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RGSQ: Riemannian Geometry-Sensitive Quantization for Large Vision-Language Models
Large vision-language models (VLMs) can be efficiently deployed under stringent memory and latency constraints through post training quantization (PTQ). However, most PTQ methods are designed for unimodal large language models (LLMs). These methods treat quantization errors as isotropic perturbation
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