| title | DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data |
|---|---|
| authors | Peter Schneider-Kamp, Jacob Nielsen, Gianluca Barmina, Kenneth Enevoldsen, Lukas Galke Poech |
| date | 2026-08-13 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.13517 |
| affiliation | University of Southern Denmark (SDU) |
| upvotes | 29 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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: https://huggingface.co/danish-foundation-models/DFM-Mimir |
cite
@misc{schneiderkamp2026dfm,
title={DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data},
author={Peter Schneider-Kamp and Jacob Nielsen and Gianluca Barmina and Kenneth Enevoldsen and Lukas Galke Poech},
year={2026},
eprint={2608.13517},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.13517}
}