Transformer Explainer is an interactive visualization learning tool designed to help anyone learn how Transformer-based models such as GPT work. It was jointly created by Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Jay Wang, Seongmin Lee, Benjamin Hoover, and Polo Chau of the Georgia Institute of Technology.
Purpose This tool is intended for learners who want to intuitively understand the internal mechanisms of large language model (LLM) Transformers. Through interactive visualization, it displays the internal components and computational processes of the Transformer, helping users understand how the model predicts the next token.
Main capabilities Transformer Explainer runs a live GPT-2 model in the browser. Users can enter their own text and observe in real time how the Transformer's internal components and operations work together to predict the next token.
How to use Users can directly use the online address http://poloclub.github.io/transformer-explainer to try it, or watch the YouTube demo video https://youtu.be/TFUc41G2ikY。 To run it locally, the following environment requirements must be met: Node.js v20 or later; NPM v10 or later. Then visit in the browser http://localhost:5173。
Use cases Suitable for individuals, students, or researchers learning how Transformer text generation models such as GPT work, to explore through interactive visualization and experimentation.
Limitations The materials do not state other limitations. This software is provided under the MIT License. If you have questions, you can submit an issue or contact the above contributors.
