Summary of Gwq: Gradient-aware Weight Quantization For Large Language Models, by Yihua Shao et al.
GWQ: Gradient-Aware Weight Quantization for Large Language Models
by Yihua Shao, Siyu Liang, Zijian Ling, Minxi Yan, Haiyang Liu, Siyu Chen, Ziyang Yan, Chenyu Zhang, Haotong Qin, Michele Magno, Yang Yang, Zhen Lei, Yan Wang, Jingcai Guo, Ling Shao, Hao Tang
First submitted to arxiv on: 30 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 The proposed gradient-aware weight quantization (GWQ) approach addresses the challenge of deploying large language models on edge devices by leveraging gradients to localize outliers. This method retains weights corresponding to top 1% outliers at FP16 precision while storing remaining non-outlier weights in a low-bit format, achieving better performance compared to current quantization methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can solve complex tasks impressively, but their many parameters make deployment on edge devices difficult. To fix this, researchers developed GWQ, which uses gradients to find outliers and keep important ones at higher precision. This helps the model run faster and use less memory while still being accurate. The method works well with multiple language models and can even be used for multimodal models. |
Keywords
* Artificial intelligence * Precision * Quantization
