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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.11847 |
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| _version_ | 1866915064827609088 |
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| author | Chan, Ryan Sze-Yin Nanni, Federico Williams, Angus R. Brown, Edwin Burke-Moore, Liam Chapman, Ed Onslow, Kate Sippy, Tvesha Bright, Jonathan Gabasova, Evelina |
| author_facet | Chan, Ryan Sze-Yin Nanni, Federico Williams, Angus R. Brown, Edwin Burke-Moore, Liam Chapman, Ed Onslow, Kate Sippy, Tvesha Bright, Jonathan Gabasova, Evelina |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompto: An open source library for asynchronous querying of LLM endpoints Chan, Ryan Sze-Yin Nanni, Federico Williams, Angus R. Brown, Edwin Burke-Moore, Liam Chapman, Ed Onslow, Kate Sippy, Tvesha Bright, Jonathan Gabasova, Evelina Computation and Language 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). |
| title | Prompto: An open source library for asynchronous querying of LLM endpoints |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.11847 |