TokenTown: A visual way to understand how LLMs work
laurentiugabriel.github.io
July 29, 2026
2 min read
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46/100
Summary
TokenTown visualizes a transformer language model as an isometric city, with each district representing a stage of the model. A convoy transports a hidden state through various processes, including tokenization, vector casting, and layer processing, ultimately producing a probability distribution for token selection.
Key Takeaways
- TokenTown is an isometric city representation of a transformer language model, where each district corresponds to a stage of the model's processing.
- The model operates in a simplified manner with reduced dimensions, attention heads, layers, and vocabulary compared to full-scale transformer models.
- The output of TokenTown is generated using random weights and a bigram prior, allowing for a visual and interactive demonstration of language model mechanics.
- Users can control the speed of the simulation and explore explanations of each district's function in the model's architecture.
Community Sentiment
Concerns
- This doesn't help at all — the visual tool fails to clarify how LLMs work and seems buggy and confusing.
- The explanations are just typical hand-wavy LLM jargon, lacking clear, polished content that would genuinely aid understanding.
- It’s hard to grasp the concepts without prior knowledge, which makes this tool less accessible for average users.