Summary of Gptqt: Quantize Large Language Models Twice to Push the Efficiency, by Yipin Guo et al.
GPTQT: Quantize Large Language Models Twice to Push the Efficiency
by Yipin Guo, Yilin Lang, Qinyuan Ren
First submitted to arxiv on: 3 Jul 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 Medium Difficulty summary: This paper introduces a new post-training quantization method, GPTQT, to reduce memory usage and enhance processing speed in Large Language Models (LLMs). The method employs a progressive two-step approach, initially quantizing weights using Linear quantization to a relatively high bit, followed by converting obtained int weight to lower bit binary coding. A re-explore strategy is proposed to optimize initial scaling factor. Testing across various models and datasets confirms GPTQT’s effectiveness, with a reduction in perplexity by 4.01 on opt-66B and an increase in speed by 1.24 times on opt-30b. The results on Llama2 show that GPTQT is currently the best binary coding quantization method for such kind of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper helps computers process big language models more efficiently. It introduces a new way to store and use these models, making them take up less space and work faster. The method works by first reducing the amount of information stored in the model’s weights, then converting it into an easy-to-use format. Tests show that this method is effective and can reduce errors by 4% on one dataset and speed up processing by 24% on another. This new method can be used to improve language models like Llama2. |
Keywords
» Artificial intelligence » Perplexity » Quantization
