MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models
Conventional Post-Training Quantization (PTQ) methods struggle with 4-bit Omni-modal Large Language Models (OLLMs) due to the extreme distribution heterogeneity and disparate outlier patterns across modalities. To address this, we propose MorphoQuant, a modality-aware PTQ framework engineered to preserve cross-modal morphology and mitigate outlier loss. Specifically, we introduce Distribution-Aware Bias Compensation (DABC), which selectively absorbs long-tailed outliers into channel-wise biases. This mechanism safeguards outlier magnitudes while maintaining high-precision discretization for dense inliers, thereby preserving accurate discretization across diverse modal distribution. Comple...
| Authors | Yue Wu, Changyuan Wang, Zixuan Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 5 June 2026 |
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