A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face

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.

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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 some prior 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 configuration—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.

What if I do not like the book? Can I get a refund?

Yes, you can! No questions asked! If you are not happy with your purchase, just reply to the download email within 30 days, and you will get your money back.

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Table of Contents

• 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