DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data

Abstract:Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture of 161 datasets, Mimir v1 outperforms the original HRM-Text 1B and competes with larger frontier models like Qwen 3.5 4B and Gemma 4 E2B, tested across 20 benchmarks for English, Math & Code and Danish. The model is available on the Hugging Face Hub: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.13517 [cs.CL]

(or arXiv:2608.13517v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.13517 Focus to learn more arXiv-issued DOI via DataCite

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