h2oGPT: Democratizing Large Language Models
Authors: name: Arno Candel; name: Jon McKinney; name: Philipp Singer; name: Pascal Pfeiffer; name: Maximilian Jeblick; name: Prithvi Prabhu; name: Jeff Gambera; name: Mark Landry; name: Shivam Bansal; name: Ryan Chesler; name: Chun Ming Lee; name: Marcos V. Conde; name: Pasha Stetsenko; name: Olivier Grellier; name: SriSatish Ambati
Published: 2023-06-13 22:19:53

Abstract: Foundation Large Language Models (LLMs) such as GPT-4 represent a revolution
in AI due to their real-world applications though 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 Large Language Models (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 GPTs. 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.