GPT4All: An Ecosystem of Open Source Compressed Language Models
Yuvanesh Anand, Zach Nussbaum, Adam Treat, Aaron Miller, Richard Guo, Benjamin Schmidt, Brandon Duderstadt, Andriy Mulyar
Abstract
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks.The accessibility of these models has lagged behind their performance.State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports.In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs.We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem.It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.- Anthology ID:
- 2023.nlposs-1.7
- Volume:
- Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Liling Tan, Dmitrijs Milajevs, Geeticka Chauhan, Jeremy Gwinnup, Elijah Rippeth
- Venues:
- NLPOSS | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 59–64
- Language:
- URL:
- https://aclanthology.org/2023.nlposs-1.7
- DOI:
- 10.18653/v1/2023.nlposs-1.7
- Cite (ACL):
- Yuvanesh Anand, Zach Nussbaum, Adam Treat, Aaron Miller, Richard Guo, Benjamin Schmidt, Brandon Duderstadt, and Andriy Mulyar. 2023. GPT4All: An Ecosystem of Open Source Compressed Language Models. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 59–64, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- GPT4All: An Ecosystem of Open Source Compressed Language Models (Anand et al., NLPOSS-WS 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.nlposs-1.7.pdf