  To remove redundant components of large language models (LLMs) without
incurring significant computational costs, this work focuses on single-shot
pruning without a retraining phase. We simplify the pruning process for
Transformer-based LLMs by identifying a depth-2 pruning structure that
functions independently. Additionally, we propose two inference-aware pruning
criteria derived from the optimization perspective of output approximation,
which outperforms traditional training-aware metrics such as gradient and
Hessian. We also introduce a two-step reconstruction technique to mitigate
pruning errors without model retraining. Experimental results demonstrate that
our approach significantly reduces computational costs and hardware
requirements while maintaining superior performance across various datasets and
models.
