22 papers
MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
Zhong Wang, Zukang Xu, Xing Hu +1
Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited resources. As an efficient model c…
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…
CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model
Houji Wen, Jiangyong Yu, Jun Li +1
Segment Anything Models (SAMs) are extensively used in computer vision for universal image segmentation, but deploying them on resource-constrained devices is challenging due to th…
R3-VAE: Reference Vector-Guided Rating Residual Quantization VAE for Generative Recommendation
Qiang Wan, Ze Yang, Dawei Yang +8
Generative Recommendation (GR) has gained traction for its merits of superior performance and cold-start capability. As the vital role in GR, Semantic Identifiers (SIDs) represent…
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…