E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring
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:...
| Authors | Wenjun Wang, Yanggan Gu, Shuo Cai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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