Large language models(LLMs) containing tens of billions of parameters (or
even more) have demonstrated impressive capabilities in various NLP tasks.
However, substantial model size poses challenges to training, inference, and
deployment so that it is necessary to compress the model. At present, most
model compression for LLMs requires manual design of pruning features, which
has problems such as complex optimization pipeline and difficulty in retaining
the capabilities of certain parts of the model.Therefore, we propose a novel
pruning approach: firstly, a training set of a certain number of
architecture-accuracy pairs is established, and then a non-neural model is
trained as an accuracy predictor. Using the accuracy predictor to further
optimize the search space and search, the optimal model can be automatically
selected. Experiments show that our proposed approach is effective and
efficient. Compared with the baseline, the perplexity(PPL) on Wikitext2 and PTB
dropped by 9.48% and 5,76% respectively, and the average accuracy of MMLU
increased by 6.28%.

基于新的剪枝方法与准确度预测模型，本研究提出一种能够自动选择最佳模型的压缩方法，实验证明其有效性和高效性。相较于基准模型，Wikitext2 和 PTB 上的困惑度分别降低了 9.48% 和 5.76%，而平均 MMLU 准确度提高了 6.28%。

通过准确度预测器修剪大型语言模型

Pruning Large Language Models via Accuracy Predictor

Sounds carry an abundance of information about activities and events in our
everyday environment, such as traffic noise, road works, music, or people
talking. Recent machine learning methods, such as convolutional neural networks
(CNNs), have been shown to be able to automatically recognize sound activities,
a task known as audio tagging. One such method, pre-trained audio neural
networks (PANNs), provides a neural network which has been pre-trained on over
500 sound classes from the publicly available AudioSet dataset, and can be used
as a baseline or starting point for other tasks. However, the existing PANNs
model has a high computational complexity and large storage requirement. This
could limit the potential for deploying PANNs on resource-constrained devices,
such as on-the-edge sound sensors, and could lead to high energy consumption if
many such devices were deployed. In this paper, we reduce the computational
complexity and memory requirement of the PANNs model by taking a pruning
approach to eliminate redundant parameters from the PANNs model. The resulting
Efficient PANNs (E-PANNs) model, which requires 36\% less computations and 70\%
less memory, also slightly improves the sound recognition (audio tagging)
performance. The code for the E-PANNs model has been released under an open
source license.

通过裁剪方法减少预训练音频神经网络 (PANNs) 的冗余参数，降低了计算复杂度和存储需求，提高了声音识别表现，从而可在资源受限的设备上进行部署。