| --- |
| license: cc-by-nc-4.0 |
| language: |
| - ro |
| base_model: |
| - google/gemma-2-9b-it |
| datasets: |
| - OpenLLM-Ro/ro_sft_alpaca |
| - OpenLLM-Ro/ro_sft_alpaca_gpt4 |
| - OpenLLM-Ro/ro_sft_dolly |
| - OpenLLM-Ro/ro_sft_selfinstruct_gpt4 |
| - OpenLLM-Ro/ro_sft_norobots |
| - OpenLLM-Ro/ro_sft_orca |
| - OpenLLM-Ro/ro_sft_camel |
| - OpenLLM-Ro/ro_sft_oasst |
| - OpenLLM-Ro/ro_sft_ultrachat |
| - OpenLLM-Ro/ro_sft_magpie_mt |
| - OpenLLM-Ro/ro_sft_magpie_reasoning |
| model-index: |
| - name: OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23 |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: Score |
| type: Score |
| value: 6.78 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoCulturaBench |
| type: RoCulturaBench |
| metrics: |
| - name: Score |
| type: Score |
| value: 4.89 |
| - task: |
| type: text-generation |
| dataset: |
| name: Romanian_Academic_Benchmarks |
| type: Romanian_Academic_Benchmarks |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 54.39 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 50.24 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 62.00 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 70.38 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_hellaswag |
| type: OpenLLM-Ro/ro_hellaswag |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 52.25 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 40.51 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_truthfulqa |
| type: OpenLLM-Ro/ro_truthfulqa |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 50.97 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 84.23 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 60.14 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 17.78 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 18.24 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average exact_match |
| type: exact_match |
| value: 49.22 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average f1 |
| type: f1 |
| value: 66.33 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average spearman |
| type: spearman |
| value: 70.17 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average pearson |
| type: pearson |
| value: 70.80 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: First turn |
| type: Score |
| value: 7.00 |
| - name: Second turn |
| type: Score |
| value: 6.55 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 47.47 |
| - name: 1-shot |
| type: accuracy |
| value: 50.56 |
| - name: 3-shot |
| type: accuracy |
| value: 50.73 |
| - name: 5-shot |
| type: accuracy |
| value: 50.39 |
| - name: 10-shot |
| type: accuracy |
| value: 50.99 |
| - name: 25-shot |
| type: accuracy |
| value: 51.33 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 58.73 |
| - name: 1-shot |
| type: accuracy |
| value: 60.12 |
| - name: 3-shot |
| type: accuracy |
| value: 64.93 |
| - name: 5-shot |
| type: accuracy |
| value: 64.21 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 66.06 |
| - name: 1-shot |
| type: accuracy |
| value: 70.40 |
| - name: 3-shot |
| type: accuracy |
| value: 72.30 |
| - 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: 56.30 |
| - name: 1-shot |
| type: accuracy |
| value: 58.29 |
| - name: 3-shot |
| type: accuracy |
| value: 50.88 |
| - name: 5-shot |
| type: accuracy |
| value: 44.38 |
| - name: 10-shot |
| type: accuracy |
| value: 51.41 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: 1-shot |
| type: accuracy |
| value: 27.29 |
| - name: 3-shot |
| type: accuracy |
| value: 39.04 |
| - name: 5-shot |
| type: accuracy |
| value: 55.19 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 59.19 |
| - name: 1-shot |
| type: macro-f1 |
| value: 94.22 |
| - name: 3-shot |
| type: macro-f1 |
| value: 93.24 |
| - name: 5-shot |
| type: macro-f1 |
| value: 90.27 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 32.52 |
| - name: 1-shot |
| type: macro-f1 |
| value: 68.64 |
| - name: 3-shot |
| type: macro-f1 |
| value: 70.14 |
| - name: 5-shot |
| type: macro-f1 |
| value: 69.26 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 1.96 |
| - name: 1-shot |
| type: bleu |
| value: 27.30 |
| - name: 3-shot |
| type: bleu |
| value: 28.31 |
| - name: 5-shot |
| type: bleu |
| value: 13.56 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 0.66 |
| - name: 1-shot |
| type: bleu |
| value: 26.76 |
| - name: 3-shot |
| type: bleu |
| value: 31.88 |
| - name: 5-shot |
| type: bleu |
| value: 13.66 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_EM |
| type: XQuAD_EM |
| metrics: |
| - name: 0-shot |
| type: exact_match |
| value: 49.92 |
| - name: 1-shot |
| type: exact_match |
| value: 47.98 |
| - name: 3-shot |
| type: exact_match |
| value: 45.71 |
| - name: 5-shot |
| type: exact_match |
| value: 53.28 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_F1 |
| type: XQuAD_F1 |
| metrics: |
| - name: 0-shot |
| type: f1 |
| value: 67.52 |
| - name: 1-shot |
| type: f1 |
| value: 63.97 |
| - name: 3-shot |
| type: f1 |
| value: 62.39 |
| - name: 5-shot |
| type: f1 |
| value: 71.43 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Spearman |
| type: STS_Spearman |
| metrics: |
| - name: 1-shot |
| type: spearman |
| value: 82.53 |
| - name: 3-shot |
| type: spearman |
| value: 65.73 |
| - name: 5-shot |
| type: spearman |
| value: 62.25 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Pearson |
| type: STS_Pearson |
| metrics: |
| - name: 1-shot |
| type: pearson |
| value: 82.89 |
| - name: 3-shot |
| type: pearson |
| value: 66.26 |
| - name: 5-shot |
| type: pearson |
| value: 63.25 |
|
|
|
|
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
| This model points/is identical to [RoGemma2-9b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23). |
|
|
| RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **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:** [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) |
| - **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat), [RoMagpiePro](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_mt), [RoMagpieReasoning](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_reasoning) |
|
|
|
|
| ### 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") |
| model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct") |
| |
| 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><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>54.39</em></center></td><td><center><em>50.24</em></center></td><td><center><em>62.00</em></center></td><td><center><em>70.38</em></center></td><td><center><em>52.25</em></center></td><td><center><em>40.51</em></center></td><td><center><em>50.97</em></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>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center><strong>59.79</strong></center></td><td><center>55.66</center></td><td><center>64.00</center></td><td><center><strong>73.16</strong></center></td><td><center><strong>64.26</strong></center></td><td><center>37.80</center></td><td><center><strong>63.86</strong></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-2025-04-23</em></td><td><center><em>84.23</em></center></td><td><center><em>60.14</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>17.78</em></center></td><td><center><em>18.24</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></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>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>82.84</center></td><td><center>65.95</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>28.16</strong></center></td><td><center>19.34</center></td><td><center>-</center></td><td><center>-</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-2025-04-23</em></td><td><center><em>49.22</em></center></td><td><center><em>66.33</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>70.17</em></center></td><td><center><em>70.80</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></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>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>30.82</center></td><td><center>48.53</center></td><td><center>-</center></td><td><center>-</center></td><td><center>73.24</center></td><td><center>73.13</center></td><td><center>-</center></td><td><center>-</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-2025-04-23</em></td><td><center><em>6.78</em></center></td><td><center><em>7.00</em></center></td><td><center><em>6.55</em></center></td><td><center><em><strong>160/160</strong></em></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>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>7.26</center></td><td><center>7.65</center></td><td><center>6.86</center></td><td><center><strong>160/160</strong></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-2025-04-23</em></td><td><center><em>4.89</em></center></td><td><center><em><strong>100/100</strong></em></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>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>5.36</center></td><td><center><strong>100/100</strong></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] --> |
