Fine-Tuning Your First Large Language Model (LLM) with PyTorch and Hugging Face

Daniel Voigt Godoy's blog post, adapted from his book "A Hands-On Guide to Fine-Tuning Large Language Models with PyTorch and Hugging Face," provides a step-by-step tutorial on fine-tuning Microsoft's Phi-3 Mini 4K Instruct model to translate English into Yoda-speak. The guide emphasizes using quantization via BitsAndBytes to reduce the model's memory footprint and low-rank adapters (LoRA) to enable efficient fine-tuning with minimal trainable parameters. It details the process of setting up the environment, configuring the model, loading the Yoda-speak dataset, and using Hugging Face's SFTTrainer for supervised fine-tuning. The post also includes insights on adapting the tokenizer for optimal performance and addresses potential issues with recent library updates. Finally, it outlines saving the fine-tuned model and sharing it on the Hugging Face Hub, showcasing a practical approach to model customization using cutting-edge tools.