Summary of Accurate Lora-finetuning Quantization Of Llms Via Information Retention, by Haotong Qin et al.
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
by Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xianglong Liu, Michele Magno
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a novel approach, IR-QLoRA, to improve the accuracy of quantized large language models (LLMs) during LoRA finetuning. The method relies on two key technologies: statistics-based Information Calibration Quantization and finetuning-based Information Elastic Connection. These technologies allow the quantized LLM parameters to retain original information accurately while utilizing elastic representation transformation with diverse information. Experimental results show that IR-QLoRA can significantly improve accuracy across LLaMA and LLaMA2 families under 2-4 bit-widths, achieving a 1.4% improvement on MMLU compared to state-of-the-art methods. The approach requires only a tiny additional time consumption, revealing its efficiency. IR-QLoRA is also versatile, compatible with various frameworks, and brings general accuracy gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper introduces a new way to make large language models work better on devices that have limited resources. They want to keep the original information of these models while still making them smaller and more efficient. The method uses two main ideas: one helps the model remember its original information, and the other allows it to adapt to different situations. Tests show that this approach can make the models 1.4% better at understanding text compared to existing methods. It also doesn’t take much extra time or effort. |
Keywords
* Artificial intelligence * Llama * Lora * Quantization
