DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data
Mimir v1 is an open 1B-parameter reasoning model trained with permissible post-training data. It is relevant for compact or Danish deployments, though the abstract supplies no benchmark scores.
Mimir v1 is a **1B-parameter** language model based on the Hierarchical Reasoning Model architecture and released on Hugging Face. Its training mixture uses **161 datasets**, with permissible post-training data.
The authors evaluate it across **20 benchmarks** covering English, math and code, and Danish. Builders considering compact or Danish-language models now have an openly available candidate whose data sourcing is a stated design constraint.
Mimir v1 is a **1B-parameter** language model based on the Hierarchical Reasoning Model architecture and released on Hugging Face. Its training mixture uses **161 datasets**, with permissible post-training data. The authors evaluate it across **20 benchmarks** covering English, math and code, and Danish. Builders considering compact or Danish-language models now have an openly available candidate whose data sourcing is a stated design constraint. The paper claims gains over HRM-Text 1B and competition with larger Qwen and Gemma models, but the supplied material includes no task-level scores, licenses, latency, or hardware results. Those details need checking before model selection.
This adds an open, compact model candidate whose permissible-data constraint and Danish evaluation broaden selection criteria beyond English benchmark performance and parameter scale. It supports testing whether a 1B hierarchical architecture can cover reasoning, code, and regional-language needs, but the absence of task scores, licensing details, latency, and hardware results prevents comparison with larger open or hosted alternatives.