HackerPapers

arXiv:2401.16818cs.CL

H2O-Danube-1.8B Technical Report

Philipp Singer, Pascal Pfeiffer, Yauhen Babakhin, Maximilian Jeblick, Nischay Dhankhar, Gabor Fodor, Sri Satish Ambati

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

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 additional 2T tokens. Our models exhibit highly competitive metrics across a multitude of benchmarks and, as of the time of this writing, H2O-Danube2-1.8B achieves the top ranking on Open LLM Leaderboard for all models below the 2B parameter range. The models follow core principles of LLama 2 and Mistral, and we leverage and refine various techniques for pre-training large language models. We additionally release chat models trained with supervised fine-tuning followed by direct preference optimization. We make all models openly available under Apache 2.0 license further democratizing LLMs to a wider audience economically.

arXiv comments
Journal reference
DOI
Subjects
cs.CLComputation and Language cs.LGMachine Learning

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Apr 2026 to Apr 2026. Found in 2 HN submissions from Apr 2024 to Apr 2024. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments1 thread

    • On changing the training mix, H20 did that with Danube in 2024: https://arxiv.org/pdf/2401.16818 With those results, I would've already done that in any models I got to train. There's also the principle that the LLM's are often better at what they saw last in their training set.…

      nickpsecurityView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.