The Matrix - GPT

Matrix GPT

Overview

Matrix GPT is a transformer-based GPT model implemented from scratch using PyTorch, inspired by the seminal paper “Attention is All You Need” and Andrej Karpathy’s “Let’s Build GPT” YouTube series. The model is trained on the scripts of The Matrix and The Matrix Reloaded, aiming to generate original text in the style of these iconic films. This project showcases the application of transformer architecture to a creative text generation task, with model weights to be shared once sufficient training is completed.

Tech Stack

PyTorch

Key Features

Custom GPT implementation following transformer principles.
Trained on a unique dataset: The Matrix movie scripts.
Generates text reflecting characters and settings from the films, demonstrating contextual understanding.

Example Output

Trained on a free Google Colab GPU, the model produces output like:

“Thois, Thrinity strom-theacts and groub. I gassice want the to gens to we sswaing, betare hes of jomiugh as endlices…”

While not fully coherent due to limited resources, the text captures recognizable elements of the Matrix universe, laying a foundation for further refinement.

Challenges and Improvements

Limited training resources constrained the model’s coherence. Future enhancements include:

Implementing a tokenizer for improved embedding efficiency (beyond character-level processing).
Experimenting with alternative architectures, hyperparameters, and expanded datasets.

Takeaways

This project demonstrates foundational skills in building and training transformer models with PyTorch, with potential for significant improvement given more computational power and optimization. It’s a stepping stone toward more advanced GPT implementations.