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 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. 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 2 The Original GPT4All Model 2.1 Data Collection and Curation To train the original GPT4All model, we collected roughly one million prompt-response pairs using the GPT-3.5-Turbo OpenAI API between March 20, 2023 and March 26th, 2023. In particular, we gathered GPT3.5-Turbo responses to prompts of three publicly available datasets: the unified chip2 subset of LAION OIG, a random sub-sample of Stackoverflow Questions, and a sub-sample of Bigscience/P3 (Sanh et al., 2021). Following the approach in Stanford Alpaca (Taori et al., 2023), an open source LLaMA variant that came just before GPT4All, we focused substantial effort on dataset curation. The collected dataset was loaded into Atlas (AI, 2023)—a visual interface for exploring and tagging massive unstructured datasets —for data curation. Using AtarXiv:2311.04931v1 [cs.CL] 6 Nov 2023 las, we identified and removed subsets of the data where GPT-3.5-Turbo refused to respond, had malformed output, or produced a very short response. This resulted in the removal of the entire Bigscience/P3 subset of our data, as many P3 prompts induced responses that were simply one word. After curation, we were left with a set of 437,605 prompt-response pairs, which we visualize in Figure 1a. 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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Original GPT4All Model: How We Collected Data and Then Curated It

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