Transformer Explainer
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
Last verified:
What is Transformer Explainer?
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.
Key features include the ability to input your own text prompts and watch the model process them in real-time, visualize the self-attention matrix showing how words interact, adjust temperature to see how it affects creativity versus predictability, and control top-k sampling parameters. The tool visualizes the complete decoder-only inference flow including token embedding, positional encoding, dropout & residuals, QKV calculations across self-attention heads, MLP with GeLU activation, logits to scaled logits, softmax, and output generation.
The tool is designed for non-experts, students, educators, and anyone interested in AI and machine learning who wants to understand transformer architecture without overwhelming math. It requires no installation or special hardware, runs entirely in the browser using ONNX Runtime with Karpathy's nanoGPT implementation converted, and is freely open-source under MIT License.
Transformer Explainer pricing
Pricing model: Freemium
The tool is completely free to use with no paid tiers. It is open-source under MIT License and available at poloclub.github.io/transformer-explainer. Users can run it directly in the browser without any account or payment, or clone the repository and run locally with Node.js v20+ and NPM v10+.
Transformer Explainer pros
- Runs live GPT-2 model directly in browser with no installation required
- Interactive real-time visualization of transformer internals
- Users can input their own text prompts for experimentation
- Visualizes self-attention matrix showing word-to-word interactions
- Temperature control to adjust model creativity vs predictability
- Top-k sampling parameter adjustment visible in real time
- Shows complete decoder-only inference flow end-to-end
- Token embedding and positional encoding visualization
- QKV calculations visible across all self-attention heads
- MLP layer with GeLU activation shown visually
- Dropout and residuals displayed in the architecture
- Logits to scaled logits to softmax pipeline visualized
- Smooth transitions across abstraction levels
- No special hardware requirements needed
- Open-source under MIT License freely available
Transformer Explainer cons
- Only shows decoder-only inference flow, not encoder-decoder
- Limited to GPT-2 small model, not larger modern LLMs
- Cannot visualize encoder-only models like BERT
- Browser-based computation may be slow on older devices
- No support for multi-turn conversation or chat interfaces
- Limited to text input, no image or audio multimodal support
- Cannot fine-tune or train models, only inference visualization
- No API access for programmatic integration
Frequently asked questions about Transformer Explainer
What is Transformer Explainer?
Transformer Explainer is an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. 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 internal components and parameters work together to predict next tokens.
Do I need to install anything to use it?
No installation is required. The tool runs entirely in your web browser with no special hardware needed. You can access it directly at poloclub.github.io/transformer-explainer, or optionally clone the GitHub repository and run locally with Node.js v20+ and NPM v10+.
What model does it run?
It runs a live GPT-2 small model directly in the browser. The implementation is based on Karpathy's nanoGPT PyTorch implementation, converted into ONNX Runtime, meaning every numerical value updates live as you type your input.
Can I type my own text?
Yes, you can input your own text prompts and watch the model process them in real-time. The tool allows you to experiment with your own text and observe how the Transformer predicts next tokens.
What visualization features does it have?
It visualizes token embeddings, positional encoding, dropout & residuals, QKV calculations across self-attention heads, the self-attention matrix showing word interactions, MLP with GeLU activation, logits, scaled logits, top-k sampling, softmax, and the final output. It also shows smooth transitions across abstraction levels.
Can I adjust model parameters?
Yes, you can adjust the temperature to see how it affects the model's creativity versus predictability, and control top-k sampling parameters to see how text generation changes.
Who is this tool for?
The tool is designed for non-experts, students, educators, and anyone interested in AI and machine learning who wants to understand transformer architecture without overwhelming math. It broadens public education access to modern generative AI techniques.
Is it free to use?
Yes, the tool is completely free. It is open-source under MIT License and requires no payment, account, or installation to use in the browser.
What are the limitations?
The tool only shows decoder-only inference flow (like GPT), not encoder-decoder or encoder-only models like BERT. It uses GPT-2 small, not larger modern LLMs, and cannot fine-tune or train models.
Who created Transformer Explainer?
It 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's Polo Club of Data Science.