Summary of Mixllm: Llm Quantization with Global Mixed-precision Between Output-features and Highly-efficient System Design, by Zhen Zheng et al.
MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
by Zhen Zheng, Xiaonan Song, Chuanjie Liu
First submitted to arxiv on: 19 Dec 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 presents a comprehensive analysis of quantization principles and their effects on the triangle of accuracy, memory consumption, and system efficiency in Large Language Models (LLMs). The authors propose MixLLM, a novel mixed-precision quantization method that identifies high-salience output features and assigns larger bit-widths accordingly. This approach achieves good accuracy with low memory consumption. To address system challenges, the authors design a two-step dequantization algorithm and present a software pipeline to optimize data type conversion and matrix multiplication. Experimental results show that MixLLM reduces perplexity (PPL) by 0.93 compared to state-of-the-art models, while maintaining state-of-the-art system efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making language models smaller without losing their ability to understand and generate text. The authors want to find the best way to do this, which is called “quantization.” They propose a new method that looks at how important each part of the model is and assigns more computer memory to the most important parts. This makes the model work better while using less memory. To make it easier to use, they also design a special way to convert the data type and perform matrix multiplication quickly. The results show that their method works well and is faster than other methods. |
Keywords
» Artificial intelligence » Perplexity » Precision » Quantization
