rasbt / LLMs-from-scratch

Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

Home Page:https://www.manning.com/books/build-a-large-language-model-from-scratch

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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 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 own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.

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.


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) Check Python style Check hyperlinks

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
Ch 3: Coding Attention Mechanisms - ch03.ipynb
- multihead-attention.ipynb (summary)
- exercise-solutions.ipynb
Ch 4: Implementing a GPT Model from Scratch - ch04.ipynb
- gpt.py (summary)
- exercise-solutions.ipynb
Ch 5: Pretraining on Unlabeled Data - ch05.ipynb
- gpt_train.py (summary)
- gpt_generate.py (summary)
- exercise-solutions.ipynb
Ch 6: Finetuning for Text Classification - ch06.ipynb
- gpt-class-finetune.py
- exercise-solutions.ipynb
Ch 7: Finetuning with Human Feedback Q2 2024 ...
Appendix A: Introduction to PyTorch - code-part1.ipynb
- code-part2.ipynb
- DDP-script.py
- exercise-solutions.ipynb
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:



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

  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}


Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step




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