Boldt-1B-IT-Preview

Boldt-1B-IT-Preview is a preview of an instruction-tuned German language model, fine-tuned on top of Boldt-1B. It is part of the Boldt series of German Small Language Models (SLMs) trained from scratch at Humboldt-Universität zu Berlin.

• Boldt-DC-350M
• Boldt-DC-1B
• Boldt-1B
• Boldt-1B-IT-Preview (this model)

Preview status. This is an early release intended to demonstrate instruction-following capabilities emerging from our quality-focused pre-training recipe. It has not undergone systematic safety evaluation and should not be used in production settings without further assessment.

Training data

Boldt-1B-IT-Preview was fine-tuned on a curated mixture of 95k German instruction-output pairs from four sources:

• Aya: German subset of the Aya dataset, consisting of approximately 200 human-authored instruction-output pairs.
• SmolTalk2 (DE, improved): an improved German subset of the SmolTalk2 dataset. We adjusted 52k prompts for more natural flowing German and regenerated outputs using Qwen-3.6-27B to improve their quality.
• r/FragReddit: 7k prompts sourced from the r/FragReddit subreddit. Outputs were generated using Qwen-3.6-27B.
• Synthetic Reddit: 19k synthetic QA pairs derived from a dump of r/FragReddit posts. We used Qwen-3.6-27B to filter useful posts, rephrase questions for clarity, and generate helpful and educational answers.
• NER instructions: 17k NER tasks derived from 2 German NER datasets.

The mixture is designed to combine broad topical coverage with naturalness of German expression, complementing the information-dense pre-training corpus underlying the base model.

Usage

Boldt-1B-IT-Preview is designed for single-turn German-language instruction-following tasks. It was not fine-tuned for multi-turn conversations, and performance in multi-turn settings is not guaranteed. It uses a standard chat template and can be used as follows:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Boldt/Boldt-1B-IT-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Erkläre mir kurz, wie Quantencomputer funktionieren."}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

Limitations

• Language: This model is optimized for German. Other languages are not supported.
• Preview status: This model is released as a research preview. It may produce factually incorrect or inconsistent outputs. Not optimized for multi-turn dialogue.
• Safety: We have not conducted systematic evaluations for toxic content, demographic biases, or harmful stereotypes. Quality filtering during pre-training may reduce some risks relative to unfiltered corpora but cannot eliminate them. Repeated multi-epoch exposure may amplify encoded biases. Users should exercise caution in sensitive applications.

Citation

@misc{boldt,
      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}, 
}