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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…