DFM unveils Mimir v1, an open HRM achieving frontier performance at 1 billion parameters using only permissible post‑training data
open-source reasoning training
| Source: HF Papers | Original article
Mimir v1, a new open‑source 1‑billion‑parameter language model built on the Hierarchical Reasoning Model, achieves frontier performance using only permissible post‑training data.
The University of Southern Denmark has unveiled **DFM Mimir v1**, a 1‑billion‑parameter language model built on the Hierarchical Reasoning Model (HRM) architecture. Trained from scratch using only permissible post‑training data, Mimir v1 delivers performance that rivals commercial systems that are four times larger, according to the team’s internal benchmarks. It matches leading models on twenty reasoning tasks in English and sets a new state‑of‑the‑art result for Danish.
The release tackles a growing concern in the AI community: most large‑scale models rely on massive datasets that include copyrighted or otherwise non‑permissible material, creating legal and ethical hurdles for open‑source developers. By restricting its training corpus to data that can be freely used, Mimir v1 demonstrates that high‑quality reasoning capabilities do not require indiscriminate data harvesting. The achievement also lowers the entry barrier for researchers and smaller organisations that lack the resources to acquire or curate huge proprietary corpora.
Mimir v1’s success is likely to spark interest in further HRM‑based models and in expanding the approach to other Nordic languages. Observers will watch for follow‑up releases that scale the architecture, for community‑driven fine‑tuning efforts, and for independent evaluations on broader benchmark suites. If the model’s open‑source licence and data‑compliance stance gain traction, it could reshape how academic and industry teams build and share language technologies across Europe.
Sources
Back to AIPULSEN