New C-PTQ method enhances multimodal LLM quantization efficiency

Researchers have developed C-PTQ, a novel post-training quantization method designed to improve the efficiency of multimodal large language models (MLLMs). This technique addresses performance degradation caused by outlier channels, which are highly sensitive to quantization. C-PTQ utilizes a Fisher-weighted objective, approximating second-order derivatives to better capture the impact of quantization on task-specific loss. Experiments on models like Qwen2.5VL, InternVL2, and LLaVA-OV across multiple benchmarks show C-PTQ's effectiveness in both weight-only and weight-activation quantization scenarios. AI

IMPACT Improves efficiency and deployment feasibility of multimodal LLMs by reducing computational costs.

RANK_REASON The cluster contains a research paper detailing a new method for model quantization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• C-PTQ
• Fisher-weighted Channel-wise Sensitivity
• Hessian
• InternVL2
• LLaVA-OV
• Lora
• MLLMs
• Post Training Quantization Preprocessing Method of Convolutional Neural Network via Outlier Removal
• Qwen2.5VL

• paper
• infra

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