This project provides an interactive web-based visualization tool for understanding how Transformer-based language models, such as GPT-2, generate text. It targets students, researchers, and developers seeking to demystify the internal workings of LLMs through hands-on experimentation. The primary benefit is an intuitive, visual learning experience that accelerates comprehension of complex neural network architectures.

How It Works

The tool runs a GPT-2 model directly in the browser, leveraging client-side computation. Users input text, and the visualization dynamically illustrates how different components of the Transformer architecture, including attention mechanisms and feed-forward networks, contribute to the prediction of subsequent tokens. This real-time feedback loop allows for immediate observation of cause and effect within the model.

Quick Start & Requirements

• Install via npm install after cloning the repository.
• Run locally using npm run dev.
• Access the application at http://localhost:5173.
• Requires Node.js v20+ and NPM v10+.
• Live demo available at: http://poloclub.github.io/transformer-explainer

Highlighted Details

• Interactive visualization of GPT-2's internal Transformer components.
• Real-time observation of token prediction based on user input.
• Companion research paper presented at IEEE VIS 2024.
• Part of a suite of AI explainer tools from the same group.

Maintenance & Community

Created by researchers at the Georgia Institute of Technology. Further AI explainer projects are linked for exploration. Contact via GitHub issues.

Licensing & Compatibility

Released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The tool visualizes a specific GPT-2 model; results may not generalize to all Transformer architectures or larger, more complex LLMs. Performance is dependent on the user's browser and hardware capabilities.