LLMs-from-scratch

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.

• Troubleshooting Guide

| 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