h2oGPT: Democratizing Large Language Models
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Abstract (arXiv)
Applications built on top of Large Language Models (LLMs) such as GPT-4 represent a revolution in AI due to their human-level capabilities in natural language processing. However, they also pose many significant risks such as the presence of biased, private, or harmful text, and the unauthorized inclusion of copyrighted material. We introduce h2oGPT, a suite of open-source code repositories for the creation and use of LLMs based on Generative Pretrained Transformers (GPTs). The goal of this project is to create the world's best truly open-source alternative to closed-source approaches. In collaboration with and as part of the incredible and unstoppable open-source community, we open-source several fine-tuned h2oGPT models from 7 to 40 Billion parameters, ready for commercial use under fully permissive Apache 2.0 licenses. Included in our release is 100% private document search using natural language. Open-source language models help boost AI development and make it more accessible and trustworthy. They lower entry hurdles, allowing people and groups to tailor these models to their needs. This openness increases innovation, transparency, and fairness. An open-source strategy is needed to share AI benefits fairly, and H2O.ai will continue to democratize AI and LLMs.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 0 of its 4 references that are in the map. First posted on arXiv 13 Jun 2023.
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Citing papers per year: 2023: 2; 2024: 3.
5 papers.
- 2023Fine-tuning ChatGPT for Automatic Scoringcited by 50
- 2023Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagationcited by 12
- 2024Towards Optimal Learning of Language Modelscited by 7
- 2024Evaluating the Performance of LLMs on Technical Language Processing taskscited by 2
- 2024CURATRON: Complete and Robust Preference Data for Rigorous Alignment of Large Language Modelscited by 0
- 2024Evaluating the Performance of LLMs on Technical Language Processing tasks21 Mar 2024
- 2024CURATRON: Complete and Robust Preference Data for Rigorous Alignment of Large Language Models5 Mar 2024
- 2024Towards Optimal Learning of Language Models27 Feb 2024
- 2023Fine-tuning ChatGPT for Automatic Scoring16 Oct 2023
- 2023Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation22 Sep 2023