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.
- Large Language Model Tutorials - Explains the fundamental mechanics of large language models through hands-on, step-by-step implementation examples.
- Machine Learning Curricula - Offers a comprehensive educational path covering the architecture, training, and implementation of large language models.
- Neural Network Components - Defines modular mathematical transformations and layer structures essential for building custom neural network architectures.
- Technical Training Repositories - Bundles instructional materials and code examples to guide developers through the implementation of complex technical systems.
- Interactive Notebooks - Organizes technical concepts into sequential, executable notebooks that allow users to verify theory through immediate practice.
- Low-Level Tensor Libraries - Utilizes low-level array manipulation to perform mathematical operations and build neural network layers from scratch.
- Interactive Learning Platforms - Provides interactive, executable documents that allow users to experiment with model components in a live environment.
- Technical Tutorials - Teaches the fundamentals of large language models through a structured curriculum of guided, hands-on tutorials.
- AI and Neural Networks - Building large language models from the ground up.
- Artificial Intelligence - Listed in the “Artificial Intelligence” section of the Build Your Own X awesome list.
- Large Language Models - Educational guide to building language models from the ground up.
- LLM Development and Research - Step-by-step implementation of a ChatGPT-like model.
- LLM Development Books - Step-by-step technical guide to building models from the ground up.
- Machine Learning - Educational repository for building LLMs.
- Natural Language Processing - Step-by-step guide to building language models from the ground up.
- Books and Guides - Step-by-step guide to building LLMs from scratch.
- Educational Resources - Repository for building large language models from scratch.
- Learning Resources - Step-by-step implementation of LLMs.
- Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.
- Video Courses - Features a comprehensive companion video course that walks through the implementation of each chapter.
- Modular and Plugin Architectures - Separates distinct stages of model development into independent, modular directories for clear architectural organization.
Star history
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