Different calibration objectives produce distinct layer pruning patterns in LLMs, while search algorithms converge to similar solutions under a fixed objective.
A simple and effective pruning approach for large language models
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Putri is a structured pruning technique for LLMs that compensates for pruning errors via weight updates and sequential processing while pruning at the attention-head level to reach state-of-the-art results at extreme sparsity.
citing papers explorer
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Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning
Different calibration objectives produce distinct layer pruning patterns in LLMs, while search algorithms converge to similar solutions under a fixed objective.
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Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Putri is a structured pruning technique for LLMs that compensates for pruning errors via weight updates and sequential processing while pruning at the attention-head level to reach state-of-the-art results at extreme sparsity.