Summary of Wkvquant: Quantizing Weight and Key/value Cache For Large Language Models Gains More, by Yuxuan Yue et al.
WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More
by Yuxuan Yue, Zhihang Yuan, Haojie Duanmu, Sifan Zhou, Jianlong Wu, Liqiang Nie
First submitted to arxiv on: 19 Feb 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 tackles the challenges facing Large Language Models (LLMs) by focusing on quantization, a technique that reduces memory consumption and computational demands. The authors critically analyze existing approaches, highlighting limitations in balancing accuracy and efficiency. They propose WKVQuant, a PTQ framework specifically designed for LLMs, incorporating past-only quantization to improve attention computation and two-dimensional quantization strategy to handle KV cache distribution. Additionally, they introduce cross-block reconstruction regularization for parameter optimization. Experimental results show that WKVQuant achieves almost comparable memory savings to weight-activation quantization while approaching the performance of weight-only quantization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to use Large Language Models on computers with limited memory and processing power. The authors found that existing methods for shrinking these massive models didn’t quite work as well as they should. So, they came up with a new approach called WKVQuant that can be used specifically for language models. It has two special features: it only looks at the past to figure out what words might come next, and it uses a clever way to shrink the KV cache (a part of the model) while keeping it working well. The results show that this new method is almost as good as some other methods that are already popular. |
Keywords
* Artificial intelligence * Attention * Optimization * Quantization * Regularization
