Create README.md

README.md

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+---
+license: gemma
+language:
+- sl
+- en
+- hr
+- sr
+- bs
+base_model:
+- cjvt/GaMS-9B-Instruct
+pipeline_tag: text-generation
+---
+
+# Model Card for GaMS-DPO-Translator
+
+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. 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/652d40a78fa1fbb0aae165bb/94gX0PG8zRB_Zg31K2y_i.png)
+
+## Basic information
+
+- **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.
+- **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.
+- **Base model:** [cjvt/GaMS-9B-Instruct](https://huggingface.co/cjvt/GaMS-9B-Instruct)
+- **License:** [Gemma](https://ai.google.dev/gemma/terms)
+
+## Usage
+
+The model can be run through `pipeline` API using the following code:
+
+```python
+from transformers import pipeline
+
+model_id = "GaMS-Beta/GaMS-9B-Instruct-DPO-Translator"
+
+pline = pipeline(
+    "text-generation",
+    model=model_id,
+    device_map="cuda" # replace with "mps" to run on a Mac device
+)
+
+# Example of response generation
+message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}]
+response = pline(message, max_new_tokens=512)
+print("Translation:", response[0]["generated_text"][-1]["content"])
+```
+
+For multi GPU inference set the `device_map` to `auto`:
+
+```python
+from transformers import pipeline
+
+model_id = "GaMS-Beta/GaMS-9B-Instruct-DPO-Translator"
+
+pline = pipeline(
+    "text-generation",
+    model=model_id,
+    device_map="auto"
+)
+
+# Example of response generation
+message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}]
+response = pline(message, max_new_tokens=512)
+print("Model's response:", response[0]["generated_text"][-1]["content"])
+
+# Example of conversation chain
+new_message = response[0]["generated_text"]
+new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"})
+response = pline(new_message, max_new_tokens=1024)
+print("Model's response:", response[0]["generated_text"][-1]["content"])
+```
+
+## Data
+
+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.
+
+## Training
+
+The model was trained on the [Vega HPC](https://izum.si/vega_slv/)
+
+## Evaluation
+
+The model was evaluated by our custom script on three types of data. The results are show in the following table.
+
+| Model | Overall Comet | ccnews | nemotron | wikipedia | Bad Lang (%) | Short (%) | Bad Markdown (%) |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| gemini-2.5-flash | **0.717982** | 0.702981 | **0.697498** | **0.753924** | **0.35%** | 0.42% | **3.70%** |
+| GaMS-9B-Instruct-DPO-Translator | 0.714729 | **0.708317** | 0.689316 | 0.746768 | 1.88% | 1.56% | 13.22% |
+| GaMS-9B-SFT-Translator-DPO | 0.708042 | 0.702903 | 0.679462 | 0.742583 | 0.91% | **0.28%** | 18.28% |
+| GaMS-27B-Instruct | 0.701284 | 0.686480 | 0.680014 | 0.730733 | 27.28% | 5.36% | 62.07% |
+| GaMS-9B-Instruct | 0.693659 | 0.685006 | 0.673394 | 0.723470 | 13.50% | 4.83% | 33.15% |
+| EuroLLM-9B-Instruct | 0.689321 | 0.668084 | 0.670723 | 0.729227 | 8.97% | 1.89% | 35.08% |
+| GaMS-9B-SFT-Translator | 0.682467 | 0.676580 | 0.673650 | 0.699602 | 5.14% | 1.48% | 30.53% |