GPT in 60 Lines of NumPy

Overview

This project implements a GPT model from scratch using only NumPy in about 60 lines of code. It demonstrates the core components of a transformer decoder, including token embedding, positional encoding, multi-head attention, and feed-forward layers. The code is designed for educational purposes to help developers understand how GPT works internally.

Best for

Best for
Developers and students who want to deeply understand GPT's inner workings through hands-on code

Use cases

• Learning the internals of transformer decoder architecture
• Experimenting with small-scale GPT training and inference
• Prototyping custom modifications to attention or embedding layers

Notes

This project implements a GPT model from scratch using only NumPy in about 60 lines of code. It demonstrates the core components of a transformer decoder, including token embedding, positional encoding, multi-head attention, and feed-forward layers. The code is designed for educational purposes to help developers understand how GPT works internally.

Use cases

• Learning the internals of transformer decoder architecture
• Experimenting with small-scale GPT training and inference
• Prototyping custom modifications to attention or embedding layers

Pros

• Minimal dependencies (only NumPy) makes setup trivial
• Concise code clearly exposes each transformer component
• Excellent for building intuition about GPT mechanics

Cons

• Not optimized for performance or large-scale models
• Lacks GPU support and advanced training features
• Limited to very small model sizes due to NumPy’s memory and speed constraints

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