Over the past few years, as large language models have ushered in an era of
intelligence emergence, there has been an intensified focus on scaling
networks. Currently, many network architectures are designed manually, often
resulting in sub-optimal configurations. Although Neural Architecture Search
(NAS) methods have been proposed to automate this process, they suffer from low
search efficiency. This study introduces Differentiable Model Scaling (DMS),
increasing the efficiency for searching optimal width and depth in networks.
DMS can model both width and depth in a direct and fully differentiable way,
making it easy to optimize. We have evaluated our DMS across diverse tasks,
ranging from vision tasks to NLP tasks and various network architectures,
including CNNs and Transformers. Results consistently indicate that our DMS can
find improved structures and outperforms state-of-the-art NAS methods.
Specifically, for image classification on ImageNet, our DMS improves the top-1
accuracy of EfficientNet-B0 and Deit-Tiny by 1.4% and 0.6%, respectively, and
outperforms the state-of-the-art zero-shot NAS method, ZiCo, by 1.3% while
requiring only 0.4 GPU days for searching. For object detection on COCO, DMS
improves the mAP of Yolo-v8-n by 2.0%. For language modeling, our pruned
Llama-7B outperforms the prior method with lower perplexity and higher
zero-shot classification accuracy. We will release our code in the future.

基于可微模型缩放的神经架构搜索方法能够提高搜索网络的效率并找到改进的结构，在不同任务和不同网络架构下具有一致的结果并超越了现有的神经架构搜索方法。