| title | E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring |
|---|---|
| authors | Wenjun Wang, Yanggan Gu, Shuo Cai, Yuanyi Wang, Pengkai Wang, Jianmin Wu, Hongxia Yang |
| date | 2026-05-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.16882 |
| affiliation | The Hong Kong Polytechnic University |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model merging has become an increasingly practical low-resource strategy for integrating multiple task- or domain-specialized experts into a single model without joint training or multi-model serving. Together, quantization and model merging enable an efficient low-resource deployment pipeline by integrating multiple experts into one low-bit model. We formulate this setting as Post-Merge Quantization (PMQ). We show that directly applying post-training quantization (PTQ) to a merged model is unreliable because two distinct deviations are coupled: the quantization deviation introduced by low-bit reconstruction and the expert-relative merging deviation inherited from model merging. To mitigate these deviations, we propose E-PMQ, an expert-guided PMQ framework that uses source expert weights to provide expert- guided output targets during layer-wise calibration, together with merged-weight anchoring to stabilize the calibration and preserve the integrated behavior of the merged model. On CLIP-ViT-B/32 eight-task merging, E-PMQ improves 4-bit GPTQ from 65.0% to 73.6% under Task Arithmetic and from 69.1% to 74.8% under TIES-Merging. On harder settings, E-PMQ improves GPTQ from 34.8% to 76.7% on 20-task CLIP-ViT-L/14 and from 78.26% to 83.34% on FLAN-T5- base GLUE. These results demonstrate that E-PMQ enables effective post-merge quantization and low-bit deployment. |
cite
@misc{wang2026e,
title={E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring},
author={Wenjun Wang and Yanggan Gu and Shuo Cai and Yuanyi Wang and Pengkai Wang and Jianmin Wu and Hongxia Yang},
year={2026},
eprint={2605.16882},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.16882}
}