1 citations · 1 across the 20 of their papers we have counts for
21 papers
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…
ACE: Adaptive Calibration-Free Expert Skipping for MoE-based LLMs
Zukang Xu, Zhixiong Zhao, Xing Hu +5
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs), yet fixed top-k routing activates the same number of expert slots for…
TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
Zhixiong Zhao, Zukang Xu, Zhixuan Chen +3
Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a prom…
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…
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…
Scaling Embeddings Outperforms Scaling Experts in Language Models
Hong Liu, Jiaqi Zhang, Chao Wang +13
While Mixture-of-Experts (MoE) architectures have become the standard for sparsity scaling in large language models, they increasingly face diminishing returns and system-level bot…
SAES-SVD: Self-Adaptive Suppression of Accumulated and Local Errors for SVD-based LLM Compression
Xing Hu, Dawei Yang, Yuan Cheng +2
The rapid growth in the parameter scale of large language models (LLMs) has created a high demand for efficient compression techniques. As a hardware-agnostic and highly compatible…
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…
FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection
Jiangyong Yu, Changyong Shu, Sifan Zhou +4
Camera-based multi-view 3D detection is crucial for autonomous driving. PETR and its variants (PETRs) excel in benchmarks but face deployment challenges due to high computational c…
FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error
Fengjuan Wang, Zhiyi Su, Xingzhu Hu +2
Training large Mixture-of-Experts (MoE) models remains computationally prohibitive due to their extreme compute and memory demands. Although low-precision training promises to acce…
CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models
Zhuofan Chen, Jiyuan He, Yichi Zhang +4
Mathematical reasoning poses significant challenges for Large Language Models (LLMs) due to its demand for multi-step reasoning and abstract conceptual integration. While recent te…
MoEQuant: Enhancing Quantization for Mixture-of-Experts Large Language Models via Expert-Balanced Sampling and Affinity Guidance
Xing Hu, Zhixuan Chen, Dawei Yang +5
Mixture-of-Experts (MoE) large language models (LLMs), which leverage dynamic routing and sparse activation to enhance efficiency and scalability, have achieved higher performance…
RWKVQuant: Quantizing the RWKV Family with Proxy Guided Hybrid of Scalar and Vector Quantization
Chen Xu, Yuxuan Yue, Zukang Xu +6
RWKV is a modern RNN architecture with comparable performance to Transformer, but still faces challenges when deployed to resource-constrained devices. Post Training Quantization (…
FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection
Jiangyong Yu, Changyong Shu, Sifan Zhou +4
Camera-based multi-view 3D detection is crucial for autonomous driving. PETR and its variants (PETRs) excel in benchmarks but face deployment challenges due to high computational c…
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization
JiangYong Yu, Sifan Zhou, Dawei Yang +7
Multimodal large language models (MLLMs) have garnered widespread attention due to their ability to understand multimodal input. However, their large parameter sizes and substantia…
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
Xing Hu, Yuan Cheng, Dawei Yang +6
Post-training quantization (PTQ) has emerged as a widely adopted technique for compressing and accelerating Large Language Models (LLMs). The major challenge in LLM quantization is…
MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods
Zukang Xu, Yuxuan Yue, Xing Hu +7
Mamba is an efficient sequence model that rivals Transformers and demonstrates significant potential as a foundational architecture for various tasks. Quantization is commonly used…
I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
Xing Hu, Yuan Cheng, Dawei Yang +4
Post-training quantization (PTQ) serves as a potent technique to accelerate the inference of large language models (LLMs). Nonetheless, existing works still necessitate a considera…
Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder
Sifan Zhou, Zhihang Yuan, Dawei Yang +5
Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attenti…
Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splitting
Dawei Yang, Ning He, Xing Hu +4
Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources. Network quantization is a powerful…