Summary of Spinquant: Llm Quantization with Learned Rotations, by Zechun Liu et al.
SpinQuant: LLM quantization with learned rotations
by Zechun Liu, Changsheng Zhao, Igor Fedorov, Bilge Soran, Dhruv Choudhary, Raghuraman Krishnamoorthi, Vikas Chandra, Yuandong Tian, Tijmen Blankevoort
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach called SpinQuant is proposed in this work, which incorporates learned rotation matrices to achieve optimal quantized network accuracy. The technique leverages post-training quantization (PTQ) and rotates activation or weight matrices to remove outliers, enhancing quantization accuracy. Experimental results show that SpinQuant narrows the accuracy gap on zero-shot reasoning tasks with full precision, surpassing existing methods such as LLM-QAT, SmoothQuant, and QuaRot. For instance, SpinQuant reduces the gap by up to 45.1% relative to QuaRot for LLaMA-3 8B models that are hard to quantize. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make Large Language Models (LLMs) use less memory and energy while keeping their accuracy high. By rotating certain parts of the model, called activation or weight matrices, they can get rid of noisy data points that affect the results. The approach is called SpinQuant and it works well with different types of LLMs, even the tricky ones. In fact, SpinQuant does better than other methods in some cases. |
Keywords
» Artificial intelligence » Llama » Precision » Quantization » Zero shot
