Summary of Swiftkv: Fast Prefill-optimized Inference with Knowledge-preserving Model Transformation, by Aurick Qiao et al.
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation
by Aurick Qiao, Zhewei Yao, Samyam Rajbhandari, Yuxiong He
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents SwiftKV, a novel model transformation and distillation procedure that reduces the time and cost of processing prompt tokens while preserving high-quality generated tokens. The approach combines three key mechanisms: SingleInputKV, AcrossKV, and a knowledge-preserving distillation procedure. SwiftKV is designed to adapt existing large language models (LLMs) for efficient inference, achieving significant reductions in compute and memory requirements without compromising quality. Specifically, the authors demonstrate that SwiftKV can reduce the compute requirement of prefill by 50% and the memory requirement of the KV cache by 62.5%, while maintaining minimal accuracy impact across a range of tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SwiftKV is a new way to make language models work better and faster. It helps big language models do their job without using too much computer power or memory. This makes it possible to generate text, summarize documents, and do other tasks more quickly and efficiently. The authors show that SwiftKV can reduce the time it takes to process text by half while keeping the quality of the generated text good. |
Keywords
» Artificial intelligence » Distillation » Inference » Prompt
