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Prompto: An open source library for asynchronous querying of LLM endpoints

Authors :
Chan, Ryan Sze-Yin
Nanni, Federico
Brown, Edwin
Chapman, Ed
Williams, Angus R.
Bright, Jonathan
Gabasova, Evelina
Publication Year :
2024

Abstract

Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and enabling faster experimentation and evaluation. prompto is released with an introductory video (https://youtu.be/-eZAmlV4ypk) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto).

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.11847
Document Type :
Working Paper