README.md

---
license: apache-2.0
language:
- de
pipeline_tag: text-generation
library_name: transformers
tags:
- instruction-tuned
- german
base_model:
- Boldt/Boldt-1B
---

# Boldt-1B-IT-Preview

<img src="logo.png" width="500">

**Boldt-1B-IT-Preview** is a preview of an instruction-tuned German language model, fine-tuned on top of [Boldt-1B](https://huggingface.co/Boldt/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](https://huggingface.co/Boldt/Boldt-DC-350M)
- [Boldt-DC-1B](https://huggingface.co/Boldt/Boldt-DC-1B)
- [Boldt-1B](https://huggingface.co/Boldt/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](https://huggingface.co/datasets/CohereForAI/aya_dataset), consisting of approximately 200 human-authored instruction-output pairs.
- **SmolTalk2 (DE, improved):** an improved German subset of the [SmolTalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2) dataset. We adjusted 52k prompts for more natural flowing German and regenerated outputs using [Qwen-3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) to improve their quality.
- **r/FragReddit:** 7k prompts sourced from the [r/FragReddit](https://www.reddit.com/r/FragReddit/) subreddit. Outputs were generated using [Qwen-3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B).
- **Synthetic Reddit:** 19k synthetic QA pairs derived from a dump of r/FragReddit posts. We used [Qwen-3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) 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:

```python
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

```bibtex
@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}, 
}
```