Unlock the Power of Tiny Transformers: Build Your Own GPT Model in Go
Want to understand how large language models work without getting lost in complex math? This guide breaks down a simple GPT implementation in Go, allowing you to train your own mini-GPT model. Learn the core concepts behind these powerful AI tools.
Train Your Own GPT Model: A Practical Go Implementation
This project provides a simplified GPT model built with the Go programming language. It's designed for educational purposes, prioritizing clarity and ease of understanding over raw performance. You can train your own GPT model using your dataset.
- Simplicity First: Ditch the complexity of large-scale frameworks and dive into the fundamentals of transformer architecture. This implementation uses readable code, making it easier to grasp the key concepts.
- Customizable Training: Train the model on your own text corpus by adjusting the
data.datasetvariable. Experiment with different datasets and observe how the model learns. - Start Small, Learn Big: Perfect for learning and experimenting with the building blocks of GPT models.
Understanding the Code: Explore the Evolution of a Neural Network
The repository is structured to reflect the iterative development of a neural network.
- Step-by-Step Learning: Use
git checkout <tag>to follow the model's evolution: naive, bigram, multihead, block, residual, full. - Refer to
main_test.go: Find detailed explanations and step-by-step guidance on how the model works. Perfect for beginners wanting to understand the underlying mechanisms. - From Basic to Advanced: The tagging system enables you to learn at your own pace. Start with basic models and move on to more complex architectures.
Design Choices for Clarity: No Batches, No External Dependencies
Several key design decisions were made to enhance understanding.
- Simplified Matrices: The batch dimension was removed to improve code clarity. Working with familiar 2D matrices makes understanding easier than with 3D tensors.
- Independent Implementation: The dependency on
gonumwas removed in favor of simplicity, although it gave a performance boost, it increased complexity. A readablematmulimplementation is provided. - Radical Simplicity: The project aims to provide a clear and educational resource. Simplicity and readability have been prioritized.
Model Parameters and Performance: Train Your Own Text Generator
The pretrained model uses these parameters: bs=32, es=64, lr=0.0010, ls=1.0, vs=3000, epochs=20000. Trained on selections from Jules Verne, this is the kind of output to expect:
Mysterious Island. Well. My days must follow.
The training loss decreases over time, reflecting the model's learning process. Here is some sample loss data:
- epoch: 18000, loss: 5.04248
- epoch: 19000, loss: 4.97543
- epoch: 20000, loss: 4.86982
Delve Deeper: Essential Papers on Transformer Architecture
You don't need to read research papers to understand the code, but these are valuable resources.
- Attention Is All You Need
- Deep Residual Learning
- DeepMind WaveNet
- Kaiming initialization
- Batch Normalization
- OpenAI GPT-3 paper
Learn GPT in Go: Contributing and Inspiration
This project was inspired by Andrej Karpathy's Neural Networks: Zero to Hero course. Its autograd package was contributed by @itsubaki. Use this repository as a launchpad for your own exploration of language models! Feel free to contribute and learn.
