EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 28·5 min read
You Could Have Come Up with Kimi Delta Attention
TL;DR
Kimi Delta Attention (KDA) is a new linear attention variant that simplifies the understanding of complex attention mechanisms in AI models. It builds on the DeltaNet family, enhancing performance in models like Qwen and Kimi.
✦ Why It Matters
Explore Kimi Delta Attention to enhance your AI model's efficiency and interpretability today.
Key Takeaways
- Adopt KDA for improved memory management in neural networks.
- Utilize vectorized retention for selective forgetting in attention mechanisms.
- Implement both recurrent and chunkwise execution for optimal performance.
- Explore KDA's application in long sequence processing to reduce memory overhead.
How It Works
Kimi Delta Attention (KDA) enhances linear attention by introducing a vectorized retention mechanism that allows for selective forgetting of memory states. This is achieved through a delta-rule correction, which updates the memory based on the difference between predicted and actual values, ensuring that only relevant information is retained.
The model operates in two regimes: recurrent for short sequences and chunkwise for longer sequences, optimizing performance based on the task requirements.