4 citations · 5 across the 25 of their papers we have counts for
7 papers · 2 filters
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers
Zhixiong Zhao, Zukang Xu, Xing Hu +3
Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evolving recurrent states poses…
All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs
Zhixiong Zhao, Zukang Xu, Guangyu Sun +2
Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment. Weight binarization is a p…
TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization
Zukang Xu, Xing Hu, Dawei Yang
As Large Language Models (LLMs) advance toward practical deployment, the Microscaling FP4 (MXFP4) format has emerged as a cornerstone for next-generation low-bit inference, owing t…
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
Zhixiong Zhao, Zukang Xu, Dawei Yang
Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder practical deployment. Binarization can compress weight…
MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
Zhixiong Zhao, Zukang Xu, Zhixuan Chen +1
Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binarization provides extreme efficie…
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu +2
Mixture of Experts (MoE) models have achieved great success by significantly improving performance while maintaining computational efficiency through sparse expert activation. Howe…
NLI:Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
Jiangyong Yu, Xiaomeng Han, Xing Hu +3
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks, but their deployment is often constrained by substantial memory footprints and c…