A Hands-On Guide to Fine-Tuning Large Language Models with PyTorch and Hugging Face

Revised Edition (October/2025)

Are you ready to fine-tune your own LLMs?

This book is a practical guideto 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 modelsusing BitsAndBytes.
• Configure Low-Rank Adapters (LoRA) using Hugging Face’s PEFT.
• Format datasets effectively using chat templatesand formatting functions.
• Fine-tune LLMs on consumer-grade GPUsusing techniques such as gradient checkpointing and accumulation.
• Deploy LLMs locallyin the GGUFformat using Llama.cppand Ollama.
• Troubleshootcommon 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