LLMs-from-scratch README

The project's README from github.com/rasbt/LLMs-from-scratch. As one markdown file: README.md.

Build a Large Language Model (From Scratch)

This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch).




In Build a Large Language Model (From Scratch), you'll learn and understand how large language models (LLMs) work from the inside out by coding them from the ground up, step by step. In this book, I'll guide you through creating your own LLM, explaining each stage with clear text, diagrams, and examples.

The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT. In addition, this book includes code for loading the weights of larger pretrained models for finetuning.



To download a copy of this repository, click on the Download ZIP button or execute the following command in your terminal:

git clone --depth 1 https://github.com/rasbt/LLMs-from-scratch.git

(If you downloaded the code bundle from the Manning website, please consider visiting the official code repository on GitHub at https://github.com/rasbt/LLMs-from-scratch for the latest updates.)



Table of Contents

Please note that this README.md file is a Markdown (.md) file. If you have downloaded this code bundle from the Manning website and are viewing it on your local computer, I recommend using a Markdown editor or previewer for proper viewing. If you haven't installed a Markdown editor yet, Ghostwriter is a good free option.

You can alternatively view this and other files on GitHub at https://github.com/rasbt/LLMs-from-scratch in your browser, which renders Markdown automatically.



Tip: If you're seeking guidance on installing Python and Python packages and setting up your code environment, I suggest reading the README.md file located in the setup directory.



Code tests Linux Code tests Windows Code tests macOS

Chapter Title Main Code (for Quick Access) All Code + Supplementary
Setup recommendations
How to best read this book		 - -
Ch 1: Understanding Large Language Models No code -
Ch 2: Working with Text Data - ch02.ipynb
- dataloader.ipynb (summary)
- exercise-solutions.ipynb		 ./ch02
Ch 3: Coding Attention Mechanisms - ch03.ipynb
- multihead-attention.ipynb (summary)
- exercise-solutions.ipynb		 ./ch03
Ch 4: Implementing a GPT Model from Scratch - ch04.ipynb
- gpt.py (summary)
- exercise-solutions.ipynb		 ./ch04
Ch 5: Pretraining on Unlabeled Data - ch05.ipynb
- gpt_train.py (summary)
- gpt_generate.py (summary)
- exercise-solutions.ipynb		 ./ch05
Ch 6: Finetuning for Text Classification - ch06.ipynb
- gpt_class_finetune.py
- exercise-solutions.ipynb		 ./ch06
Ch 7: Finetuning to Follow Instructions - ch07.ipynb
- gpt_instruction_finetuning.py (summary)
- ollama_evaluate.py (summary)
- exercise-solutions.ipynb		 ./ch07
Appendix A: Introduction to PyTorch - code-part1.ipynb
- code-part2.ipynb
- DDP-script.py
- exercise-solutions.ipynb		 ./appendix-A
Appendix B: References and Further Reading No code ./appendix-B
Appendix C: Exercise Solutions - list of exercise solutions ./appendix-C
Appendix D: Adding Bells and Whistles to the Training Loop - appendix-D.ipynb ./appendix-D
Appendix E: Parameter-efficient Finetuning with LoRA - appendix-E.ipynb ./appendix-E

 

The mental model below summarizes the contents covered in this book.


 

Prerequisites

The most important prerequisite is a strong foundation in Python programming. With this knowledge, you will be well prepared to explore the fascinating world of LLMs and understand the concepts and code examples presented in this book.

If you have some experience with deep neural networks, you may find certain concepts more familiar, as LLMs are built upon these architectures.

This book uses PyTorch to implement the code from scratch without using any external LLM libraries. While proficiency in PyTorch is not a prerequisite, familiarity with PyTorch basics is certainly useful. If you are new to PyTorch, Appendix A provides a concise introduction to PyTorch. Alternatively, you may find my book, PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUs, helpful for learning about the essentials.


 

Hardware Requirements

The code in the main chapters of this book is designed to run on conventional laptops within a reasonable timeframe and does not require specialized hardware. This approach ensures that a wide audience can engage with the material. Additionally, the code automatically utilizes GPUs if they are available. (Please see the setup doc for additional recommendations.)

 

Video Course

A 17-hour and 15-minute companion video course where I code through each chapter of the book. The course is organized into chapters and sections that mirror the book's structure so that it can be used as a standalone alternative to the book or complementary code-along resource.

 

Companion Book / Sequel

Build A Reasoning Model (From Scratch), while a standalone book, can be considered as a sequel to Build A Large Language Model (From Scratch).

It starts with a pretrained model and implements different reasoning approaches, including inference-time scaling, reinforcement learning, and distillation, to improve the model's reasoning capabilities.

Similar to Build A Large Language Model (From Scratch), Build A Reasoning Model (From Scratch) takes a hands-on approach implementing these methods from scratch.


 

Exercises

Each chapter of the book includes several exercises. The solutions are summarized in Appendix C, and the corresponding code notebooks are available in the main chapter folders of this repository (for example, ./ch02/01_main-chapter-code/exercise-solutions.ipynb.

In addition to the code exercises, you can download a free 170-page PDF titled Test Yourself On Build a Large Language Model (From Scratch) from the Manning website. It contains approximately 30 quiz questions and solutions per chapter to help you test your understanding.

 

Bonus Material

Several folders contain optional materials as a bonus for interested readers:

More bonus material from the Reasoning From Scratch repository:


 

Questions, Feedback, and Contributing to This Repository

I welcome all sorts of feedback, best shared via the Manning Forum or GitHub Discussions. Likewise, if you have any questions or just want to bounce ideas off others, please don't hesitate to post these in the forum as well.

Please note that since this repository contains the code corresponding to a print book, I currently cannot accept contributions that would extend the contents of the main chapter code, as it would introduce deviations from the physical book. Keeping it consistent helps ensure a smooth experience for everyone.

 

Citation

If you find this book or code useful for your research, please consider citing it.

Chicago-style citation:

Raschka, Sebastian. Build A Large Language Model (From Scratch). Manning, 2024. ISBN: 978-1633437166.

BibTeX entry:

@book{build-llms-from-scratch-book,
  author       = {Sebastian Raschka},
  title        = {Build A Large Language Model (From Scratch)},
  publisher    = {Manning},
  year         = {2024},
  isbn         = {978-1633437166},
  url          = {https://www.manning.com/books/build-a-large-language-model-from-scratch},
  github       = {https://github.com/rasbt/LLMs-from-scratch}
}

LLMs-from-scratch (rasbt/LLMs-from-scratch)

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LLMs-from-scratch
Что это такое?Это официальный репозиторий с кодом к книге 'Build a Large Language Model (From Scratch)', которая шаг за шагом учит реализовывать GPT-подобную языковую модель (LLM) на PyTorch: от токенизации до предобучения и дообучения.
Это библиотека, которую нужно импортировать, или набор обучающих блокнотов?Это собрание блокнотов Jupyter и скриптов Python, организованных по главам книги. Их нужно читать и запускать вместе с книгой, а не устанавливать как библиотеку.
С чего начать работу с кодом?Скачайте или клонируйте репозиторий, настройте Python по инструкциям из каталога setup, затем проходите папки глав по порядку (с ch02 по ch07, а также приложения).
Нужен ли предварительный опыт в глубоком обучении, чтобы во всём разобраться?Главное требование — уверенное знание Python; опыт работы с глубокими нейронными сетями и основы PyTorch помогают, но не обязательны, так как Приложение A вводит PyTorch с нуля.
Какие темы освещает книга по главам?Книга охватывает работу с текстовыми данными и токенизацию, программирование механизмов внимания, создание GPT-модели с нуля, предобучение на неразмеченных данных, дообучение для классификации текста и дообучение для следования инструкциям. В приложениях рассматриваются PyTorch, решения упражнений и дообучение с помощью LoRA.
Есть ли видеоверсия книги?Да, есть сопутствующий видеокурс продолжительностью 17 часов 15 минут. Он повторяет структуру глав книги и может использоваться как самостоятельный или дополнительный ресурс.
Есть ли продолжение книги?Да, 'Build A Reasoning Model (From Scratch)' — самостоятельное продолжение, которое начинается с предобученной модели и реализует методы рассуждения: масштабирование на этапе инференса, обучение с подкреплением и дистилляцию.
Какие дополнительные материалы есть помимо основных глав?Бонусные папки охватывают такие темы, как реализации токенизатора BPE, KV-кэш, Grouped-Query и Multi-Head Latent Attention, Mixture-of-Experts, преобразование GPT в Llama, Direct Preference Optimization, а также реализации с нуля Llama 3.2, Qwen3 и Gemma 3.
What are similar projects?llama3-from-scratch, train-llm-from-scratch, minGPT, nn-zero-to-hero, OpenArch, llm-architecture-from-scratch, nlp_course, ai-engineering-from-scratch
Which topics is it in?finetuning, tokenizer, instruction-tuning, pretraining, from-scratch, attention-mechanism, language-model, lora, natural-language-processing, transformers, gpt, jupyter notebook
Creator
rasbt
Size
16.7 MB
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106.7K
Updated
CommitOct 02
Age
3y
Commits · 90 Days