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.
- Link to the official source code repository
- Link to the book at Manning (the publisher's website)
- Link to the book page on Amazon.com
- ISBN 9781633437166
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.
| 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.
- Amazon link (TBD)
- Manning link
- GitHub repository
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:
Setup
Chapter 2: Working With Text Data
Chapter 3: Coding Attention Mechanisms
Chapter 4: Implementing a GPT Model From Scratch
Chapter 5: Pretraining on Unlabeled Data
- Alternative Weight Loading Methods
- Pretraining GPT on the Project Gutenberg Dataset
- Adding Bells and Whistles to the Training Loop
- Optimizing Hyperparameters for Pretraining
- Building a User Interface to Interact With the Pretrained LLM
- Converting GPT to Llama
- Memory-efficient Model Weight Loading
- Extending the Tiktoken BPE Tokenizer with New Tokens
- PyTorch Performance Tips for Faster LLM Training
- LLM Architectures
- Chapter 5 with other LLMs as Drop-In Replacement (e.g., Llama 3, Qwen 3)
Chapter 6: Finetuning for classification
Chapter 7: Finetuning to follow instructions
- Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries
- Evaluating Instruction Responses Using the OpenAI API and Ollama
- Generating a Dataset for Instruction Finetuning
- Improving a Dataset for Instruction Finetuning
- Generating a Preference Dataset With Llama 3.1 70B and Ollama
- Direct Preference Optimization (DPO) for LLM Alignment
- Building a User Interface to Interact With the Instruction-Finetuned GPT Model
More bonus material from the Reasoning From Scratch repository:
Qwen3 (From Scratch) Basics
Evaluation
Inference Scaling
Reinforcement Learning (RL)
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}
}File: appendix-A/02_setup-recommendations/README.md
Python and Environment Setup Recommendations
Please see the README.md in the setup directory for Python installation and setup recommendations.
File: appendix-A/01_main-chapter-code/README.md
Appendix A: Introduction to PyTorch
Main Chapter Code
- code-part1.ipynb contains all the section A.1 to A.8 code as it appears in the chapter
- code-part2.ipynb contains all the section A.9 GPU code as it appears in the chapter
- DDP-script.py contains the script to demonstrate multi-GPU usage (note that Jupyter Notebooks only support single GPUs, so this is a script, not a notebook). You can run it as
python DDP-script.py. If your machine has more than 2 GPUs, run it asCUDA_VISIBLE_DEVIVES=0,1 python DDP-script.py. - exercise-solutions.ipynb contains the exercise solutions for this chapter
Optional Code
- DDP-script-torchrun.py is an optional version of the
DDP-script.pyscript that runs via the PyTorchtorchruncommand instead of spawning and managing multiple processes ourselves viamultiprocessing.spawn. Thetorchruncommand has the advantage of automatically handling distributed initialization, including multi-node coordination, which slightly simplifies the setup process. You can use this script viatorchrun --nproc_per_node=2 DDP-script-torchrun.py
File: appendix-A/README.md
Appendix A: Introduction to PyTorch
Main Chapter Code
- 01_main-chapter-code contains the main chapter code
Bonus Materials
- 02_setup-recommendations contains Python installation and setup recommendations.
File: appendix-D/README.md
Appendix D: Adding Bells and Whistles to the Training Loop
- 01_main-chapter-code contains the main chapter code.
File: appendix-E/README.md
Appendix E: Parameter-efficient Finetuning with LoRA
- 01_main-chapter-code contains the main chapter code.
File: ch01/README.md
Chapter 1: Understanding Large Language Models
Main Chapter Code
There is no code in this chapter.
Bonus Materials
Recommendations for Getting the Most Out of This Book
In the video below, I share my personal approach to setting up a Python environment on my computer:
As an optional bonus, the following video tutorial provides an overview of the LLM development lifecycle covered in this book:
File: ch02/05_bpe-from-scratch/README.md
Byte Pair Encoding (BPE) Tokenizer From Scratch
bpe-from-scratch-simple.ipynb contains optional (bonus) code that explains and shows how the BPE tokenizer works under the hood; this is geared for simplicity and readability.
bpe-from-scratch.ipynb implements a more sophisticated (and much more complicated) BPE tokenizer that behaves similarly as tiktoken with respect to all the edge cases; it also has additional funcitionality for loading the official GPT-2 vocab.
File: ch02/04_bonus_dataloader-intuition/README.md
Chapter 2: Working with Text Data
- dataloader-intuition.ipynb contains optional (bonus) code to explain the data loader more intuitively with simple numbers rather than text.
File: ch02/03_bonus_embedding-vs-matmul/README.md
Chapter 2: Working with Text Data
- embeddings-and-linear-layers.ipynb contains optional (bonus) code to explain that embedding layers and fully connected layers applied to one-hot encoded vectors are equivalent.