LLM Attention Visualization
A browser-based interactive visualization that shows how transformer LLMs allocate attention to past tokens during generation by aggregating attention weights and value magnitudes across heads and layers.
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Quick Overview
Best for: Research
What it does
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Best fit
Research
Pricing snapshot
Pricing available on request
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LLM Attention Visualization
LLM Attention Visualization is an interactive browser-based tool that visualizes the attention mechanism of transformer-based large language models during generation. It aggregates attention weights scaled by the magnitude of the value vector across all attention heads and summed across all layers, then maps those aggregated values to the opacity of prior tokens so users can see which past tokens influenced a generated token. The tool is implemented as a React app using Transformers.js with an instrumented ONNX model and is aimed at developers, researchers, and anyone interested in model interpretability and debugging.
A visualization of the attention mechanism in LLMs.
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Token-level attention visualization
Tap or hover over any generated token to see which past tokens affected its generation, with opacity encoding aggregated attention-derived values.
Aggregated attention metric
Computes attention weight scaled by the magnitude of the value vector, aggregated across attention heads and summed across layers to produce a single numeric value per past token used for visualization.
Interactive examples and pre-generated prompts
Includes pre-generated prompts (e.g., 'Office Move Summary', 'Debugging an Average Function') so examples load instantly in the browser without waiting for large model downloads.
Browser-based generation with Transformers.js
Generates text in-browser using Transformers.js and an instrumented ONNX model to expose internal values required for visualization.
Instrumented ONNX model hosted
Uses a modified ONNX file uploaded to the author's Hugging Face repo to expose internal tensors not available from the regular model outputs.
Open-source code available
Author provides the code in a GitHub repository (mentioned in the post) for reproducing or studying the implementation.
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Understand copying behavior
Visualize why LLMs can reproduce source data verbatim by seeing which source tokens are being attended to during generation (demonstrated in the 'Office Move Summary' example).
Debug model outputs
Inspect token influences to aid debugging of generated outputs and to understand where a model draws information from (illustrated with the 'Debugging an Average Function' example).
Model interpretability research and education
Serve as an educational tool for researchers and practitioners to explore attention patterns and how different tokens combine information across phrases.
Integrations
Transformers.js
Used to run generation in the browser and integrate model inference into the React app.
ONNX (instrumented model)
A modified ONNX model is used to expose internal tensors required for the visualization; the instrumented model is uploaded to the author's Hugging Face repo.
React
The visualization is implemented as a React application for interactive UI and rendering.
GitHub
Code for the project is available in a GitHub repository referenced in the post.
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Open the LLM Attention Visualization page in a browser with JavaScript enabled (the page shows 'Loading... (JavaScript required)').
- 2 Select or load one of the pre-generated prompts to view examples instantly.
- 3 Hover or tap on generated tokens to see which past tokens influenced that token's generation.
- 4 Visit the GitHub repository (linked from the post) to inspect the code and learn how the instrumented ONNX model and generation loop were implemented.
Support
docs
Code and implementation details are available in the GitHub repository referenced in the post.
author contact
The blog/site includes an 'Email Me' link for contacting the author (refer to the site header).
API
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