Build a Large Language Model (From Scratch)

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 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.

• Link to the official source code repository
• Link to the book at Manning
• Link to the book page on Amazon
• ISBN 9781633437166

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, 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
Setup recommendations	-	-
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: Instruction Finetuning	Q2 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
Appendix E: Parameter-efficient Finetuning with LoRA	- appendix-E.ipynb	./appendix-E

 

Shown below is a mental model summarizing the contents covered in this book.

 

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.

 

Bonus Material

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

• Setup
  • Python Setup Tips
  • Installing Libraries Used In This Book
  • Docker Environment Setup Guide
• Chapter 2:
  • Comparing Various Byte Pair Encoding (BPE) Implementations
  • Understanding the Difference Between Embedding Layers and Linear Layers
  • Dataloader Intuition with Simple Numbers
• Chapter 3:
  • Comparing Efficient Multi-Head Attention Implementations
• Chapter 4:
  • FLOPS Analysis
• Chapter 5:
  • Alternative Weight Loading from Hugging Face Model Hub using Transformers
  • Pretraining GPT on the Project Gutenberg Dataset
  • Adding Bells and Whistles to the Training Loop
  • Optimizing Hyperparameters for Pretraining
• Chapter 6:
  • Additional experiments finetuning different layers and using larger models
  • Finetuning different models on 50k IMDB movie review dataset
• Chapter 7:
  • Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries
  • Evaluating Instruction Responses Using the OpenAI API and Ollama

 

Citation

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}
}