57 lines
2.5 KiB
Markdown
57 lines
2.5 KiB
Markdown
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---
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language:
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- de
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license: apache-2.0
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library_name: transformers
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base_model:
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- allenai/Olmo-3-1025-7B
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tags:
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- multilingual
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- synthetic
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- sft
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datasets:
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- ljvmiranda921/PolyglotTeachers-SFT-Synth
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---
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<img alt="Logo for LTL" src="ltl_logo2.svg" width="240px" style="margin-left:'auto' margin-right:'auto' display:'block'">
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# Polyglot-OLMo3-7B-SFT-de
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This model is a fine-tuned version of [allenai/OLMo-3-1025-7B](https://huggingface.co/allenai/OLMo-3-1025-7B) on German synthetic data, using the best teacher-student combination identified in our paper [Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation]().
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The training data was generated by [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) and is available in the [PolyglotTeachers-SFT-Synth](https://huggingface.co/datasets/ljvmiranda921/PolyglotTeachers-SFT-Synth) dataset.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ljvmiranda921/Polyglot-OLMo3-7B-SFT-de"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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messages = [{"role": "user", "content": "Hallo, wie geht es dir?"}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Acknowledgements
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LJVM and AK acknowledge the support of the UKRI Frontier Grant EP/Y031350/1 ([EQUATE](https://gtr.ukri.org/projects?ref=EP%2FY031350%2F1)).
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This work was performed using joint resources provided by the [Cambridge Service for Data Driven Discovery (CSD3)](https://hpc.cam.ac.uk/high-performance-computing) EP/T022159/1 and the [Isambard AI National AI Research Resource (AIRR)](https://www.bristol.ac.uk/research/centres/bristol-supercomputing/#isambard-ai) ST/AIRR/I-A-I/1023, and the Microsoft Research Grant.
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LJVM would also like to thank Songbo Hu, Chen Cecilia Liu, Millicent Ochieng, and Felermino Ali for helpful and productive discussions on the project.
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## Citation
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```bibtex
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@misc{miranda2026polyglotteachersevaluatinglanguage,
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title={Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation},
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author={Lester James V. Miranda and Ivan Vulić and Anna Korhonen},
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year={2026},
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eprint={2604.11290},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2604.11290},
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}
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```
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