---
library_name: transformers
license: other
license_name: mimir-license-v1.0-research-model-license
license_link: https://huggingface.co/danish-foundation-models/DFM-Mimir/blob/main/LICENSE
language:
  - da
  - en
pipeline_tag: text-generation
tags:
  - danish
  - english
  - foundation-model
  - instruction-tuned
---

<div align="center">
  <img src="DFM-logo.png" alt="Danish Foundation Models" width="400"/>
</div>

# DFM Mimir

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, 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, comprising approximately **70.479 billion tokens per epoch**. Mimir 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.


## Evaluation

Mimir is evaluated across Danish, English, and Math & Code benchmarks. The figure below shows average performance by subject area across compared models.

![Average scores by subject area](plots/subject_avg_scores.png)


### English benchmark results (Best scores in **bold**.)

English benchmark results (full datasets). 

| Model | BoolQ (Acc) | Winogrande (Acc) | Hellaswag (Acc) | MMLU (Acc) | ARC-C (Acc) | DROP (F1) | GovRep. (R1) | Avg. |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **~1B models** | | | | | | | | |
| Mimir 1B | **87.8** | **73.5** | 67.3 | 57.5 | 81.6 | **83.1** | 32.0 | 69.0 |
| HRM-Text 1B | 87.5 | 70.4 | 60.4 | 58.7 | 82.2 | 78.1 | 25.4 | 66.1 |
| Qwen 3.5 0.8B | 69.8 | 48.9 | 37.0 | 51.5 | 68.4 | 45.2 | 32.5 | 50.5 |
| Gemma 3 1B | 62.4 | 49.1 | 30.6 | 37.5 | 43.5 | 7.0 | 29.5 | 37.1 |
| OLMo 2 1B | 67.2 | 51.0 | 42.4 | 41.6 | 48.1 | 12.4 | 37.7 | 42.9 |
| **2--3B models** | | | | | | | | |
| Qwen 3.5 2B | 80.8 | 53.4 | 64.6 | 62.8 | 82.7 | 31.3 | 31.5 | 58.2 |
| SmolLM3 3B | 84.3 | 60.3 | 65.1 | 60.2 | 79.5 | 54.0 | **38.1** | 63.1 |
| **4--5B models** | | | | | | | | |
| Qwen 3.5 4B | 87.0 | 70.0 | **83.2** | **75.8** | **92.9** | 48.0 | 27.9 | **69.3** |
| Gemma 4 E2B | 64.1 | 56.7 | 55.6 | 59.3 | 69.8 | 57.3 | 33.6 | 56.6 |
| Gemma 4 E2B (think) | 83.4 | 63.0 | 55.8 | 72.0 | 86.8 | 70.8 | 34.7 | 66.6 |




### Math & Code benchmark results  (Best scores in **bold**.)

| Model | GSM8K (Acc) | MATH (Acc) | HumanEval (Acc) | Avg. |
| :--- | :---: | :---: | :---: | :---: |
| **~1B models** | | | | |
| Mimir 1B | 89.9 | 45.8 | 56.7 | 64.1 |
| HRM-Text 1B | 84.8 | 56.0 | 0.0 | 46.9 |
| Qwen 3.5 0.8B | 49.1 | 36.1 | 30.5 | 38.6 |
| Gemma 3 1B | 49.7 | 37.2 | 42.7 | 43.2 |
| OLMo 2 1B | 59.4 | 18.8 | 15.9 | 31.4 |
| **2--3B models** | | | | |
| Qwen 3.5 2B | 73.7 | 55.7 | 47.6 | 59.0 |
| SmolLM3 3B | 80.0 | 62.2 | 61.6 | 67.9 |
| **4--5B models** | | | | |
| Qwen 3.5 4B | 60.5 | 56.5 | 78.0 | 65.0 |
| Gemma 4 E2B | 88.3 | **64.2** | **73.8** | **75.4** |
| Gemma 4 E2B (think) | **90.3** | 49.1 | 72.0 | 70.5 |



### Danish benchmark results (Best scores in **bold**.)

| Model | Angry Tweets (Acc) | DaLA (F1) | GEC (EM) | PIQA (Acc) | Daisy (EM) | WikiQA (EM) | WMT (chrF) | N.News (chrF) | IFEval (Acc) | Hellaswag-DA (Acc) | Avg. |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **~1B models** | | | | | | | | | | | |
| Mimir 1B | 67.4 | **96.1** | **85.6** | 53.7 | 9.6 | **66.8** | 53.9 | 35.87 | 63.9 | 35.3 | **56.8** |
| HRM-Text 1B | 42.4 | 26.7 | 0.5 | 13.0 | 0.0 | 34.9 | 25.4 | 26.76 | 18.5 | 28.8 | 21.7 |
| Qwen 3.5 0.8B | 53.8 | 51.0 | 0.7 | 56.5 | 0.7 | 41.6 | 37.8 | 35.30 | 39.6 | 25.0 | 34.2 |
| Gemma 3 1B | 54.4 | 41.0 | 3.3 | 72.2 | 1.4 | 42.6 | 45.1 | 35.56 | 47.2 | 24.8 | 36.8 |
| OLMo 2 1B | 33.6 | 48.7 | 0.2 | 75.0 | 0.0 | 8.4 | 30.0 | 33.77 | 32.5 | 26.7 | 28.9 |
| **2--3B models** | | | | | | | | | | | |
| Qwen 3.5 2B | 61.6 | 36.4 | 8.0 | 25.0 | 2.5 | 49.4 | 45.6 | 34.85 | 56.1 | 24.7 | 34.4 |
| SmolLM3 3B | 63.2 | 33.5 | 3.3 | 51.9 | 2.2 | 0.3 | 37.3 | 35.98 | 49.8 | 40.1 | 31.7 |
| **4--5B models** | | | | | | | | | | | |
| Qwen 3.5 4B | 69.1 | 50.1 | 42.6 | 70.4 | 4.7 | 57.1 | 52.1 | **37.03** | 73.7 | 34.7 | 49.2 |
| Gemma 4 E2B | 64.6 | 56.7 | 36.9 | 46.3 | 5.6 | 44.1 | 55.2 | 35.67 | 75.5 | 25.6 | 44.6 |
| Gemma 4 E2B (think) | 67.7 | 66.8 | 23.4 | 63.9 | 5.1 | 59.3 | 56.0 | 36.30 | **81.2** | **39.0** | 49.9 |
| **8--9B models** | | | | | | | | | | | |
| Munin-Apertus 8B | 60.6 | 46.1 | 42.1 | 81.5 | **12.5** | 49.9 | 55.8 | 30.30 | 53.0 | 24.5 | 45.6 |
| Munin-Mistral 8B | 61.3 | 48.8 | 26.4 | 76.9 | 8.4 | 48.4 | 51.8 | 32.92 | 67.8 | 33.6 | 45.6 |
| Munin-Qwen 9B | **69.1** | 60.6 | 11.4 | 38.9 | 5.4 | 55.7 | **56.1** | 35.89 | 71.8 | 34.3 | 43.9 |





## Model details

| Architecture | Parameters | Hidden size | Layers | Attention heads | Vocab size | Context length | Training steps | Tokens per epoch | License |
|---|---|---:|---:|---:|---:|---:|---:|---|---|
| HRM-Text | ~1B | 1,536 | 16 | 12 | 262,144 | 4,096 | 1,750,000 | ~70.5B | MIMIR License v1.0 |


## Technical Report
Training was performed using a fork of [HRM-Text](https://github.com/schneiderkamplab/HRM-Text). Further details are provided in our technical report [here](https://arxiv.org/pdf/2608.13517)

## Limitations

Mimir v1 was trained on Danish and English data only. It will likely have poor performance on other languages. The model has not been specifically aligned for safety and may reflect social biases present in its training data.

## License

This model is released under the **MIMIR License v1.0 - Research Model License**.

See the full license text in [LICENSE](https://huggingface.co/danish-foundation-models/DFM-Mimir/blob/main/LICENSE).

## Project partners & funding

The development of Mimir v1 was performed in close collaboration between [University of Southern Denmark](https://www.sdu.dk/en/forskning/machine-learning), [Aarhus University](https://chc.au.dk/), [University of Copenhagen](https://www.ku.dk/en) and the [Alexandra Institute](https://alexandra.dk/), as part of [Danish Foundation Models](https://foundationmodels.dk/).

Funding was provided by the [Ministry of Science, Higher Education and Digital Affairs](https://ufm.dk/en).

## How to cite

```bibtex
@misc{mimir-v1,
  title  = {DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data},
  author = {Schneider-Kamp, Peter and Nielsen, Jacob and Barmina, Gicanluca and Enevoldsen, Kenneth and Poech, Lukas Galke},
  year   = {2026},
  url    = {https://huggingface.co/danish-foundation-models/HRM-Mimir-v1}
}
```