The popularity of LLaMAand 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 theSheared-LLaMAseries,
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 onlyof 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.111Please find our code and models athttps://github.com/princeton-nlp/LLM-Shearing.