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You can use this page to email Daniel Voigt Godoy about A Hands-On Guide to Fine-Tuning Large Language Models with PyTorch and Hugging Face.
About the Book
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 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.
About the Author
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers.
He writes regularly for Towards Data Science. His blog post "Understanding PyTorch with an example: a step-by-step tutorial" reached more than 220,000 views since it was published.
The positive feedback from the readers resulted in an invitation to speak at the Open Data Science Conference (ODSC) Europe in 2019. It also motivated him to write the book "Deep Learning with PyTorch Step-by-Step", which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.