Understanding Transformers Using a Minimal Example

Twenty-dimensional boxes shaped like Legos might be the key to understanding how large language models actually think. A new visualization project strips down the complex machinery of transformer models to make their inner workings visible to human eyes.

"This approach offers a transparent view into the core operations of a Transformer," explains the visualization, which uses a deliberately simplified model trained on just 94 words about fruits and tastes.

Instead of billions of parameters, this mini-model uses only 10,000, representing each token as a stack of five colorful boxes whose dimensions reflect the 20 numbers that make up each word's embedding.

When given the prompt "I like spicy so I like," the model correctly predicts "chili" despite never seeing that exact pattern in training. The visualization shows how the representation for the final word "like" transforms through the model's layers until it closely resembles the embedding for "chili."

By examining how attention mechanisms weigh different input tokens, viewers can trace exactly how information flows through the system, offering an intuitive window into the inner workings of AI language systems.