Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate

Abstract:Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces memory footprint without retraining by leveraging a small calibration set. Recent Hessian-based PTQ methods compensate quantization error via cross-channel dependencies, but such approaches degrade at low bit-widths due to noisy curvature estimates from limited calibration data. We propose DASH-Q, a robust PTQ framework using diagonal Hessian approximation and iterative weighted least squares. By discarding noise-prone dependencies, DASH-Q filters sampling noise while prioritizing the preservation of salient feature power. We outperform other PTQ baselines in ultra low-bit regime, improving zero-shot accuracy by 7.01% on average and up to 14.01% over the strongest baselines across five baseline LLM models, while showing robust and stable performance with very small calibration data.

Machine Learning (cs.LG)

arXiv:2604.13806 [cs.LG]

(or arXiv:2604.13806v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.13806 Focus to learn more arXiv-issued DOI via DataCite

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