<Building and Learning LLM from Scratch> Code Commentary

This course explains the example code provided with . The GitHub repository (https://github.com/rickiepark/llm-from-scratch/) contains not only the example code from the book but also various supplementary materials. Explanations for these additional contents are also provided.

This course can be taken by anyone without purchasing the book. However, it is most effective when taken together with the book. Some code explanations may be difficult to understand without viewing them alongside the book. The prerequisite knowledge required is Python programming. It would be helpful if you have experience using deep learning and PyTorch. If you are encountering these two concepts for the first time, please read Appendix A first.

The lectures covering the content of <Build a Large Language Model (From Scratch)> can be viewed for free on YouTube. Please refer to the translator's blog for errata.

Book Introduction

Follow the code line by line, and your own GPT will be complete!
A practical guide to implementing GPT from scratch and mastering LLM principles through hands-on experience

Difficult concepts are explained through illustrations, and you learn LLMs by building them yourself. This book is a hands-on LLM introduction that allows you to learn by implementing the structure and operating principles of large language models from start to finish. Rather than simply explaining concepts, it starts with text preprocessing and tokenization, embedding processes, and gradually builds up self-attention and multi-head attention, transformer blocks step by step. It then integrates these components to complete an actual GPT model, and directly handles core elements of modern architecture design such as model parameter count, training stabilization techniques, activation functions, and normalization methods. It also provides in-depth guidance on pre-training and fine-tuning processes. You can conduct pre-training on unlabeled data, tune models for downstream tasks like text classification, and even practice the recently popular instruction-based learning techniques. It also includes cutting-edge content like LoRA-based parameter-efficient fine-tuning (PEFT), broadly presenting methods to connect LLMs to actual services and research. All concepts are implemented in PyTorch code and optimized to be practical even in regular laptop environments. By following the implementation process in this book, you will naturally understand what happens inside LLMs and gain hands-on experience of how large language model mechanisms work.