GPT 4 A ll: An Ecosystem of Open Source Compressed Language Models (2023)

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.

## introduction on march 14 2023, openai released gpt-4, a large language model capable of achieving human level performance on a variety of professional and academic benchmarks. despite the popularity of the release, the gpt-4 technical report @xcite contained virtually no details regarding the architecture, hardware, training compute, dataset construction, or training method used to create the model. moreover, users could only access the model through the internet interface at chat.openai.com, which was severely rate limited and unavailable in several locales (e.g. italy) @xcite . additionally, gpt-4 refused to answer a wide * shared senior authorship variety of queries, responding only with the now infamous "as an ai language model, i cannot..." prefix @xcite . these transparency and accessibility concerns spurred several developers to begin creating open source large language model (llm) alternatives. several grassroots efforts focused on fine tuning meta's open code llama model @xcite , whose weights were leaked on bittorrent less than a week prior to the release of gpt-4 @xcite . gpt4all started as one of these variants. in this paper, we tell the story of gpt4all. we comment on the technical details of the original gpt4all model @xcite , as well as the evolution of gpt4all from a single model to an ecosystem of several models. we remark on the impact that the project has had on the open source community, and discuss future directions. 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. ## the future of gpt4all in the future, we will continue to grow gpt4all, supporting it as the de facto solution for llm accessibility. concretely, this means continuing to compress and distribute important open-source language models developed by the community, as well as compressing and distributing increasingly multimodal ai models. furthermore, we will expand the set of hardware devices that gpt4all models run on, so that gpt4all models "just work" on any machine, whether it comes equipped with apple metal silicon, nvidia, amd, or other edgeaccelerated hardware. overall, we envision a world where anyone, anywhere, with any machine, can access and contribute to the cutting edge of ai.