Summary of Claq: Pushing the Limits Of Low-bit Post-training Quantization For Llms, by Haoyu Wang et al.
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
by Haoyu Wang, Bei Liu, Hang Shao, Bo Xiao, Ke Zeng, Guanglu Wan, Yanmin Qian
First submitted to arxiv on: 27 May 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 The paper presents a novel framework, Column-Level Adaptive weight Quantization (CLAQ), for reducing memory costs and improving computational efficiency of Large Language Models (LLMs). The CLAQ framework introduces three adaptive strategies to overcome the limitations of existing methods in low-bit scenarios. Specifically, it uses K-Means clustering-based algorithms to dynamically generate quantization centroids for each column of a parameter matrix, an outlier-guided adaptive precision search strategy to assign varying bit-widths to different columns, and a dynamic outlier reservation scheme to retain some parameters in their original float point precision. The framework is evaluated on mainstream open-source LLMs, including LLaMA-1, LLaMA-2, and Yi, achieving state-of-the-art results across different bit settings, particularly in extremely low-bit scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer models that understand human language more efficient. It presents a new way to reduce the memory needed for these models while keeping their performance good. The method uses three strategies to adapt to different situations and choose the best way to represent each part of the model. This helps the model work better in low-bit scenarios, which is important because it means the model can be used on devices with limited storage. The new method was tested on several well-known language models and showed better results than previous methods. |
Keywords
» Artificial intelligence » Clustering » K means » Llama » Precision » Quantization
