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.

• Node.js v20 or higher
• NPM v10 or higher

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.

• Diffusion Explainer for learning how Stable Diffusion transforms text prompt into image
• CNN Explainer
• GAN Lab for playing with Generative Adversarial Networks in browser