Summary of Advancing Multimodal Large Language Models with Quantization-aware Scale Learning For Efficient Adaptation, by Jingjing Xie et al.
Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation
by Jingjing Xie, Yuxin Zhang, Mingbao Lin, Liujuan Cao, Rongrong Ji
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 study explores the use of parameter quantization to reduce resource constraints in multimodal large language models during vision-language instruction tuning. A novel method called QSLAW (Quantization-aware Scale LeArning with multimodal Warmup) is introduced, which consists of two key innovations: group-wise scale factors for quantized LLM weights and a multimodal warmup that integrates linguistic and multimodal training samples. The authors demonstrate that models quantized by QSLAW perform similarly to or better than full-precision counterparts, while reducing tuning time and GPU consumption by up to 1.4 times. The study’s findings have implications for the efficient training of multimodal language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make big computers learn faster when they’re doing two tasks together: understanding text and looking at pictures. They came up with a new way called QSLAW that helps make this learning happen more efficiently. This method has two important parts: finding the right scales for the computer’s weight (which is like adjusting the volume) and gradually teaching the computer to understand both text and pictures. The researchers tested this method and found that it works just as well as using the whole computer, but takes much less time and uses fewer resources. |
Keywords
» Artificial intelligence » Instruction tuning » Precision » Quantization
