The popularity of LLaMA (Touvron et al., 2023a;b) and other recently emerged
moderate-sized large language models (LLMs) highlights the potential of
building smaller yet powerful LLMs. Regardless, the cost of training such
models from scratch on trillions of tokens remains high. In this work, we study
structured pruning as an effective means to develop smaller LLMs from
pre-trained, larger models. Our approach employs two key techniques: (1)
targeted structured pruning, which prunes a larger model to a specified target
shape by removing layers, heads, and intermediate and hidden dimensions in an
end-to-end manner, and (2) dynamic batch loading, which dynamically updates the
composition of sampled data in each training batch based on varying losses
across different domains. We demonstrate the efficacy of our approach by
presenting the Sheared-LLaMA series, pruning the LLaMA2-7B model down to 1.3B
and 2.7B parameters. Sheared-LLaMA models outperform state-of-the-art
open-source models of equivalent sizes, such as Pythia, INCITE, and OpenLLaMA
models, on a wide range of downstream and instruction tuning evaluations, while
requiring only 3% of compute compared to training such models from scratch.
This work provides compelling evidence that leveraging existing LLMs with
structured pruning is a far more cost-effective approach for building smaller
LLMs.

利用结构修剪技术从预训练的大型语言模型生成更小但功能强大的语言模型。这项工作通过展示 Sheared-LLaMA 系列，在仅使用相当于从头训练这些模型所需计算量的 3％的情况下，将 LLaMA2-7B 模型修剪为 1.3B 和 2.7B 参数，优于等规模的开源模型，并提供了使用结构修剪来构建更小型语言模型更具成本效益的佐证。