Architecture
Attention Sinks
Attention sinks are the first few tokens of a sequence, which LLMs learn to send large attention scores to even when those tokens carry no special meaning.
In depth
Attention sinks are the first few tokens of a sequence, which LLMs learn to send large attention scores to even when those tokens carry no special meaning. The StreamingLLM paper found that plain window attention breaks once the oldest tokens are evicted from the KV cache, and that keeping the KV entries of those initial tokens restores stable output. That trick let models like Llama 2 and Falcon generate over 4 million tokens without fine-tuning.
Primary source: arxiv.org
Example
That trick let models like Llama 2 and Falcon generate over 4 million tokens without fine-tuning.
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FAQ
Common questions
What is Attention Sinks?
Attention sinks are the first few tokens of a sequence, which LLMs learn to send large attention scores to even when those tokens carry no special meaning.
Why does Attention Sinks matter for AI developers?
Attention Sinks sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Attention Sinks?
Developers Digest publishes tutorials and videos that cover Architecture topics including Attention Sinks. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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Autoregressive Model
A model that generates a sequence one element at a time, feeding each output back in as input for the next step.
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Grouped-Query Attention (GQA)
Grouped-query attention is a middle ground between multi-head and multi-query attention: query heads are split into groups, and each group shares one key head and one value head.
Architecture
FlashAttention
An exact attention algorithm that produces the same result as standard attention but reorganizes the work around GPU memory.
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