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
- Generative AI Resources - Guides learners through the end-to-end creation of generative language models using a structured, ground-up approach.
- Backpropagation Implementations - Implements gradient-based optimization logic manually to clarify the mechanics of weight updates and loss minimization.
- Deep Learning Implementations - Translates complex deep learning theory into functional code to provide practical experience with neural network architectures.
- Educational Neural Network Implementations - Demonstrates the construction of neural network components from first principles without relying on high-level abstractions.
- Model Training Frameworks - Establishes a structured environment for building and training custom language models to master the development lifecycle.
