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arXiv Machine Learning By Jaemin Kim, Sungkyun Kim, Junyeol Lee, Jiwon Seo

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

Read the original on arXiv Machine Learning →

The paper introduces DASH-Q, a post‑training quantization method that uses a diagonal Hessian approximation and iterative weighted least squares to reduce noise in curvature estimates. By discarding noisy cross‑channel dependencies, DASH‑Q preserves salient feature power and achieves superior performance in ultra low‑bit quantization. Across five large language models, it improves zero‑shot accuracy by an average of 7.01% and up to 14.01% over the strongest baselines, even with very small calibration datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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