → evaluation → deployment—entirely on their MacBooks.

Tech stack