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H2O-Danube3 Technical Report

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arxiv 2407.09276 v1 pith:XWKX2UTG submitted 2024-07-12 cs.CL cs.LG

classification cs.CLcs.LG
keywords modelsh2o-danube3tokenschatconsistingdatatrainedacademic
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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.

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Cited by 2 Pith papers

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  1. Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed

    cs.CL 2026-08 conditional novelty 5.0 of 10

    Quantizing a larger pretrained model preserves trustworthiness better than pruning, and it produces small models that score higher on trustworthiness than small models trained from scratch.

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    cs.CL 2025-02 conditional novelty 5.0 of 10

    Uncertainty-based routing thresholds for small-to-large LLM offloading can be bootstrapped from a calibration set built on other datasets, because confidence distributions depend more on the small model and uncertaint...

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