Transformer 视觉解释: 通过互动可视化了解 LLM、Transformer 模型的工作原理
Transformer 视觉解释: 通过互动可视化了解 LLM、Transformer 模型的工作原理
Transformer Explainer is an interactive visualization tool designed to help anyone learn how Transformer-based models like GPT work. It runs a live GPT-2 model right in your browser, allowing you to experiment with your own text and observe in real time how internal components and operations of the Transformer work together to predict the next tokens. Try Transformer Explainer at http://poloclub.github.io/transformer-explainer and watch a demo video on YouTube https://youtu.be/TFUc41G2ikY.
Try Transformer Explainer: http://poloclub.github.io/transformer-explainer
Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentations. Aeree Cho, Grace C. Kim, Alexander Karpekov, Seongmin Lee, Alec Helbling, Benjamin Hoover, Zijie J. Wang, Minsuk Kahng, Duen Horng Chau. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems.
git clone https://github.com/poloclub/transformer-explainer.git
cd transformer-explainer
npm install
npm run dev
Then, on your web browser, access http://localhost:5173.
Transformer Explainer was created by Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Jay Wang, Seongmin Lee, Benjamin Hoover, and Polo Chau at the Georgia Institute of Technology.
@inproceedings{cho2026transformer,
title={Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation},
author={Cho, Aeree and Kim, Grace C and Karpekov, Alexander and Lee, Seongmin and Helbling, Alec and Hoover, Benjamin and Wang, Zijie J and Kahng, Minsuk and Chau, Duen Horng},
booktitle={Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems},
pages={1--21},
year={2026}
}
The software is available under the MIT License.
If you have any questions, feel free to open an issue or contact Aeree Cho or any of the contributors listed above.
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