[Read] PDF/Book A Hands-On Guide to Fine-Tuning Large Language Models with PyTorch and Hugging Face by
Daniel Voigt Godoy
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Are you ready to fine-tune your own LLMs?
This book is a practical guide to fine-tuning Large Language Models (LLMs), combining high-level concepts with step-by-step instructions to train these powerful models for your specific use cases.
Who Is This Book For?
This is an intermediate-level resource—positioned between building a large language model from scratch and deploying an LLM in production—designed for practitioners with someprior experience in deep learning.
If terms like Transformers, attention mechanisms, Adam optimizer, tokens, embeddings, or GPUs sound familiar, you’re in the right place. Familiarity with Hugging Face and PyTorch is assumed. If you're new to these concepts, consider starting with a beginner-friendly introduction to deep learning with PyTorch before diving in.
What You’ll Learn
Load quantized models using BitsAndBytes.
Configure Low-Rank Adapters (LoRA) using Hugging Face's PEFT. Format datasets effectively using chat templates and formatting functions. Fine-tune LLMs on consumer-grade GPUs using techniques such as gradient checkpointing and accumulation.
Deploy LLMs locally in the GGUF format using Llama cpp and Ollama. Troubleshoot common error messages and exceptions to keep your fine-tuning process on track.
This book doesn’t just skim the surface; it zooms in on the critical adjustments and configurations—those all-important "knobs"—that make or break the fine-tuning process. By the end, you’ll have the skills and confidence to fine-tune LLMs for your own real-world applications. Whether you’re looking to enhance existing models or tailor them to niche tasks, this book is your essential companion.
Table of Contents
Frequently Asked Questions (FAQ)
Chapter 0: TL;DR
Chapter 1: Pay Attention to LLMs
Chapter 2: Loading a Quantized Base Model
Chapter 3: Low-Rank Adaptation (LoRA)
Chapter 4: Formatting Your Dataset
Chapter 5: Fine-Tuning with SFTTrainer
Chapter 6: Deploying It Locally
Chapter -1: Troubleshooting
Appendix A: Setting Up Your GPU Pod
Appendix B: Data Types' Internal Representation