KUALA LUMPUR, April 12 (Bernama) -- H2O.ai announced its latest open-weights (Apache v2.0) small language model, H2O-Danube2-1.8B, has secured the top position on the Hugging Face Open large language model (LLM) Leaderboard for below two billion (2B) range, even surpassing the much larger Gemma-2B model from Google in the 2.5B parameter category.
This achievement underscores H2O.ai's commitment to advancing artificial intelligence (AI) accessibility and performance through innovative open-source solutions, according to a statement.
Its Chief Executive Officer and Founder, Sri Ambati said this model not only outperformed leading competitors like Microsoft Phi-2 and Google Gemma 2, but also provides economic efficiency and ease of deployment for enterprise and edge computing applications.
“We love this category – a great size to fine tune or post-train on domain specific datasets for our enterprise customers, economically efficient on inference and training, and very easily embedded on edge devices like mobile phones, drones and in offline applications.
“The applications of this model are far reaching, from detecting and preventing PII data leakage to improving prompt generation and enhancing guardrails and the robustness of RAG systems,” Ambati said.
H2O-Danube2-1.8B is built upon the success of its predecessor, H2O-Danube 1.8B, with notable upgrades and optimisations that have propelled it to the forefront of the 2B SLM category.
Leveraging a vast dataset of two trillion high-quality tokens, this model builds upon the Mistral architecture and optimizations, such as dropping windowing attention, to deliver unparalleled performance in natural language processing tasks.
The open source leader in Generative AI (GenAI) and machine learning continues to drive innovation in AI research and development, empowering organisations to leverage cutting-edge technology without the constraints of traditional resource-intensive approaches.
Founded in 2012, H2O.ai aims to bring together the world’s top data scientists with customers to co-create GenAI applications that are usable and valuable by everyone.
-- BERNAMA
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