Papers › h2oGPT: Democratizing Large Language Models

h2oGPT: Democratizing Large Language Models

13 Jun 2023arXiv:2306.08161archive 2025-07-28

Arno Candel, Jon McKinney, Philipp Singer, Pascal Pfeiffer, Maximilian Jeblick, Prithvi Prabhu, Jeff Gambera, Mark Landry, Shivam Bansal, Ryan Chesler, Chun Ming Lee, Marcos V. Conde, Pasha Stetsenko, Olivier Grellier, SriSatish Ambati

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.

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Code

h2oai/h2o-llmstudio officialmentioned in paperpytorchApache-2.0 report

h2oai/h2ogpt mentioned in papermentioned on GitHubpytorchApache-2.0 report

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Tasks

Chatbot Fairness NER Question Answering Sentiment Analysis Text Classification Text Generation Text Retrieval Text Summarization Translation

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Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout GPT-4 Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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