BASE-Q combines bias correction and asymmetric scaling under fixed rotations to improve 4-bit weight-activation quantization, narrowing the accuracy gap to full precision by up to 50.5% over prior rotation-based methods.
Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
BASE-Q combines bias correction and asymmetric scaling under fixed rotations to improve 4-bit weight-activation quantization, narrowing the accuracy gap to full precision by up to 50.5% over prior rotation-based methods.