  In recent years, instruction tuning has gained increasing attention and
emerged as a crucial technique to enhance the capabilities of Large Language
Models (LLMs). To construct high-quality instruction datasets, many instruction
processing approaches have been proposed, aiming to achieve a delicate balance
between data quantity and data quality. Nevertheless, due to inconsistencies
that persist among various instruction processing methods, there is no standard
open-source instruction processing implementation framework available for the
community, which hinders practitioners from further developing and advancing.
To facilitate instruction processing research and development, we present
EasyInstruct, an easy-to-use instruction processing framework for LLMs, which
modularizes instruction generation, selection, and prompting, while also
considering their combination and interaction. EasyInstruct is publicly
released and actively maintained at https://github.com/zjunlp/EasyInstruct,
along with an online demo app and a demo video for quick-start, calling for
broader research centered on instruction data and synthetic data.
