Dawei Yang

NLI : Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference

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

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

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

CMQuant: A Quantization-Aware Parameter-Efficient Fine-Tuning Framework for 4-Bit Consistency Models

DLLMQuant: A Post-Training Quantization Framework Tailored for Diffusion-Based Large Language Models

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

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

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

QuantGen: Parameter Generation for Controllable Model Quantization

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

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