Easyinstruct: An Easy-to-use Instruction Processing Framework For Large Language Models

Ou Yixin, Zhang Ningyu, Gui Honghao, Xu Ziwen, Qiao Shuofei, Xue Yida, Fang Runnan, Liu Kangwei, Li Lei, Bi Zhen, Zheng Guozhou, Chen Huajun. Arxiv 2024

[Paper] [Code]    
Attention Mechanism Has Code Model Architecture Prompting Tools

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.

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