初始化项目，由ModelHub XC社区提供模型
Model: GaMS-Beta/GaMS-9B-SFT-Translator-DPO Source: Original Platform
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ModelHub XC
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README.md
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README.md
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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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- GaMS-Beta/GaMS-9B-SFT-Translator
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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-Beta/GaMS-9B-SFT-Translator-DPO is a fine-tuned version of GaMS-9B-SFT-Translator. 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-SFT-Translator-DPO"
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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-SFT-Translator-DPO"
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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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