Zukang Xu

KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling

SAES-SVD: Self-Adaptive Suppression of Accumulated and Local Errors for SVD-based LLM Compression

BiMoE：Pushing the Limit of Post-Training Quantization for MoE-based LLMs

MSAVQ: Multi-dimensional Sensitivity-Aware Vector Quantization for Ultra-Low-Bit Vision-Language Models

Towards W2A4 LLM Inference: Hybrid SQ-VQ Framework with Adaptive Error Compensation

RSAVQ: Riemannian Sensitivity-Aware Vector Quantization for Large Language Models

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

OSTQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance

MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization

RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization

I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models