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

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Pubblicato in:arXiv.org (Dec 16, 2024), p. n/a
Autore principale: Chan, Ryan Sze-Yin
Altri autori: Nanni, Federico, Williams, Angus R, Brown, Edwin, Burke-Moore, Liam, Chapman, Ed, Onslow, Kate, Sippy, Tvesha, Bright, Jonathan, Gabasova, Evelina
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Cornell University Library, arXiv.org
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022 |a 2331-8422 
035 |a 3096439091 
045 0 |b d20241216 
100 1 |a Chan, Ryan Sze-Yin 
245 1 |a Prompto: An open source library for asynchronous querying of LLM endpoints 
260 |b Cornell University Library, arXiv.org  |c Dec 16, 2024 
513 |a Working Paper 
520 3 |a 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 allowing faster experimentation, data generation and evaluation. prompto is released with an introductory video (https://youtu.be/lWN9hXBOLyQ) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto). 
653 |a Comparative studies 
653 |a Availability 
653 |a Python 
653 |a Large language models 
700 1 |a Nanni, Federico 
700 1 |a Williams, Angus R 
700 1 |a Brown, Edwin 
700 1 |a Burke-Moore, Liam 
700 1 |a Chapman, Ed 
700 1 |a Onslow, Kate 
700 1 |a Sippy, Tvesha 
700 1 |a Bright, Jonathan 
700 1 |a Gabasova, Evelina 
773 0 |t arXiv.org  |g (Dec 16, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3096439091/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2408.11847