Paper · arXiv 2407.09276
H2O-Danube3 Technical Report
Pascal Pfeiffer, Philipp Singer, Yauhen Babakhin, Gabor Fodor, Nischay Dhankhar, Sri Satish Ambati
H2O-Danube3, a series of small language models, achieves high performance across various benchmarks and is efficient for local inference on smartphones.
Abstract
We present H2O-Danube3, a series of small language models consisting of H2O-Danube3-4B, trained on 6T tokens and H2O-Danube3-500M, trained on 4T tokens. Our models are pre-trained on high quality Web data consisting of primarily English tokens in three stages with different data mixes before final supervised tuning for chat version. The models exhibit highly competitive metrics across a multitude of academic, chat, and fine-tuning benchmarks. Thanks to its compact architecture, H2O-Danube3 can be efficiently run on a modern smartphone, enabling local inference and rapid processing capabilities even on mobile devices. We make all models openly available under Apache 2.0 license further democratizing LLMs to a wider audience economically.