Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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...
+
+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}, 
+}
+```