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H2O-Danube-1.8B Technical Report

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arxiv 2401.16818 v2 pith:IGKT6SRZ submitted 2024-01-30 cs.CL cs.LG

classification cs.CLcs.LG
keywords modelstrainedh2o-danube-1h2o-danube2-1languagetokensachievesacross
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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.

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  1. Hymba: A Hybrid-head Architecture for Small Language Models

    cs.CL 2024-11 conditional novelty 6.0 of 10

    Hymba-1.5B, a hybrid-head model with parallel attention and Mamba heads plus learnable meta tokens, achieves state-of-the-art accuracy among sub-2B models and outperforms Llama-3.2-3B with an 11.67x smaller cache and ...

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