1 citations · 1 across the 10 of their papers we have counts for
10 papers
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference
Haoqian Meng, Yilun Luo, Yafei Zhao +4
Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation compression remains challenging…
ID-LoRA: Efficient Low-Rank Adaptation Inspired by Matrix Interpolative Decomposition
Xindian Ma, Rundong Kong, Peng Zhang +2
LoRA has become a universal Parameter-Efficient Fine-Tuning (PEFT) technique that equips Large Language Models (LLMs) to adapt quickly to new tasks. However, when these models are…
Hierarchical Adaptive Eviction for KV Cache Management in Multimodal Language Models
Xindian Ma, Yidi Lu, Peng Zhang +1
The integration of visual information into Large Language Models (LLMs) has enabled Multimodal LLMs (MLLMs), but the quadratic memory and computational costs of Transformer archite…
Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2
Yilun Luo, Huaqing Zheng, Haoqian Meng +2
Huawei's openPangu-Embedded-1B and openPangu-Embedded-7B are variants of the openPangu large language model, designed for efficient deployment on Ascend NPUs. The 7B variant suppor…
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
Haoqian Meng, Yilun Luo, Yafei Zhao +3
The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing…
MicroMix: Efficient Mixed-Precision Quantization with Microscaling Formats for Large Language Models
Wenyuan Liu, Haoqian Meng, Yilun Luo +3
Quantization significantly accelerates inference in large language models (LLMs) by replacing original high-precision matrices with low-precision counterparts. Recent advances in w…
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
Jing Zhang, Shuzhen Sun, Peng Zhang +5
Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resourc…
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression
Wenyuan Liu, Xindian Ma, Peng Zhang +1
Post-Training Quantization (PTQ) is an effective technique for compressing Large Language Models (LLMs). While many studies focus on quantizing both weights and activations, it is…
3D-RPE: Enhancing Long-Context Modeling Through 3D Rotary Position Encoding
Xindian Ma, Wenyuan Liu, Peng Zhang +1
Inspired by the Bloch Sphere representation, we propose a novel rotary position encoding on a three-dimensional sphere, named 3D Rotary Position Encoding (3D-RPE). 3D-RPE is an adv…
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Yuxi Ren, Jie Wu, Peng Zhang +6
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on pond…