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--- |
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license: gemma |
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language: |
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- sl |
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- en |
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- hr |
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- sr |
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- bs |
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base_model: |
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- cjvt/GaMS-9B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# Model Card for GaMS-DPO-Translator |
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GaMS-9B-Instruct-DPO-Translator is a fine-tuned version of GaMS-9B-Instruct. Direct Preference Optimization (DPO) was performed on the original model. The learning dataset was synthetially generated by using GaMS-9B-SFT-Translator and EuroLLM-9B-Instruct. |
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## Basic information |
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- **Developed by:** team of researchers at the University of Ljubljana, Faculty for Computer and Information Science. Team members: Dario Vajda, Domen Vreš and Marko Robnik-Šikonja. |
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- **Languages:** Slovene, English (primary), Croatian, Bosnian and Serbian (secondary). The model might also work for other languages supported by Gemma 2, even though it was not continually pretrained on them. |
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- **Base model:** [cjvt/GaMS-9B-Instruct](https://huggingface.co/cjvt/GaMS-9B-Instruct) |
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- **License:** [Gemma](https://ai.google.dev/gemma/terms) |
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## Usage |
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The model can be run through `pipeline` API using the following code: |
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```python |
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from transformers import pipeline |
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model_id = "GaMS-Beta/GaMS-9B-Instruct-DPO-Translator" |
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pline = pipeline( |
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"text-generation", |
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model=model_id, |
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device_map="cuda" # replace with "mps" to run on a Mac device |
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) |
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# Example of response generation |
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message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}] |
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response = pline(message, max_new_tokens=512) |
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print("Translation:", response[0]["generated_text"][-1]["content"]) |
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``` |
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For multi GPU inference set the `device_map` to `auto`: |
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```python |
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from transformers import pipeline |
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model_id = "GaMS-Beta/GaMS-9B-Instruct-DPO-Translator" |
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pline = pipeline( |
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"text-generation", |
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model=model_id, |
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device_map="auto" |
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) |
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# Example of response generation |
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message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}] |
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response = pline(message, max_new_tokens=512) |
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print("Model's response:", response[0]["generated_text"][-1]["content"]) |
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# Example of conversation chain |
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new_message = response[0]["generated_text"] |
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new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"}) |
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response = pline(new_message, max_new_tokens=1024) |
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print("Model's response:", response[0]["generated_text"][-1]["content"]) |
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``` |
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## Data |
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Data for fine-tuning the original model was acquired by translating a large corpora of wikipedia articles, ccnews articles, bookcorpus texts and english conversational datasets by two models(GaMS-9B-SFT-Translator and EuroLLM-9B-Instruct) which were then ranked by some automatic metrics for translation quality and reliability. |
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## Training |
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The model was trained on the [Vega HPC](https://izum.si/vega_slv/) |
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## Evaluation |
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The model was evaluated by our custom script on three types of data. The results are show in the following table. |
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| Model | Overall Comet | ccnews | nemotron | wikipedia | Bad Lang (%) | Short (%) | Bad Markdown (%) | |
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| --- | --- | --- | --- | --- | --- | --- | --- | |
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| gemini-2.5-flash | 0.717982 | 0.702981 | 0.697498 | 0.753924 | 0.35% | 0.42% | 3.70% | |
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| **GaMS-9B-Instruct-DPO-Translator** | **0.714729** | **0.708317** | **0.689316** | **0.746768** | **1.88%** | **1.56%** | **13.22%** | |
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| GaMS-9B-SFT-Translator-DPO | 0.708042 | 0.702903 | 0.679462 | 0.742583 | 0.91% | 0.28% | 18.28% | |
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| GaMS-27B-Instruct | 0.701284 | 0.686480 | 0.680014 | 0.730733 | 27.28% | 5.36% | 62.07% | |
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| GaMS-9B-Instruct | 0.693659 | 0.685006 | 0.673394 | 0.723470 | 13.50% | 4.83% | 33.15% | |
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| EuroLLM-9B-Instruct | 0.689321 | 0.668084 | 0.670723 | 0.729227 | 8.97% | 1.89% | 35.08% | |
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| GaMS-9B-SFT-Translator | 0.682467 | 0.676580 | 0.673650 | 0.699602 | 5.14% | 1.48% | 30.53% | |
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*Note* - the evaluation script and evaluation data can be found in this [github repo](https://github.com/DarioVajda/translation_dpo) under the data_pipeline folder. See the README for more detailed instructions. |
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## Citation |
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If you found this project useful in your work, please cite our paper with the following BibTeX citation: |
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```txt |
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@misc{vajda2025improvingllmsmachinetranslation, |
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title={Improving LLMs for Machine Translation Using Synthetic Preference Data}, |
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author={Dario Vajda and Domen Vreš and Marko Robnik-Šikonja}, |
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year={2025}, |
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eprint={2508.14951}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2508.14951}, |
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} |
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``` |
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