This repository contains the code for coding, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch).
(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.)
In Build a Large Language Model (From Scratch), you'll discover how LLMs work from the inside out. In this book, I'll guide you step by step 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 small but functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.
- Link to the official source code repository
- Link to the early access version at Manning
- ISBN 9781633437166
- Publication in Early 2025 (estimated)
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, MarkText is a good free option.
Alternatively, you can view this and other files on GitHub at https://github.com/rasbt/LLMs-from-scratch.
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.
| Chapter Title | Main Code (for quick access) | All Code + Supplementary |
|---|---|---|
| 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 | Q2 2024 | ... |
| Ch 7: Finetuning with Human Feedback | Q2 2024 | ... |
| Ch 8: Using Large Language Models in Practice | Q2/3 2024 | ... |
| 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 C: Exercise Solutions | No code | - |
| Appendix D: Adding Bells and Whistles to the Training Loop | - appendix-D.ipynb | ./appendix-D |
Shown below is a mental model summarizing the contents covered in this book.
Several folders contain optional materials as a bonus for interested readers:
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Setup
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Chapter 2:
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Chapter 3:
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Chapter 5:
If you find this book or code useful for your research, please consider citing it:
@book{build-llms-from-scratch-book,
author = {Sebastian Raschka},
title = {Build A Large Language Model (From Scratch)},
publisher = {Manning},
year = {2023},
isbn = {978-1633437166},
url = {https://www.manning.com/books/build-a-large-language-model-from-scratch},
note = {Work in progress},
github = {https://github.com/rasbt/LLMs-from-scratch}
}