Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References 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. 1 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 (OpenAI, 2023) 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) (BBC News, 2023). 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 (Vincent, 2023). 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 (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). 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 (Anand et al., 2023), 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. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References 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. 1 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 (OpenAI, 2023) 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) (BBC News, 2023). 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 (Vincent, 2023). 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 (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). 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 (Anand et al., 2023), 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. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

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