Transformer Explainer: Interactive Learning of Text-Generative Models
Abstract
Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and enabling smooth transitions across abstraction levels of mathematical operations and model structures. It runs a live GPT-2 instance locally in the user's browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. Our tool requires no installation or special hardware, broadening the public's education access to modern generative AI techniques. Our open-sourced tool is available at https://poloclub.github.io/transformer-explainer/. A video demo is available at https://youtu.be/ECR4oAwocjs.
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This is visualisation done right. I am lost for words.
Great to see! We need more visualisations like this. How I wish this resource existed about six months ago when I was grasping the fundamentals of Transformers.
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Thanks for your interest in Transformer Explainer’s potential to support more models! The ManimML project from our group has been investigating how to enable ML developers more easily create animated visual explanations of ML concepts https://github.com/helblazer811/ManimML . Currently Transformer Explainer does not yet support similar capabilities, but that’s something on our long-term road map.
Thanks for your interest in Transformer Explainer’s potential to support more models! The ManimML project from our group has been investigating how to enable ML developers more easily create animated visual explanations of ML concepts https://github.com/helblazer811/ManimML . Currently Transformer Explainer does not yet support similar capabilities, but that’s something on our long-term road map.
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