A training-free layer pruning framework for large vision-language models, combining token importance scoring with subspace-compensated weight projection, preserves most accuracy while speeding inference.
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Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers
A training-free layer pruning framework for large vision-language models, combining token importance scoring with subspace-compensated weight projection, preserves most accuracy while speeding inference.