  Large language models (LLMs) based on transformer are witnessing a notable
trend of size expansion, which brings considerable costs to both model training
and inference. However, existing methods such as model quantization, knowledge
distillation, and model pruning are constrained by various issues, including
hardware support limitations, the need for extensive training, and alterations
to the internal structure of the model. In this paper, we propose a concise
layer-wise pruning method called \textit{Layer Collapse (LaCo)}, in which rear
model layers collapse into a prior layer, enabling a rapid reduction in model
size while preserving the model structure. Comprehensive experiments show that
our method maintains an average task performance of over 80\% at pruning ratios
of 25-30\%, significantly outperforming existing state-of-the-art structured
pruning methods. We also conduct post-training experiments to confirm that the
proposed pruning method effectively inherits the parameters of the original
model. Finally, we discuss our motivation from the perspective of layer-wise
similarity and evaluate the performance of the pruned LLMs across various
pruning ratios.
