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SignRoundV2: Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs research paper by Intel, 2025

Intel · Dec 4, 2025 · Training and scaling · 0 citations · 17 upvotes · unverified 10 months ago

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What it shows

SignRoundV2, a post-training quantization framework, achieves competitive accuracy for Large Language Models at extremely low-bit quantization through layer-wise bit allocation and pre-tuning scale search.

By Wenhua Cheng, Weiwei Zhang, Heng Guo and 1 more · arXiv 2512.04746 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Wenhua Cheng, Weiwei Zhang, Heng Guo and 1 more
arXiv
2512.04746 · PDF
Citations
0, 0 influential · Semantic Scholar
Upvotes
17 · Hugging Face
Code
github.com/intel/auto-round
Lab
Intel · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 0Oct 5, 2026today
Citationsfirst count: 0Oct 5, 2026today
New paperFound by the weekly scan, unverifiedOct 5, 2026today

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