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New ShamAN-Q method slashes LLM weights to sub-1-bit

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ShamAN-Q, a novel sub-1-bit post-training quantization method for large language models. This technique enhances NanoQuant by incorporating a dense curvature metric derived from the Shampoo optimizer, which uses the empirical Fisher information matrix. ShamAN-Q aims to improve model efficiency and performance by optimizing weight reconstruction and redistributing bits across layers, showing significant reductions in perplexity on the Qwen3-Base model. AI

IMPACT This research could lead to more efficient LLMs that require less computational resources for deployment and inference.

RANK_REASON The cluster contains an academic paper detailing a new method for LLM quantization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ShamAN-Q method slashes LLM weights to sub-1-bit

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The cluster contains an academic paper detailing a new method for LLM quantization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jonathan Mei, Sang Hyub Kim, Oliver Knitter, Chi Chen, Martin Roetteler ·

    ShamAN-Q: Shampoo Augmented NanoQuant for Sub-1-bit LLM Weights

    arXiv:2609.38521v1 Announce Type: cross Abstract: We introduce ShamAN-Q, a sub-1-bit post-training quantization method that extends NanoQuant by replacing each its diagonal reconstruction geometry with a tractable dense curvature metric, using a general paradigm popularized by th…

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