This repository, "rasbt/llms-from-scratch," serves as the official code companion to the book "Build a Large Language Model (From Scratch)" by Sebastian Raschka. Its primary purpose is to provide a hands-on, educational resource for understanding and implementing large language models (LLMs). The repository contains the complete code necessary to build a ChatGPT-like LLM from the ground up, step by step, using PyTorch. This approach allows users to gain a deep understanding of the inner workings of LLMs, mirroring the methods used in creating large-scale foundational models.

The core functionality of the repository revolves around the implementation of a GPT-like LLM. It guides users through the entire lifecycle of an LLM, from initial development to pretraining and finetuning. The code is structured to align with the book's chapters, offering a clear and organized learning experience. Key features include code for working with text data, implementing attention mechanisms, building a GPT model from scratch, pretraining on unlabeled data, and finetuning for specific tasks like text classification and instruction following. The repository also includes code for loading weights of larger pretrained models for finetuning, allowing users to leverage existing knowledge and accelerate the training process.

The repository's main features are organized into chapters, each corresponding to a specific stage in the LLM development process. Chapter 2 focuses on working with text data, including data loading and preprocessing techniques. Chapter 3 delves into the crucial attention mechanisms that enable LLMs to understand relationships within text. Chapter 4 provides the code for implementing the GPT model itself, the core architecture. Chapter 5 covers pretraining the model on unlabeled data, a critical step in enabling the model to learn general language patterns. Chapter 6 demonstrates finetuning the model for text classification tasks, showcasing how to adapt the model for specific applications. Chapter 7 explores finetuning for instruction following, a key capability for models like ChatGPT.

Beyond the core code, the repository offers supplementary materials and bonus content. These include appendices providing introductions to PyTorch, references, exercise solutions, and additional training loop enhancements. Bonus materials cover topics like Byte Pair Encoding (BPE) tokenizers, efficient multi-head attention implementations, and performance analysis. The repository also includes a comprehensive set of exercises within each chapter, allowing users to test their understanding and reinforce their learning. Solutions to these exercises are provided in the repository.

The repository is designed to be accessible to a wide audience. While a strong foundation in Python programming is essential, the code is written to be understandable, and the book provides clear explanations and diagrams. The code is designed to run on conventional laptops, making it accessible to users without specialized hardware. The repository also includes links to a companion video course, providing an alternative learning experience. Furthermore, the repository is linked to a sequel book, "Build A Reasoning Model (From Scratch)," which builds upon the concepts learned in the original book. The repository encourages community engagement through the Manning Forum and GitHub Discussions, providing a platform for users to ask questions, share feedback, and collaborate. The repository also provides citation information for those who find the book or code useful for their research.