Summary of Bitstack: Any-size Compression Of Large Language Models in Variable Memory Environments, by Xinghao Wang et al.
BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
by Xinghao Wang, Pengyu Wang, Bo Wang, Dong Zhang, Yunhua Zhou, Xipeng Qiu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 paper presents BitStack, a novel training-free weight compression approach that enables megabyte-level trade-offs between memory usage and model performance. The authors introduce a decomposition-based method that iteratively decomposes weight matrices while considering the significance of each parameter. This approach allows for fine-grained size control and demonstrates consistent matches or surpasses strong quantization baselines, particularly at extreme compression ratios. The paper also provides extensive experiments across various tasks to showcase BitStack’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made significant advancements in many areas, but their deployment is often limited by memory constraints on local devices. This paper introduces a new approach called BitStack that helps overcome this limitation. It’s a way to compress model weights without needing to retrain the model, and it allows for fine control over how much memory is used. The authors tested BitStack on many different tasks and showed that it works well even when trying to use very little memory. |
Keywords
* Artificial intelligence * Quantization
