| --- |
| license: cc-by-nc-4.0 |
| language: |
| - ro |
| base_model: |
| - OpenLLM-Ro/RoMistral-7b-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/RoMistral-7b-Instruct-DPO-2025-04-23 |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: Score |
| type: Score |
| value: 6.61 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoCulturaBench |
| type: RoCulturaBench |
| metrics: |
| - name: Score |
| type: Score |
| value: 4.93 |
| - task: |
| type: text-generation |
| dataset: |
| name: Romanian_Academic_Benchmarks |
| type: Romanian_Academic_Benchmarks |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 56.62 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 55.51 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 52.61 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 68.04 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_hellaswag |
| type: OpenLLM-Ro/ro_hellaswag |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 64.97 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 41.07 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_truthfulqa |
| type: OpenLLM-Ro/ro_truthfulqa |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 57.55 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 97.94 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 66.13 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 27.24 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 18.41 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average exact_match |
| type: exact_match |
| value: 40.86 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average f1 |
| type: f1 |
| value: 62.24 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average spearman |
| type: spearman |
| value: 77.89 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average pearson |
| type: pearson |
| value: 76.40 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: First turn |
| type: Score |
| value: 6.86 |
| - name: Second turn |
| type: Score |
| value: 6.35 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 53.56 |
| - name: 1-shot |
| type: accuracy |
| value: 52.96 |
| - name: 3-shot |
| type: accuracy |
| value: 55.01 |
| - name: 5-shot |
| type: accuracy |
| value: 56.64 |
| - name: 10-shot |
| type: accuracy |
| value: 57.07 |
| - name: 25-shot |
| type: accuracy |
| value: 57.84 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 53.37 |
| - name: 1-shot |
| type: accuracy |
| value: 51.73 |
| - name: 3-shot |
| type: accuracy |
| value: 52.64 |
| - name: 5-shot |
| type: accuracy |
| value: 52.68 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 67.09 |
| - name: 1-shot |
| type: accuracy |
| value: 67.72 |
| - name: 3-shot |
| type: accuracy |
| value: 67.96 |
| - name: 5-shot |
| type: accuracy |
| value: 69.38 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_hellaswag |
| type: OpenLLM-Ro/ro_hellaswag |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 65.04 |
| - name: 1-shot |
| type: accuracy |
| value: 64.00 |
| - name: 3-shot |
| type: accuracy |
| value: 64.82 |
| - name: 5-shot |
| type: accuracy |
| value: 65.37 |
| - name: 10-shot |
| type: accuracy |
| value: 65.60 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: 1-shot |
| type: accuracy |
| value: 34.19 |
| - name: 3-shot |
| type: accuracy |
| value: 42.76 |
| - name: 5-shot |
| type: accuracy |
| value: 46.25 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 97.47 |
| - name: 1-shot |
| type: macro-f1 |
| value: 98.00 |
| - name: 3-shot |
| type: macro-f1 |
| value: 98.20 |
| - name: 5-shot |
| type: macro-f1 |
| value: 98.10 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 56.61 |
| - name: 1-shot |
| type: macro-f1 |
| value: 68.50 |
| - name: 3-shot |
| type: macro-f1 |
| value: 68.86 |
| - name: 5-shot |
| type: macro-f1 |
| value: 70.57 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 26.03 |
| - name: 1-shot |
| type: bleu |
| value: 27.66 |
| - name: 3-shot |
| type: bleu |
| value: 27.81 |
| - name: 5-shot |
| type: bleu |
| value: 27.46 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 2.80 |
| - name: 1-shot |
| type: bleu |
| value: 8.45 |
| - name: 3-shot |
| type: bleu |
| value: 28.81 |
| - name: 5-shot |
| type: bleu |
| value: 33.58 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_EM |
| type: XQuAD_EM |
| metrics: |
| - name: 0-shot |
| type: exact_match |
| value: 26.05 |
| - name: 1-shot |
| type: exact_match |
| value: 41.93 |
| - name: 3-shot |
| type: exact_match |
| value: 47.31 |
| - name: 5-shot |
| type: exact_match |
| value: 48.15 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_F1 |
| type: XQuAD_F1 |
| metrics: |
| - name: 0-shot |
| type: f1 |
| value: 49.68 |
| - name: 1-shot |
| type: f1 |
| value: 62.52 |
| - name: 3-shot |
| type: f1 |
| value: 67.35 |
| - name: 5-shot |
| type: f1 |
| value: 69.42 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Spearman |
| type: STS_Spearman |
| metrics: |
| - name: 1-shot |
| type: spearman |
| value: 77.24 |
| - name: 3-shot |
| type: spearman |
| value: 77.10 |
| - name: 5-shot |
| type: spearman |
| value: 79.34 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Pearson |
| type: STS_Pearson |
| metrics: |
| - name: 1-shot |
| type: pearson |
| value: 76.32 |
| - name: 3-shot |
| type: pearson |
| value: 75.51 |
| - name: 5-shot |
| type: pearson |
| value: 77.36 |
|
|
| --- |
| |
| # Model Card for Model ID |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| This model points/is identical to [RoMistral-7b-Instruct-DPO-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-DPO-2025-04-23). |
|
|
|
|
| RoMistral is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 7B 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:** [RoMistral-7b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-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) |
|
|
|
|
| <!-- - **Finetuned from model [optional]:** [More Information Needed] --> |
|
|
| ### 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 |
|
|
| RoMistral 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/RoMistral-7b-Instruct-DPO") |
| model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoMistral-7b-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>Mistral-7B-Instruct-v0.2</td><td><center>47.40</center></td><td><center>46.29</center></td><td><center>47.00</center></td><td><center>58.78</center></td><td><center>54.27</center></td><td><center>13.47</center></td><td><center><strong>64.59</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-05-17</td><td><center>52.54</center></td><td><center>50.41</center></td><td><center>51.61</center></td><td><center>66.48</center></td><td><center>60.27</center></td><td><center>34.19</center></td><td><center>52.30</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-10-09</td><td><center>52.91</center></td><td><center>52.27</center></td><td><center>49.33</center></td><td><center><strong>70.03</strong></center></td><td><center>62.88</center></td><td><center>32.42</center></td><td><center>50.51</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2025-04-23</td><td><center>54.40</center></td><td><center>52.86</center></td><td><center>52.33</center></td><td><center>68.57</center></td><td><center>63.50</center></td><td><center>38.15</center></td><td><center>51.01</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>51.95</center></td><td><center>50.73</center></td><td><center>47.88</center></td><td><center>68.41</center></td><td><center>62.27</center></td><td><center>32.27</center></td><td><center>50.12</center></td> |
| </tr> |
| <tr> |
| <td><em>RoMistral-7b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>56.62</strong></em></center></td><td><center><em><strong>55.51</strong></em></center></td><td><center><em><strong>52.61</strong></em></center></td><td><center><em>68.04</em></center></td><td><center><em><strong>64.97</strong></em></center></td><td><center><em><strong>41.07</strong></em></center></td><td><center><em>57.55</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>Mistral-7B-Instruct-v0.2</td><td><center>96.97</center></td><td><center>56.66</center></td><td><center>98.83</center></td><td><center>87.32</center></td><td><center>18.60</center></td><td><center><strong>33.99</strong></center></td><td><center>26.19</center></td><td><center>39.88</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-05-17</td><td><center>97.36</center></td><td><center>67.55</center></td><td><center>98.80</center></td><td><center><strong>88.28</strong></center></td><td><center>27.93</center></td><td><center>13.21</center></td><td><center><strong>28.72</strong></center></td><td><center><strong>40.86</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-10-09</td><td><center>95.56</center></td><td><center><strong>67.83</strong></center></td><td><center><strong>99.00</strong></center></td><td><center>87.57</center></td><td><center>28.28</center></td><td><center>6.10</center></td><td><center>27.70</center></td><td><center>40.36</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2025-04-23</td><td><center>97.67</center></td><td><center>61.79</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>28.69</strong></center></td><td><center>19.23</center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>82.13</center></td><td><center>65.24</center></td><td><center>-</center></td><td><center>-</center></td><td><center>26.25</center></td><td><center>6.09</center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td><em>RoMistral-7b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>97.94</strong></em></center></td><td><center><em>66.13</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>27.24</em></center></td><td><center><em>18.41</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>Mistral-7B-Instruct-v0.2</td><td><center>27.92</center></td><td><center>50.71</center></td><td><center><strong>65.46</strong></center></td><td><center><strong>79.73</strong></center></td><td><center>62.62</center></td><td><center>60.86</center></td><td><center>84.92</center></td><td><center>85.44</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-05-17</td><td><center>43.66</center></td><td><center>63.70</center></td><td><center>55.04</center></td><td><center>72.31</center></td><td><center>77.43</center></td><td><center><strong>78.43</strong></center></td><td><center>87.25</center></td><td><center>87.79</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-10-09</td><td><center>41.09</center></td><td><center>63.21</center></td><td><center>47.56</center></td><td><center>62.69</center></td><td><center>78.47</center></td><td><center>77.24</center></td><td><center><strong>87.28</strong></center></td><td><center><strong>87.88</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2025-04-23</td><td><center><strong>49.05</strong></center></td><td><center><strong>69.11</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>78.67</strong></center></td><td><center>77.08</center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>23.40</center></td><td><center>45.80</center></td><td><center>-</center></td><td><center>-</center></td><td><center>77.33</center></td><td><center>76.60</center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td><em>RoMistral-7b-Instruct-DPO-2025-04-23</em></td><td><center><em>40.86</em></center></td><td><center><em>62.24</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>77.89</em></center></td><td><center><em>76.40</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>Mistral-7B-Instruct-v0.2</td><td><center>5.03</center></td><td><center>5.05</center></td><td><center>5.00</center></td><td><center>154/160</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-05-17</td><td><center>4.99</center></td><td><center>5.46</center></td><td><center>4.53</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-10-09</td><td><center>5.29</center></td><td><center>5.86</center></td><td><center>4.72</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2025-04-23</td><td><center>6.24</center></td><td><center>6.78</center></td><td><center>5.70</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>5.88</center></td><td><center>6.44</center></td><td><center>5.33</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoMistral-7b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>6.61</strong></em></center></td><td><center><em><strong>6.86</strong></em></center></td><td><center><em><strong>6.35</strong></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>Mistral-7B-Instruct-v0.2</td><td><center>3.68</center></td><td><center>97/100</center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-05-17</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2024-10-09</td><td><center>3.99</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-2025-04-23</td><td><center>4.36</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>4.72</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoMistral-7b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>4.93</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
| </tr> |
| </tbody> |
| </table> |
|
|
|
|
|
|
|
|
| ## RoMistral Model Family |
|
|
| | Model | Link | |
| |--------------------|:--------:| |
| |RoMistral-7b-Instruct-2024-05-17| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17) | |
| |RoMistral-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09) | |
| |RoMistral-7b-Instruct-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-2025-04-23) | |
| |RoMistral-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-DPO-2024-10-09) | |
| |*RoMistral-7b-Instruct-DPO-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-DPO-2025-04-23) | |
|
|
|
|
|
|
| ## 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] --> |
