Summary of Inattention: Linear Context Scaling For Transformers, by Joseph Eisner
InAttention: Linear Context Scaling for Transformers
by Joseph Eisner
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the scalability issue of transformer models with self-attention mechanisms by introducing InAttention, a novel approach that scales linearly with context length during inference. By replacing self-attention with InAttention, the modified decoder-only transformer reduces VRAM usage quadratically, enabling efficient handling of long sequences on consumer GPUs. The proposed method is benchmarked and shown to significantly reduce VRAM requirements, improving performance on long sequences without incurring high training costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InAttention is a new way to help transformer models handle really long pieces of information. Right now, these models use something called self-attention that makes them use a lot of memory. This makes it hard for them to work with super long texts or sequences. InAttention changes this by only looking at the initial state when making decisions, which means it uses much less memory. This is great news because it could help us train these models to understand really long things without using too many resources. |
Keywords
» Artificial intelligence » Context length » Decoder » Inference » Self attention » Transformer
