  We empirically study a simple layer-pruning strategy for popular families of
open-weight pretrained LLMs, finding minimal degradation of performance on
different question-answering benchmarks until after a large fraction (up to
half) of the layers are removed. To prune these models, we identify the optimal
block of layers to prune by considering similarity across layers; then, to
"heal" the damage, we perform a small amount of finetuning. In particular, we
use parameter-efficient finetuning (PEFT) methods, specifically quantization
and Low Rank Adapters (QLoRA), such that each of our experiments can be
performed on a single A100 GPU. From a practical perspective, these results
suggest that layer pruning methods can complement other PEFT strategies to
further reduce computational resources of finetuning on the one hand, and can
improve the memory and latency of inference on the other hand. From a
scientific perspective, the robustness of these LLMs to the deletion of layers
implies either that current pretraining methods are not properly leveraging the
parameters in the deeper layers of the network or that the shallow layers play
a critical role in storing knowledge.
