This repository provides a step-by-step, from-scratch reproduction of the nanoGPT language model, accompanied by a video lecture. It targets developers and researchers interested in understanding and replicating GPT-2 (124M) architecture and training, offering a cost-effective and time-efficient path to building foundational language models.

How It Works

The project meticulously recreates the GPT-2 (124M) model using clean, incremental Git commits, allowing users to trace the development process. It focuses on the core language modeling task, training on internet documents to predict the next token. The approach emphasizes clarity and educational value, making complex concepts accessible.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.x, PyTorch, NumPy. GPU recommended for training (e.g., Lambda Labs).
• Training: Reproducing GPT-2 (124M) takes approximately 1 hour and ~$10 on a cloud GPU.
• Resources: Official YouTube lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY

Highlighted Details

• Step-by-step Git commits for clear learning.
• Reproduces GPT-2 (124M) model training.
• Cost-effective training (~$10) and time-efficient (~1 hour).
• Focuses on foundational language modeling, not chat fine-tuning.

Maintenance & Community

• Active community discussions via GitHub Discussions and Zero To Hero Discord (#nanoGPT channel).
• Errata and fixes are addressed in the repository.

Licensing & Compatibility

• License: MIT
• Compatibility: Permissive MIT license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The project focuses solely on foundational language model training and does not cover chat fine-tuning or conversational AI capabilities. Compatibility with older PyTorch versions may require specific workarounds for type casting.