A novel PTQ-based MPQ framework tailored for SR models is proposed that outperforms existing PTQ-based methods by 1.26 dB in peak signal-to-noise ratio (PSNR) on the Urban100 dataset and introduces a dynamic activation range normalization that alleviates the distributional imbalance caused by the absence of BN.
HAMP-LIC is proposed, a Hessian-aware mixed-precision post-training quantization (PTQ) framework with a four-stage optimization strategy that consistently outperforms existing fixed- and mixed-precision PTQ methods across multiple datasets while completely eliminating cross-platform encoding-decoding errors.
Hybrid vision backbones such as MambaVision combine convolutional layers, Mamba blocks (based on selective state space models), and self-attention within a single architecture. However, the heterogeneous operator composition of such models poses new challenges for post-training quantization (PTQ): uniform bit-width ass...
Daisuke Fujita, Ryuto Ishibashi, Ryuto Tanigawa et al.· International Conference on...· 0 citations
Efficient Tuning Before Quantization (ETBQ) is proposed, a pre-conditioning tuning stage for Stochastic Gradient Descent (SGD)-optimized models before PTQ, which improves low-bit PTQ across diverse tasks.
Peng Xia, Junbiao Pang, Muhammad Ayub Sabir· arXiv.org· 0 citations
Across five models from 1B to 32B, SoftWater outperforms the released WaterSIC quantizer at matched head rates on 59 of 60 test points, using none of that pipeline's refinements and cutting head-induced KL by $6.5\times$--$8.3\times at 2 bits.
A cross-modal structural sensitivity asymmetry in VLMs is revealed and SeGO is proposed, a unified structural sensitivity-aware sparse optimization framework that achieves the balance among model parameter amount, quantization accuracy and scaling factors’ search efficiency on InternVL2 and LLaVA series.
Tian-Qi Zhao, Xin-Rui Cheng, Yang Su et al.· Proceedings of the Thirty-Fi...· 0 citations
Across diverse tasks, models, and datasets, HyQuant maintains nearly lossless accuracy with an extremely simple design, demonstrating the efficiency and practical feasibility of hybrid quantization for LLM attention.
Jiarui Ding, Bin Xing, Yu Zhang et al.· 0 citations
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