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@@ -1,656 +1,691 @@
----
-license: cc-by-nc-4.0
-language:
-- ro
-base_model:
-- OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09
-datasets:
-- OpenLLM-Ro/ro_dpo_helpsteer
-model-index:
-    - name: OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09
-      results:
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: Score
-              type: Score
-              value: 6.77
-        - task:
-            type: text-generation
-          dataset:
-            name: RoCulturaBench
-            type: RoCulturaBench
-          metrics:
-            - name: Score
-              type: Score
-              value: 4.83
-        - task:
-            type: text-generation
-          dataset:
-            name: Romanian_Academic_Benchmarks
-            type: Romanian_Academic_Benchmarks
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 59.08
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 54.10
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 63.41
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 70.02
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 59.35
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 57.24
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_truthfulqa
-            type: OpenLLM-Ro/ro_truthfulqa
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 50.39
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 97.74
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 67.40
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 27.32
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 15.96
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average exact_match
-              type: exact_match
-              value: 32.42
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average f1
-              type: f1
-              value: 58.68
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average spearman
-              type: spearman
-              value: 80.82
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average pearson
-              type: pearson
-              value: 81.50
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: First turn
-              type: Score
-              value: 7.24
-            - name: Second turn
-              type: Score
-              value: 6.30
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 51.59
-            - name: 1-shot 
-              type: accuracy
-              value: 50.99
-            - name: 3-shot 
-              type: accuracy
-              value: 53.47
-            - name: 5-shot 
-              type: accuracy
-              value: 54.84
-            - name: 10-shot 
-              type: accuracy
-              value: 58.10
-            - name: 25-shot 
-              type: accuracy
-              value: 55.61
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 62.15
-            - name: 1-shot 
-              type: accuracy
-              value: 62.78
-            - name: 3-shot 
-              type: accuracy
-              value: 64.27
-            - name: 5-shot 
-              type: accuracy
-              value: 64.43
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 66.69
-            - name: 1-shot 
-              type: accuracy
-              value: 68.82
-            - name: 3-shot 
-              type: accuracy
-              value: 71.82
-            - name: 5-shot 
-              type: accuracy
-              value: 72.77
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 56.98
-            - name: 1-shot 
-              type: accuracy
-              value: 57.73
-            - name: 3-shot 
-              type: accuracy
-              value: 59.29
-            - name: 5-shot 
-              type: accuracy
-              value: 60.70
-            - name: 10-shot 
-              type: accuracy
-              value: 62.03
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: 1-shot 
-              type: accuracy
-              value: 46.78
-            - name: 3-shot 
-              type: accuracy
-              value: 59.97
-            - name: 5-shot 
-              type: accuracy
-              value: 64.97
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 97.30
-            - name: 1-shot 
-              type: macro-f1
-              value: 97.50
-            - name: 3-shot 
-              type: macro-f1
-              value: 97.83
-            - name: 5-shot 
-              type: macro-f1
-              value: 98.33
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 59.30
-            - name: 1-shot 
-              type: macro-f1
-              value: 65.52
-            - name: 3-shot 
-              type: macro-f1
-              value: 70.94
-            - name: 5-shot 
-              type: macro-f1
-              value: 73.85
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 17.49
-            - name: 1-shot 
-              type: bleu
-              value: 30.33
-            - name: 3-shot 
-              type: bleu
-              value: 30.58
-            - name: 5-shot 
-              type: bleu
-              value: 30.88
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 2.17
-            - name: 1-shot 
-              type: bleu
-              value: 10.69
-            - name: 3-shot 
-              type: bleu
-              value: 21.68
-            - name: 5-shot 
-              type: bleu
-              value: 29.28
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_EM
-            type: XQuAD_EM
-          metrics:
-            - name: 0-shot 
-              type: exact_match
-              value: 23.28
-            - name: 1-shot 
-              type: exact_match
-              value: 33.45
-            - name: 3-shot 
-              type: exact_match
-              value: 34.37
-            - name: 5-shot 
-              type: exact_match
-              value: 38.57
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_F1
-            type: XQuAD_F1
-          metrics:
-            - name: 0-shot 
-              type: f1
-              value: 47.16
-            - name: 1-shot 
-              type: f1
-              value: 60.28
-            - name: 3-shot 
-              type: f1
-              value: 62.09
-            - name: 5-shot 
-              type: f1
-              value: 65.20
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Spearman
-            type: STS_Spearman
-          metrics:
-            - name: 1-shot 
-              type: spearman
-              value: 75.34
-            - name: 3-shot 
-              type: spearman
-              value: 82.71
-            - name: 5-shot 
-              type: spearman
-              value: 84.41
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Pearson
-            type: STS_Pearson
-          metrics:
-            - name: 1-shot 
-              type: pearson
-              value: 77.97
-            - name: 3-shot 
-              type: pearson
-              value: 82.49
-            - name: 5-shot 
-              type: pearson
-              value: 84.05
-
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This model points/is identical to [RoGemma2-9b-Instruct-DPO-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09).
-
-
-RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 9B model**. Links to other models can be found at the bottom of this page.
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
-
-
-- **Developed by:** OpenLLM-Ro
-<!-- - **Funded by [optional]:** [More Information Needed] -->
-<!-- - **Shared by [optional]:** [More Information Needed] -->
-<!-- - **Model type:** [More Information Needed] -->
-- **Language(s):** Romanian
-- **License:** cc-by-nc-4.0
-- **Finetuned from model:** [RoGemma2-9b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09)
-- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer)
-
-
-### Model Sources
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
-- **Paper:** https://arxiv.org/abs/2406.18266
-
-## Intended Use
-
-### Intended Use Cases
-
-RoGemma2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
-
-
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO")
-model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO")
-
-instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
-chat = [
-        {"role": "user", "content": instruction},
-        ]
-prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
-
-inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
-outputs = model.generate(input_ids=inputs, max_new_tokens=128)
-print(tokenizer.decode(outputs[0]))
-```
-
-## Academic Benchmarks
-
-<table>
-<tbody>
-<tr>
-<td><strong>Model</strong></td>
-<td><strong><center>Average</center></strong></td>
-<td><strong><center>ARC</center></strong></td>
-<td><strong><center>MMLU</center></strong></td>
-<td><strong><center>Winogrande</center></strong></td>
-<td><strong><center>Hellaswag</center></strong></td>
-<td><strong><center>GSM8k</center></strong></td>
-<td><strong><center>TruthfulQA</center></strong></td>
-</tr>
-<tr>
-<td>gemma-2-9b-it</td><td><center>56.22</center></td><td><center>50.33</center></td><td><center><strong>64.01</strong></center></td><td><center>64.88</center></td><td><center><strong>63.11</strong></center></td><td><center>41.95</center></td><td><center>53.03</center></td>
-</tr>
-<tr>
-<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>57.06</center></td><td><center><strong>56.20</strong></center></td><td><center>62.98</center></td><td><center><strong>71.00</strong></center></td><td><center>60.52</center></td><td><center>37.86</center></td><td><center><strong>53.77</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma2-9b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>59.08</strong></em></center></td><td><center><em>54.10</em></center></td><td><center><em>63.41</em></center></td><td><center><em>70.02</em></center></td><td><center><em>59.35</em></center></td><td><center><em><strong>57.24</strong></em></center></td><td><center><em>50.39</em></center></td>
-</tr>
-</tbody>
-</table>
-
-
-## Downstream tasks
-
-<table>
-<tbody>
-<tr>
-<td></td>
-<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
-<td colspan="4"><center><strong>WMT</strong></center></td>
-</tr>
-<tr>
-<td></td>
-<td colspan="2"><center><strong>Few-shot</strong></center></td>
-<td colspan="2"><center><strong>Finetuned</strong></center></td>
-<td colspan="2"><center><strong>Few-shot</strong></center></td>
-<td colspan="2"><center><strong>Finetuned</strong></center></td>
-</tr>
-<tr>
-<td><strong>Model</strong></td>
-<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
-<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
-<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
-<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
-<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
-<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
-<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
-<td><center><strong>RO-EN<br>(Bleu)</strong></center>
-</tr>
-<tr>
-<td>gemma-2-9b-it</td><td><center>90.82</center></td><td><center>52.51</center></td><td><center><strong>98.97</strong></center></td><td><center>86.02</center></td><td><center>19.97</center></td><td><center><strong>28.94</strong></center></td><td><center>27.94</center></td><td><center><strong>41.61</strong></center></td>
-</tr>
-<tr>
-<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>96.19</center></td><td><center>62.49</center></td><td><center>98.93</center></td><td><center><strong>88.33</strong></center></td><td><center>25.74</center></td><td><center>23.16</center></td><td><center><strong>28.43</strong></center></td><td><center>40.94</center></td>
-</tr>
-<tr>
-<td><em>RoGemma2-9b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>97.74</strong></em></center></td><td><center><em><strong>67.40</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>27.32</strong></em></center></td><td><center><em>15.96</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
-</tr>
-</tbody>
-</table>
-
-
-<table>
-<tbody>
-<tr>
-<td></td>
-<td colspan="4"><center><strong>XQuAD</strong></center></td>
-<td colspan="4"><center><strong>STS</strong></center></td>
-</tr>
-<tr>
-<td></td>
-<td colspan="2"><center><strong>Few-shot</strong></center></td>
-<td colspan="2"><center><strong>Finetuned</strong></center></td>
-<td colspan="2"><center><strong>Few-shot</strong></center></td>
-<td colspan="2"><center><strong>Finetuned</strong></center></td>
-</tr>
-<tr>
-<td><strong>Model</strong></td>
-<td><center><strong>(EM)</strong></center></td>
-<td><center><strong>(F1)</strong></center></td>
-<td><center><strong>(EM)</strong></center></td>
-<td><center><strong>(F1)</strong></center></td>
-<td><center><strong>(Spearman)</strong></center></td>
-<td><center><strong>(Pearson)</strong></center></td>
-<td><center><strong>(Spearman)</strong></center></td>
-<td><center><strong>(Pearson)</strong></center></td>
-</tr>
-<tr>
-<td>gemma-2-9b-it</td><td><center>37.56</center></td><td><center>57.48</center></td><td><center><strong>71.09</strong></center></td><td><center><strong>84.78</strong></center></td><td><center>71.39</center></td><td><center>71.73</center></td><td><center>89.07</center></td><td><center>89.29</center></td>
-</tr>
-<tr>
-<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center><strong>51.37</strong></center></td><td><center><strong>70.74</strong></center></td><td><center>50.00</center></td><td><center>64.10</center></td><td><center>77.15</center></td><td><center>77.10</center></td><td><center><strong>89.45</strong></center></td><td><center><strong>89.89</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma2-9b-Instruct-DPO-2024-10-09</em></td><td><center><em>32.42</em></center></td><td><center><em>58.68</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>80.82</strong></em></center></td><td><center><em><strong>81.50</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
-</tr>
-</tbody>
-</table>
-
-## MT-Bench
-
-<table>
-<tbody>
-<tr>
-<td><strong>Model</strong></td>
-<td><strong><center>Average</center></strong></td>
-<td><strong><center>1st turn</center></strong></td>
-<td><strong><center>2nd turn</center></strong></td>
-<td><strong><center>Answers in Ro</center></strong></td>
-</tr>
-<tr>
-<td>gemma-2-9b-it</td><td><center><strong>7.50</strong></center></td><td><center><strong>7.91</strong></center></td><td><center><strong>7.09</strong></center></td><td><center>159/160</center></td>
-</tr>
-<tr>
-<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>6.08</center></td><td><center>6.78</center></td><td><center>5.39</center></td><td><center><strong>160/160</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma2-9b-Instruct-DPO-2024-10-09</em></td><td><center><em>6.77</em></center></td><td><center><em>7.24</em></center></td><td><center><em>6.30</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
-</tr>
-</tbody>
-</table>
-
-
-## RoCulturaBench
-
-<table>
-<tbody>
-<tr>
-<td><strong>Model</strong></td>
-<td><strong><center>Average</center></strong></td>
-<td><strong><center>Answers in Ro</center></strong></td>
-</tr>
-<tr>
-<td>gemma-2-9b-it</td><td><center><strong>5.68</strong></center></td><td><center><strong>100/100</strong></center></td>
-</tr>
-<tr>
-<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>4.20</center></td><td><center><strong>100/100</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma2-9b-Instruct-DPO-2024-10-09</em></td><td><center><em>4.83</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
-</tr>
-</tbody>
-</table>
-
-
-## RoGemma2 Model Family
-
-| Model              | Link  |
-|--------------------|:--------:|
-|RoGemma2-9b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) |
-|*RoGemma2-9b-Instruct-DPO-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09) |
-
-
-## Citation 
-
-```
-@misc{masala2024vorbecstiromanecsterecipetrain,
-      title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, 
-      author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
-      year={2024},
-      eprint={2406.18266},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2406.18266}, 
-}
-```
-<!-- **APA:**
-
+---
+license: cc-by-nc-4.0
+language:
+- ro
+base_model:
+- OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23
+datasets:
+- OpenLLM-Ro/ro_dpo_helpsteer
+- OpenLLM-Ro/ro_dpo_ultrafeedback
+- OpenLLM-Ro/ro_dpo_magpie
+- OpenLLM-Ro/ro_dpo_argilla_magpie
+- OpenLLM-Ro/ro_dpo_helpsteer2
+model-index:
+    - name: OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23
+      results:
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: Score
+              type: Score
+              value: 7.26
+        - task:
+            type: text-generation
+          dataset:
+            name: RoCulturaBench
+            type: RoCulturaBench
+          metrics:
+            - name: Score
+              type: Score
+              value: 5.36
+        - task:
+            type: text-generation
+          dataset:
+            name: Romanian_Academic_Benchmarks
+            type: Romanian_Academic_Benchmarks
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 59.79
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 55.66
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 64.00
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 73.16
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 64.26
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 37.80
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_truthfulqa
+            type: OpenLLM-Ro/ro_truthfulqa
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 63.86
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 82.84
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 65.95
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 28.16
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 19.34
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average exact_match
+              type: exact_match
+              value: 30.82
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average f1
+              type: f1
+              value: 48.53
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average spearman
+              type: spearman
+              value: 73.24
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average pearson
+              type: pearson
+              value: 73.13
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: First turn
+              type: Score
+              value: 7.65
+            - name: Second turn
+              type: Score
+              value: 6.86
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 52.44
+            - name: 1-shot 
+              type: accuracy
+              value: 55.70
+            - name: 3-shot 
+              type: accuracy
+              value: 56.47
+            - name: 5-shot 
+              type: accuracy
+              value: 55.70
+            - name: 10-shot 
+              type: accuracy
+              value: 57.16
+            - name: 25-shot 
+              type: accuracy
+              value: 56.47
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 65.20
+            - name: 1-shot 
+              type: accuracy
+              value: 63.27
+            - name: 3-shot 
+              type: accuracy
+              value: 63.83
+            - name: 5-shot 
+              type: accuracy
+              value: 63.69
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 74.11
+            - name: 1-shot 
+              type: accuracy
+              value: 72.53
+            - name: 3-shot 
+              type: accuracy
+              value: 72.93
+            - name: 5-shot 
+              type: accuracy
+              value: 73.09
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 65.90
+            - name: 1-shot 
+              type: accuracy
+              value: 66.06
+            - name: 3-shot 
+              type: accuracy
+              value: 62.36
+            - name: 5-shot 
+              type: accuracy
+              value: 61.87
+            - name: 10-shot 
+              type: accuracy
+              value: 65.11
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: 1-shot 
+              type: accuracy
+              value: 16.83
+            - name: 3-shot 
+              type: accuracy
+              value: 43.21
+            - name: 5-shot 
+              type: accuracy
+              value: 53.37
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 39.18
+            - name: 1-shot 
+              type: macro-f1
+              value: 96.59
+            - name: 3-shot 
+              type: macro-f1
+              value: 97.63
+            - name: 5-shot 
+              type: macro-f1
+              value: 97.97
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 58.94
+            - name: 1-shot 
+              type: macro-f1
+              value: 64.99
+            - name: 3-shot 
+              type: macro-f1
+              value: 68.86
+            - name: 5-shot 
+              type: macro-f1
+              value: 71.03
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 26.89
+            - name: 1-shot 
+              type: bleu
+              value: 31.18
+            - name: 3-shot 
+              type: bleu
+              value: 30.65
+            - name: 5-shot 
+              type: bleu
+              value: 23.91
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 2.98
+            - name: 1-shot 
+              type: bleu
+              value: 20.30
+            - name: 3-shot 
+              type: bleu
+              value: 30.08
+            - name: 5-shot 
+              type: bleu
+              value: 24.01
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_EM
+            type: XQuAD_EM
+          metrics:
+            - name: 0-shot 
+              type: exact_match
+              value: 26.39
+            - name: 1-shot 
+              type: exact_match
+              value: 23.87
+            - name: 3-shot 
+              type: exact_match
+              value: 34.03
+            - name: 5-shot 
+              type: exact_match
+              value: 38.99
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_F1
+            type: XQuAD_F1
+          metrics:
+            - name: 0-shot 
+              type: f1
+              value: 43.28
+            - name: 1-shot 
+              type: f1
+              value: 37.38
+            - name: 3-shot 
+              type: f1
+              value: 54.08
+            - name: 5-shot 
+              type: f1
+              value: 59.38
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Spearman
+            type: STS_Spearman
+          metrics:
+            - name: 1-shot 
+              type: spearman
+              value: 73.46
+            - name: 3-shot 
+              type: spearman
+              value: 73.55
+            - name: 5-shot 
+              type: spearman
+              value: 72.70
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Pearson
+            type: STS_Pearson
+          metrics:
+            - name: 1-shot 
+              type: pearson
+              value: 74.87
+            - name: 3-shot 
+              type: pearson
+              value: 72.96
+            - name: 5-shot 
+              type: pearson
+              value: 71.55
+
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 9B model**. Links to other models can be found at the bottom of this page.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
+
+
+- **Developed by:** OpenLLM-Ro
+<!-- - **Funded by [optional]:** [More Information Needed] -->
+<!-- - **Shared by [optional]:** [More Information Needed] -->
+<!-- - **Model type:** [More Information Needed] -->
+- **Language(s):** Romanian
+- **License:** cc-by-nc-4.0
+- **Finetuned from model:** [RoGemma2-9b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23)
+- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer), [RoUltraFeedback](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_ultrafeedback), [RoMagpieDPO](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_magpie), [RoArgillaMagpie](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_argilla_magpie), [RoHelpSteer2](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer2)
+
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
+- **Paper:** https://arxiv.org/abs/2406.18266
+
+## Intended Use
+
+### Intended Use Cases
+
+RoGemma2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
+
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-04-23")
+model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2025-10-23")
+
+instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
+chat = [
+        {"role": "user", "content": instruction},
+        ]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
+
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+outputs = model.generate(input_ids=inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Academic Benchmarks
+
+<table>
+<tbody>
+<tr>
+<td><strong>Model</strong></td>
+<td><strong><center>Average</center></strong></td>
+<td><strong><center>ARC</center></strong></td>
+<td><strong><center>MMLU</center></strong></td>
+<td><strong><center>Winogrande</center></strong></td>
+<td><strong><center>Hellaswag</center></strong></td>
+<td><strong><center>GSM8k</center></strong></td>
+<td><strong><center>TruthfulQA</center></strong></td>
+</tr>
+<tr>
+<td>gemma-2-9b-it</td><td><center>56.22</center></td><td><center>50.33</center></td><td><center><strong>64.01</strong></center></td><td><center>64.88</center></td><td><center>63.11</center></td><td><center>41.95</center></td><td><center>53.03</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>57.06</center></td><td><center><strong>56.20</strong></center></td><td><center>62.98</center></td><td><center>71.00</center></td><td><center>60.52</center></td><td><center>37.86</center></td><td><center>53.77</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2025-04-23</td><td><center>54.39</center></td><td><center>50.24</center></td><td><center>62.00</center></td><td><center>70.38</center></td><td><center>52.25</center></td><td><center>40.51</center></td><td><center>50.97</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>59.08</center></td><td><center>54.10</center></td><td><center>63.41</center></td><td><center>70.02</center></td><td><center>59.35</center></td><td><center><strong>57.24</strong></center></td><td><center>50.39</center></td>
+</tr>
+<tr>
+<td><em>RoGemma2-9b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>59.79</strong></em></center></td><td><center><em>55.66</em></center></td><td><center><em>64.00</em></center></td><td><center><em><strong>73.16</strong></em></center></td><td><center><em><strong>64.26</strong></em></center></td><td><center><em>37.80</em></center></td><td><center><em><strong>63.86</strong></em></center></td>
+</tr>
+</tbody>
+</table>
+
+
+## Downstream tasks
+
+<table>
+<tbody>
+<tr>
+<td></td>
+<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
+<td colspan="4"><center><strong>WMT</strong></center></td>
+</tr>
+<tr>
+<td></td>
+<td colspan="2"><center><strong>Few-shot</strong></center></td>
+<td colspan="2"><center><strong>Finetuned</strong></center></td>
+<td colspan="2"><center><strong>Few-shot</strong></center></td>
+<td colspan="2"><center><strong>Finetuned</strong></center></td>
+</tr>
+<tr>
+<td><strong>Model</strong></td>
+<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
+<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
+<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
+<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
+<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
+<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
+<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
+<td><center><strong>RO-EN<br>(Bleu)</strong></center>
+</tr>
+<tr>
+<td>gemma-2-9b-it</td><td><center>90.82</center></td><td><center>52.51</center></td><td><center><strong>98.97</strong></center></td><td><center>86.02</center></td><td><center>19.97</center></td><td><center><strong>28.94</strong></center></td><td><center>27.94</center></td><td><center><strong>41.61</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>96.19</center></td><td><center>62.49</center></td><td><center>98.93</center></td><td><center><strong>88.33</strong></center></td><td><center>25.74</center></td><td><center>23.16</center></td><td><center><strong>28.43</strong></center></td><td><center>40.94</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2025-04-23</td><td><center>84.23</center></td><td><center>60.14</center></td><td><center>-</center></td><td><center>-</center></td><td><center>17.78</center></td><td><center>18.24</center></td><td><center>-</center></td><td><center>-</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center><strong>97.74</strong></center></td><td><center><strong>67.40</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center>27.32</center></td><td><center>15.96</center></td><td><center>-</center></td><td><center>-</center></td>
+</tr>
+<tr>
+<td><em>RoGemma2-9b-Instruct-DPO-2025-04-23</em></td><td><center><em>82.84</em></center></td><td><center><em>65.95</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>28.16</strong></em></center></td><td><center><em>19.34</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
+</tr>
+</tbody>
+</table>
+
+
+<table>
+<tbody>
+<tr>
+<td></td>
+<td colspan="4"><center><strong>XQuAD</strong></center></td>
+<td colspan="4"><center><strong>STS</strong></center></td>
+</tr>
+<tr>
+<td></td>
+<td colspan="2"><center><strong>Few-shot</strong></center></td>
+<td colspan="2"><center><strong>Finetuned</strong></center></td>
+<td colspan="2"><center><strong>Few-shot</strong></center></td>
+<td colspan="2"><center><strong>Finetuned</strong></center></td>
+</tr>
+<tr>
+<td><strong>Model</strong></td>
+<td><center><strong>(EM)</strong></center></td>
+<td><center><strong>(F1)</strong></center></td>
+<td><center><strong>(EM)</strong></center></td>
+<td><center><strong>(F1)</strong></center></td>
+<td><center><strong>(Spearman)</strong></center></td>
+<td><center><strong>(Pearson)</strong></center></td>
+<td><center><strong>(Spearman)</strong></center></td>
+<td><center><strong>(Pearson)</strong></center></td>
+</tr>
+<tr>
+<td>gemma-2-9b-it</td><td><center>37.56</center></td><td><center>57.48</center></td><td><center><strong>71.09</strong></center></td><td><center><strong>84.78</strong></center></td><td><center>71.39</center></td><td><center>71.73</center></td><td><center>89.07</center></td><td><center>89.29</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center><strong>51.37</strong></center></td><td><center><strong>70.74</strong></center></td><td><center>50.00</center></td><td><center>64.10</center></td><td><center>77.15</center></td><td><center>77.10</center></td><td><center><strong>89.45</strong></center></td><td><center><strong>89.89</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2025-04-23</td><td><center>49.22</center></td><td><center>66.33</center></td><td><center>-</center></td><td><center>-</center></td><td><center>70.17</center></td><td><center>70.80</center></td><td><center>-</center></td><td><center>-</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>32.42</center></td><td><center>58.68</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>80.82</strong></center></td><td><center><strong>81.50</strong></center></td><td><center>-</center></td><td><center>-</center></td>
+</tr>
+<tr>
+<td><em>RoGemma2-9b-Instruct-DPO-2025-04-23</em></td><td><center><em>30.82</em></center></td><td><center><em>48.53</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>73.24</em></center></td><td><center><em>73.13</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
+</tr>
+</tbody>
+</table>
+
+
+## MT-Bench
+
+<table>
+<tbody>
+<tr>
+<td><strong>Model</strong></td>
+<td><strong><center>Average</center></strong></td>
+<td><strong><center>1st turn</center></strong></td>
+<td><strong><center>2nd turn</center></strong></td>
+<td><strong><center>Answers in Ro</center></strong></td>
+</tr>
+<tr>
+<td>gemma-2-9b-it</td><td><center><strong>7.50</strong></center></td><td><center><strong>7.91</strong></center></td><td><center><strong>7.09</strong></center></td><td><center>159/160</center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>6.08</center></td><td><center>6.78</center></td><td><center>5.39</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2025-04-23</td><td><center>6.78</center></td><td><center>7.00</center></td><td><center>6.55</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>6.77</center></td><td><center>7.24</center></td><td><center>6.30</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td><em>RoGemma2-9b-Instruct-DPO-2025-04-23</em></td><td><center><em>7.26</em></center></td><td><center><em>7.65</em></center></td><td><center><em>6.86</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
+</tr>
+</tbody>
+</table>
+
+
+## RoCulturaBench
+
+<table>
+<tbody>
+<tr>
+<td><strong>Model</strong></td>
+<td><strong><center>Average</center></strong></td>
+<td><strong><center>Answers in Ro</center></strong></td>
+</tr>
+<tr>
+<td>gemma-2-9b-it</td><td><center><strong>5.68</strong></center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>4.20</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-2025-04-23</td><td><center>4.89</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>4.83</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td><em>RoGemma2-9b-Instruct-DPO-2025-04-23</em></td><td><center><em>5.36</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
+</tr>
+</tbody>
+</table>
+
+
+## RoGemma2 Model Family
+
+| Model              | Link  |
+|--------------------|:--------:|
+|RoGemma2-9b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) |
+|RoGemma2-9b-Instruct-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) |
+|RoGemma2-9b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09) |
+|*RoGemma2-9b-Instruct-DPO-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09) |
+
+
+
+## Citation 
+
+```
+@misc{masala2024vorbecstiromanecsterecipetrain,
+      title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, 
+      author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
+      year={2024},
+      eprint={2406.18266},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2406.18266}, 
+}
+```
+<!-- **APA:**
+
 [More Information Needed]  -->