3 papers
cs.LG2026
LoPRo: Enhancing Low-Rank Quantization via Permuted Block-Wise Rotation
Hongyaoxing Gu, Lijuan Hu, Liye Yu +2
Post-training quantization (PTQ) enables effective model compression while preserving relatively high accuracy. Current weight-only PTQ methods primarily focus on the challenging s…
cs.LG2026
FLRQ: Faster LLM Quantization with Flexible Low-Rank Matrix Sketching
Hongyaoxing Gul, Lijuan Hu, Shuzi Niu +1
Traditional post-training quantization (PTQ) is considered an effective approach to reduce model size and accelerate inference of large-scale language models (LLMs). However, exist…
cs.MS2024
A method of using RSVD in residual calculation of LowBit GEMM
Hongyaoxing Gu
The advancements of hardware technology in recent years has brought many possibilities for low-precision applications. However, the use of low precision can introduce significant c…