poloclub/transformer-explainer
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization
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SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
Ranks #94 of 2,531 repositories GStars tracks this week (top 4%).
No releases in the last 90 days; the most recent was 858 days ago, with 12 contributors on GitHub.
Peaked at #31 in the weekly ranking the week of Feb 8, its best in the past year.
Use Cases & Benefits
- Provides an interactive visualization tool to learn how Transformer-based language models like GPT-2 generate text.
- Enables real-time experimentation with live GPT-2 models directly in the browser for intuitive understanding of Transformer internals.
- Use for educational purposes to teach students and researchers the mechanics of Transformer models through hands-on interaction.
- Use for AI developers to debug and analyze Transformer model behavior by visualizing token predictions and attention mechanisms.
- Use for AI enthusiasts to explore and experiment with text generation processes without requiring complex setup or deep technical knowledge.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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: Interactive Learning of Text-Generative Models. Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Zijie J. Wang, Seongmin Lee, Benjamin Hoover, Duen Horng Chau. Poster, IEEE VIS 2024.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+719 stars over 86 days across 11 snapshot days; best day +29 on Oct 14.
Forks
1.0K+113
Open issues
23+1
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Oct 18, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| koharu-rs/koharu | 6.4K | +192 | 63 | 30 days ago |
| poloclub/transformer-explainer | 9.0K | +143 | 75 | 858 days ago |
| vllm-project/vllm | 94.9K | +0 | 75 | 3 days ago |
| sapientinc/HRM | 12.7K | +0 | 75 | — |
| Project-MONAI/MONAI | 8.8K | +0 | 75 | 20 days ago |
| opengeos/segment-geospatial | 4.2K | +0 | 75 | 59 days ago |
| Category median | 8.8K | +0 | 75 | — |
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