Build a Large Language Model (From Scratch)

This repository contains code for developing, pretraining, and finetuning a GPT-like LLM.

Bonus Material

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

• Setup
  • Python Setup Tips
  • Installing Python Packages and Libraries Used In This Book
  • Docker Environment Setup Guide
• Chapter 2: Working with text data
  • Byte Pair Encoding (BPE) Tokenizer From Scratch
  • Comparing Various Byte Pair Encoding (BPE) Implementations
  • Understanding the Difference Between Embedding Layers and Linear Layers
  • Dataloader Intuition with Simple Numbers
• Chapter 3: Coding attention mechanisms
  • Comparing Efficient Multi-Head Attention Implementations
  • Understanding PyTorch Buffers
• Chapter 4: Implementing a GPT model from scratch
  • FLOPS Analysis
• Chapter 5: Pretraining on unlabeled data:
  • 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
  • Building a User Interface to Interact With the Pretrained LLM
  • Converting GPT to Llama
  • Llama 3.2 From Scratch
  • Memory-efficient Model Weight Loading
  • Extending the Tiktoken BPE Tokenizer with New Tokens
  • PyTorch Performance Tips for Faster LLM Training
• Chapter 6: Finetuning for classification
  • Additional experiments finetuning different layers and using larger models
  • Finetuning different models on 50k IMDB movie review dataset
  • Building a User Interface to Interact With the GPT-based Spam Classifier
• 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

Questions, Feedback, and Contributing to This Repository

I welcome all sorts of feedback, best shared via 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}
}