EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 8·5 min read
Show HN: LLM Attention Visualization
TL;DR
A new visualization tool for large language models (LLMs) allows users to see how previous tokens influence the generation of new tokens. This tool highlights the attention weights and patterns in token generation, revealing insights into the model's decision-making process.
✦ Why It Matters
Explore this visualization tool to better understand how LLMs generate text and improve your model interactions.
Key Takeaways
- Use the visualization tool to understand token influence in LLMs.
- Explore how attention weights affect text generation.
- Modify .onnx files to access internal model values.
- Pre-generate prompts for instant loading in web applications.
How It Works
The visualization tool displays how attention weights from previous tokens influence the generation of new tokens. By calculating attention weights and scaling them with value vectors, the tool shows which past tokens are most relevant, allowing users to see patterns in how information is copied or combined.