rasbt/LLMs-from-scratch
LLMs From Scratch
This repository serves as an educational framework for building large language models from the ground up. It provides a structured curriculum that guides learners through the end-to-end lifecycle of model development, including data processing, architecture design, and optimization. By focusing on low-level implementation, the project enables users to master the fundamental mechanics of artificial intelligence without relying on high-level abstraction frameworks.
The project distinguishes itself by constructing neural network components and gradient-based optimization logic from first principles. It utilizes tensor-based computational modeling and stateless functional architectures to define network layers as pure mathematical transformations. This approach exposes the underlying mechanics of weight updates and loss minimization, allowing for a deeper conceptual mastery of modern machine learning architectures.
The content is organized into a series of executable notebooks that facilitate incremental learning. Each chapter is encapsulated within an independent directory, providing a clear separation of concerns that simplifies dependency management. The repository supports various execution environments, including local Python, Docker containers, and cloud-based platforms, ensuring that the code remains accessible and functional on conventional hardware.
Features
- Language Model Development - Building and training custom language models from scratch to understand the end-to-end lifecycle of data processing, architecture design, and optimization.
- Backpropagation Implementations - Constructs gradient-based optimization logic from first principles to expose the underlying mechanics of weight updates and loss minimization.
- Deep Learning Implementations - Translating complex theoretical concepts into functional neural network code to gain a practical understanding of modern machine learning architectures.
- Model Training Frameworks - Building and training custom language models from scratch to understand the end-to-end lifecycle of data processing, architecture design, and optimization.
- Educational Neural Network Implementations - A pedagogical framework for building neural network components from first principles without relying on high-level abstraction libraries or frameworks.
- Functional Model Architectures - Defines neural network components as pure mathematical transformations that process input tensors into output predictions without hidden side effects.
- LLM Architecture Tutorials - Learning the fundamental mechanics of large language models by building them from the ground up using accessible, educational code examples.
- Machine Learning Curricula - A structured curriculum providing hands-on experience with the architecture, training, and implementation of large language models from the ground up.
- Interactive Notebooks - Organizes complex technical concepts into sequential, executable code blocks that allow users to verify theoretical understanding through immediate practical implementation.
- Technical Learning Repositories - A collection of instructional materials and code examples designed to guide developers through the implementation of complex technical concepts.
- Interactive Learning Environments - Using executable documents to experiment with algorithms and model components in a live development environment for deeper conceptual mastery.
- Technical Tutorials - Explore curated repositories containing instructional materials and documentation to learn specific technical concepts through guided examples and structured learning paths.
- Video Courses - [A 17-hour and 15-minute companion video course](https://www.manning.com/livevideo/master-and-build-large-language-models) where I code through each chapter of the book. The course is organized into chapters and sections
- Low-Level Tensor Libraries - Utilizes low-level array manipulation libraries to implement neural network layers and mathematical operations without relying on high-level abstraction frameworks.
- Modular Architectures - Isolates distinct stages of model development into independent directories to ensure clear separation of concerns and simplified dependency management.