3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 3 cited
H2O-Danube-1.8B Technical Report
Philipp Singer, Pascal Pfeiffer, Yauhen Babakhin +4
We present H2O-Danube, a series of small 1.8B language models consisting of H2O-Danube-1.8B, trained on 1T tokens, and the incremental improved H2O-Danube2-1.8B trained on an addit…
cs.CL2023
H2O Open Ecosystem for State-of-the-art Large Language Models
Arno Candel, Jon McKinney, Philipp Singer +4
Large Language Models (LLMs) represent a revolution in AI. However, they also pose many significant risks, such as the presence of biased, private, copyrighted or harmful text. For…