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Boldt
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Boldt
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Note: This is a base language model, not an instruction-tuned model. It is not optimized for chat or instruction following. For best results, use standard text completion rather than chat templates.
python
from transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = "Boldt/Boldt-DC-350M"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name) # Basic text completiontext = "Berlin ist eine Stadt, wo"inputs = tokenizer(text, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=64)We evaluate Boldt-350M on our modernized German benchmark suite. See our paper (Aynetdinov et al., 2026) for details on the structural and translation corrections we performed.
Boldt-350M while significantly smaller than the 1B models we compare with, still fares comparatively well, outperforming the much larger, multilingual Gemma-3-1B and Llama-3.2-1B models.
Note: Bold text indicates the best score in the 1B category.
| Model | Tokens | MMLU | ARC-C | ARC-E | H-Swag | LAMBADA | OBQA | Avg. |
|---|---|---|---|---|---|---|---|---|
| Boldt-DC-300M (this model) | 200B | 29.29 | 32.24 | 52.87 | 43.21 | 37.48 | 45.86 | 40.16 |
| Boldt-DC-1B | 200B | 31.06 | 35.99 | 57.30 | 48.69 | 42.80 | 48.48 | 44.05 |
| Boldt-1B | 230B | 31.42 | 34.11 | 55.78 | 48.77 | 44.70 | 52.32 | 44.52 |
| LLäMmlein-1B | 1T | 29.26 | 30.27 | 48.19 | 44.80 | 44.89 | 47.27 | 40.78 |
| Gemma-3-1B | 2T* | 30.01 | 30.55 | 47.89 | 43.43 | 41.71 | 45.05 | 39.77 |
| Llama-3.2-1B | 9T* | 28.58 | 29.90 | 40.51 | 40.07 | 44.31 | 44.04 | 37.90 |
| Qwen3.5-0.8B-Base | >36T* | 30.79 | 32.05 | 46.20 | 38.90 | 36.02 | 43.84 | 37.97 |
We have not conducted systematic model evaluations of toxicity, demographic biases, or harmful stereotypes. Quality filtering may reduce some risks relative to unfiltered web data, but cannot guarantee their absence, and repeated exposure during multi-epoch training could amplify rather than mitigate encoded biases. Users should exercise caution in sensitive use-cases without further evaluation.
bibtex
@misc{boldt2026, title={Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling}, author={Ansar Aynetdinov and Patrick Haller and Alan Akbik}, year={2026}, eprint={2604.28075}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2604.28075}, }from transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = "Boldt/Boldt-DC-350M"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name) # Basic text completiontext = "Berlin ist eine Stadt, wo"inputs = tokenizer(text, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=64)@misc{boldt2026, title={Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling}, author={Ansar Aynetdinov and Patrick Haller and Alan Akbik}, year={2026}, eprint={2604.28075}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2604.28075}, }