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A framework for few-shot language model evaluation , author=

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PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

cs.CL · 2026-07-08 · conditional · novelty 5.0

Activation-percentile-based per-layer sparsity allocation improves LLaMA-2-7B pruning by 15% perplexity over uniform Wanda, while gradient-based allocation fails worse than random.

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  • PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning cs.CL · 2026-07-08 · conditional · none · ref 27

    Activation-percentile-based per-layer sparsity allocation improves LLaMA-2-7B pruning by 15% perplexity over uniform Wanda, while gradient-based allocation fails worse than random.