89 lines
4.2 KiB
Markdown
89 lines
4.2 KiB
Markdown
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---
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license: apache-2.0
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language:
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- de
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- instruction-tuned
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- german
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base_model:
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- Boldt/Boldt-1B
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---
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# Boldt-1B-IT-Preview
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<img src="logo.png" width="500">
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**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.
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- [Boldt-DC-350M](https://huggingface.co/Boldt/Boldt-DC-350M)
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- [Boldt-DC-1B](https://huggingface.co/Boldt/Boldt-DC-1B)
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- [Boldt-1B](https://huggingface.co/Boldt/Boldt-1B)
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- **Boldt-1B-IT-Preview** *(this model)*
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> **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.
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## Training data
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Boldt-1B-IT-Preview was fine-tuned on a curated mixture of 95k German instruction-output pairs from four sources:
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- **Aya:** German subset of the [Aya dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset), consisting of approximately 200 human-authored instruction-output pairs.
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- **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.
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- **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).
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- **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.
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- **NER instructions:** 17k NER tasks derived from 2 German NER datasets.
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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.
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## Usage
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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:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "Boldt/Boldt-1B-IT-Preview"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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messages = [
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{"role": "user", "content": "Erkläre mir kurz, wie Quantencomputer funktionieren."}
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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)
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outputs = model.generate(
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input_ids,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
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```
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## Limitations
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- **Language:** This model is optimized for German. Other languages are not supported.
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- **Preview status:** This model is released as a research preview. It may produce factually incorrect or inconsistent outputs. Not optimized for multi-turn dialogue.
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- **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.
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## Citation
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```bibtex
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@misc{boldt,
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title={Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling},
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author={Ansar Aynetdinov and Patrick Haller and Alan Akbik},
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year={2026},
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eprint={2604.28075},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2604.28075},
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}
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```
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