Build a Large Language Model (From Scratch)

Explains the Transformer’s self-attention mechanism step-by-step — from text → token embedding → positional embedding → Query * Key → attention scores → attention weights → attention weights * Value → Context vector — using a simple 6-word example in PyTorch. The book concludes with a chapter on instruction fine-tuning implementation on GPT-2 open model.A great read if one is interested to build intuition about self-attention in Transformers and inner system workings of LLM model. A must keep in your book shelf!!

This is a very good book. I recommend to do the code exercises along reading. The author provides all the code, and it's easy to follow in notebooks to really see what is happening. You can modify the code easily and learn a lot. Imho this is very good investment for anyone who wants to learn how LLM work

I've bought tons of ML, DE, programming, cloud architecture books, etc...This book is absolutely fantastic! Especially combined by the current YouTube series published by the author (March 2025).Sebastian's Packt books are also excellent but I must say this book stands on its own. This book is extremely well written and clear, builds each component in the Transformer Architecture piece by piece, it makes me feel like I can actually build an LLM on my own.At a minimum this book will help you understand the Transformer Architecture (Attention Mechanism, Feed Forward, Layer Norm, etc...) rather than importing models from HugginFace and not really know what's going on in the background.If you are like me and are not satisfied with just building RAGs/LLM applications without understanding the model architecture, this book is for you!I'll keep buying from this author as long as the quality of his content is as good as this.

Clearly written with flow diagrams and code samples tightly coupled to the text so you can follow along three ways. No hand-waving or buzzwords or I'm-smarter-than-you. The author knows his stuff, and you'll know it too when you finish the book. The chapters are stand-alone, so you can immediately jump to what you want to know about. This makes the book as much a reference book as a how-to book. And the best part of all is that, unlike much Python code floating around in open source space, the code for this book works out of the box. Kudos to the author.

What an amazing book detailing how each component of the language models components fit together and work synchronously. It is not too difficult to read / follow along if you have previous coding experience with Neural Networks and PyTorch on Machine learning projects. It definitely was a great purchase to understand what it takes to build a local LLM. I had to remove 1 star because the book already tore a bit on the front cover on day 3 of reading.

This book shows step by step all ingredients which are put together in order to build a GPT-2 model from scratch. All functions are explained explicitely in python, before the equivalent functions of pytorch are used. I really liked to follow the book to the end.There is also a discussion forum about the book on github, where readers can ask questions, which are promptly answered by the author.That said, there remain many questions about WHY the method works, and why some steps are made. E.g. why use multihead attention: to my understanding this completely scrambles the embedding vectors, and it is like a miracle that the method works so well. But there were page limits for the book, and and going deeper into this kind of questions would pprobably have doubled the size of the book.

This book was perfect for me. I'm a computer performance specialist, but haven't yet gotten serious about ML and language models. I've read occasional overview articles, so have an idea what things like "vectors" and "matrix multiplication" are, but I didn't see the full picture. I had bought some other machine learning books before that tried to cover everything about everything and never got even half-way through reading them. This book covers not only the practical examples (and source code) with all the steps for training your own toy language models (Python/pytorch code), but also it explained how all the training layers work together in unison. On the training architecture topic, this book did a better job in a handful of pages than all the deep papers I had read in the past, so I should probably have started from this book, not the other way around.Also, the book does a good job incrementally building the knowledge by adding a new layer after another as you progress through the book. Highly recommended!

This is the most in-depth book about LLMs. If you want to understand how transformers work, including every layer in their architecture, this book explains everything in detail.For example, concepts like vanishing gradients and ReLU activation are thoroughly explained, providing the mathematical intuition behind the algorithms. The appendix, spanning 80 pages, delves deeper into PyTorch and neural network concepts for additional support.The book also includes example code covering byte pair encoding, attention mechanisms, and even direct preference optimization.Im happy to have bought and read this book as this gives me better intuition how the transformer model works and how to improve llm performance with fine tuning.