| --- |
| license: cc-by-nc-4.0 |
| language: |
| - ro |
| base_model: |
| - meta-llama/Llama-3.1-8B-Instruct |
| 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/RoLlama3.1-8b-Instruct-2025-04-23 |
| results: |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: Score |
| type: Score |
| value: 6.43 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoCulturaBench |
| type: RoCulturaBench |
| metrics: |
| - name: Score |
| type: Score |
| value: 4.28 |
| - task: |
| type: text-generation |
| dataset: |
| name: Romanian_Academic_Benchmarks |
| type: Romanian_Academic_Benchmarks |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 53.36 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 48.97 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 55.17 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 66.52 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_hellaswag |
| type: OpenLLM-Ro/ro_hellaswag |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 60.73 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 42.03 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_truthfulqa |
| type: OpenLLM-Ro/ro_truthfulqa |
| metrics: |
| - name: Average accuracy |
| type: accuracy |
| value: 46.71 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 95.32 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: Average macro-f1 |
| type: macro-f1 |
| value: 60.84 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 23.18 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: Average bleu |
| type: bleu |
| value: 25.11 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average exact_match |
| type: exact_match |
| value: 10.74 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD |
| type: XQuAD |
| metrics: |
| - name: Average f1 |
| type: f1 |
| value: 19.75 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average spearman |
| type: spearman |
| value: 73.53 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS |
| type: STS |
| metrics: |
| - name: Average pearson |
| type: pearson |
| value: 74.93 |
| - task: |
| type: text-generation |
| dataset: |
| name: RoMT-Bench |
| type: RoMT-Bench |
| metrics: |
| - name: First turn |
| type: Score |
| value: 6.78 |
| - name: Second turn |
| type: Score |
| value: 6.09 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_arc_challenge |
| type: OpenLLM-Ro/ro_arc_challenge |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 45.24 |
| - name: 1-shot |
| type: accuracy |
| value: 47.67 |
| - name: 3-shot |
| type: accuracy |
| value: 49.36 |
| - name: 5-shot |
| type: accuracy |
| value: 50.13 |
| - name: 10-shot |
| type: accuracy |
| value: 50.81 |
| - name: 25-shot |
| type: accuracy |
| value: 50.64 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_mmlu |
| type: OpenLLM-Ro/ro_mmlu |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 54.23 |
| - name: 1-shot |
| type: accuracy |
| value: 56.36 |
| - name: 3-shot |
| type: accuracy |
| value: 55.34 |
| - name: 5-shot |
| type: accuracy |
| value: 54.74 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_winogrande |
| type: OpenLLM-Ro/ro_winogrande |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 64.96 |
| - name: 1-shot |
| type: accuracy |
| value: 66.77 |
| - name: 3-shot |
| type: accuracy |
| value: 67.09 |
| - name: 5-shot |
| type: accuracy |
| value: 67.25 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_hellaswag |
| type: OpenLLM-Ro/ro_hellaswag |
| metrics: |
| - name: 0-shot |
| type: accuracy |
| value: 59.72 |
| - name: 1-shot |
| type: accuracy |
| value: 60.30 |
| - name: 3-shot |
| type: accuracy |
| value: 60.87 |
| - name: 5-shot |
| type: accuracy |
| value: 61.14 |
| - name: 10-shot |
| type: accuracy |
| value: 61.63 |
| - task: |
| type: text-generation |
| dataset: |
| name: OpenLLM-Ro/ro_gsm8k |
| type: OpenLLM-Ro/ro_gsm8k |
| metrics: |
| - name: 1-shot |
| type: accuracy |
| value: 30.86 |
| - name: 3-shot |
| type: accuracy |
| value: 43.90 |
| - name: 5-shot |
| type: accuracy |
| value: 51.33 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_binary |
| type: LaRoSeDa_binary |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 90.97 |
| - name: 1-shot |
| type: macro-f1 |
| value: 95.53 |
| - name: 3-shot |
| type: macro-f1 |
| value: 97.10 |
| - name: 5-shot |
| type: macro-f1 |
| value: 97.67 |
| - task: |
| type: text-generation |
| dataset: |
| name: LaRoSeDa_multiclass |
| type: LaRoSeDa_multiclass |
| metrics: |
| - name: 0-shot |
| type: macro-f1 |
| value: 63.20 |
| - name: 1-shot |
| type: macro-f1 |
| value: 64.47 |
| - name: 3-shot |
| type: macro-f1 |
| value: 55.88 |
| - name: 5-shot |
| type: macro-f1 |
| value: 59.80 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_EN-RO |
| type: WMT_EN-RO |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 4.92 |
| - name: 1-shot |
| type: bleu |
| value: 28.01 |
| - name: 3-shot |
| type: bleu |
| value: 30.16 |
| - name: 5-shot |
| type: bleu |
| value: 29.61 |
| - task: |
| type: text-generation |
| dataset: |
| name: WMT_RO-EN |
| type: WMT_RO-EN |
| metrics: |
| - name: 0-shot |
| type: bleu |
| value: 1.43 |
| - name: 1-shot |
| type: bleu |
| value: 24.78 |
| - name: 3-shot |
| type: bleu |
| value: 37.31 |
| - name: 5-shot |
| type: bleu |
| value: 36.93 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_EM |
| type: XQuAD_EM |
| metrics: |
| - name: 0-shot |
| type: exact_match |
| value: 11.18 |
| - name: 1-shot |
| type: exact_match |
| value: 26.47 |
| - name: 3-shot |
| type: exact_match |
| value: 3.95 |
| - name: 5-shot |
| type: exact_match |
| value: 1.34 |
| - task: |
| type: text-generation |
| dataset: |
| name: XQuAD_F1 |
| type: XQuAD_F1 |
| metrics: |
| - name: 0-shot |
| type: f1 |
| value: 25.76 |
| - name: 1-shot |
| type: f1 |
| value: 39.25 |
| - name: 3-shot |
| type: f1 |
| value: 8.40 |
| - name: 5-shot |
| type: f1 |
| value: 5.58 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Spearman |
| type: STS_Spearman |
| metrics: |
| - name: 1-shot |
| type: spearman |
| value: 73.52 |
| - name: 3-shot |
| type: spearman |
| value: 74.02 |
| - name: 5-shot |
| type: spearman |
| value: 73.06 |
| - task: |
| type: text-generation |
| dataset: |
| name: STS_Pearson |
| type: STS_Pearson |
| metrics: |
| - name: 1-shot |
| type: pearson |
| value: 75.81 |
| - name: 3-shot |
| type: pearson |
| value: 74.54 |
| - name: 5-shot |
| type: pearson |
| value: 74.43 |
|
|
| --- |
| |
| # Model Card for Model ID |
|
|
| *Built with Meta Llama 3.1* |
|
|
| This model points/is identical to [RoLlama3.1-8b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23). |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| RoLlama3.1 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 8B 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:** [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) |
| - **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 |
|
|
| RoLlama3.1 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/RoLlama3.1-8b-Instruct") |
| model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct") |
| |
| instruction = "Ce jocuri de societate pot juca cu prietenii mei?" |
| chat = [ |
| {"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."}, |
| {"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>Llama-3.1-8B-Instruct</td><td><center>49.87</center></td><td><center>42.86</center></td><td><center>53.73</center></td><td><center>59.71</center></td><td><center>56.82</center></td><td><center>35.56</center></td><td><center>50.54</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>53.03</center></td><td><center>47.69</center></td><td><center>54.57</center></td><td><center>65.84</center></td><td><center>59.94</center></td><td><center><strong>44.30</strong></center></td><td><center>45.82</center></td> |
| </tr> |
| <tr> |
| <td><em>RoLlama3.1-8b-Instruct-2025-04-23</em></td><td><center><em>53.36</em></center></td><td><center><em>48.97</em></center></td><td><center><em>55.17</em></center></td><td><center><em>66.52</em></center></td><td><center><em><strong>60.73</strong></em></center></td><td><center><em>42.03</em></center></td><td><center><em>46.71</em></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>52.74</center></td><td><center>44.84</center></td><td><center>55.06</center></td><td><center>65.87</center></td><td><center>58.67</center></td><td><center>44.17</center></td><td><center>47.82</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2025-04-23</td><td><center><strong>53.76</strong></center></td><td><center><strong>51.09</strong></center></td><td><center><strong>56.22</strong></center></td><td><center><strong>66.77</strong></center></td><td><center>59.38</center></td><td><center>31.54</center></td><td><center><strong>57.56</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>Llama-3.1-8B-Instruct</td><td><center>95.74</center></td><td><center>59.49</center></td><td><center><strong>98.57</strong></center></td><td><center>82.41</center></td><td><center>19.01</center></td><td><center><strong>27.77</strong></center></td><td><center><strong>29.02</strong></center></td><td><center>39.80</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>94.56</center></td><td><center>60.10</center></td><td><center>95.12</center></td><td><center><strong>87.53</strong></center></td><td><center>21.88</center></td><td><center>23.99</center></td><td><center>28.27</center></td><td><center><strong>40.44</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoLlama3.1-8b-Instruct-2025-04-23</em></td><td><center><em>95.32</em></center></td><td><center><em><strong>60.84</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>23.18</strong></em></center></td><td><center><em>25.11</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>96.10</center></td><td><center>55.37</center></td><td><center>-</center></td><td><center>-</center></td><td><center>21.29</center></td><td><center>21.86</center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2025-04-23</td><td><center><strong>96.87</strong></center></td><td><center>60.75</center></td><td><center>-</center></td><td><center>-</center></td><td><center>20.30</center></td><td><center>18.57</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>Llama-3.1-8B-Instruct</td><td><center><strong>44.96</strong></center></td><td><center><strong>64.45</strong></center></td><td><center><strong>69.50</strong></center></td><td><center><strong>84.31</strong></center></td><td><center>72.11</center></td><td><center>71.64</center></td><td><center>84.59</center></td><td><center>84.96</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>13.59</center></td><td><center>23.56</center></td><td><center>49.41</center></td><td><center>62.93</center></td><td><center>75.89</center></td><td><center>76.00</center></td><td><center><strong>86.86</strong></center></td><td><center><strong>87.05</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoLlama3.1-8b-Instruct-2025-04-23</em></td><td><center><em>10.74</em></center></td><td><center><em>19.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>73.53</em></center></td><td><center><em>74.93</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>21.58</center></td><td><center>36.54</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>78.01</strong></center></td><td><center><strong>77.98</strong></center></td><td><center>-</center></td><td><center>-</center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2025-04-23</td><td><center>9.22</center></td><td><center>22.75</center></td><td><center>-</center></td><td><center>-</center></td><td><center>30.82</center></td><td><center>20.25</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>Llama-3.1-8B-Instruct</td><td><center>5.69</center></td><td><center>5.85</center></td><td><center>5.53</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>5.42</center></td><td><center>5.95</center></td><td><center>4.89</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoLlama3.1-8b-Instruct-2025-04-23</em></td><td><center><em>6.43</em></center></td><td><center><em>6.78</em></center></td><td><center><em>6.09</em></center></td><td><center><em><strong>160/160</strong></em></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>6.21</center></td><td><center>6.74</center></td><td><center>5.69</center></td><td><center><strong>160/160</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2025-04-23</td><td><center><strong>7.00</strong></center></td><td><center><strong>7.30</strong></center></td><td><center><strong>6.70</strong></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>Llama-3.1-8B-Instruct</td><td><center>3.54</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-2024-10-09</td><td><center>3.55</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td><em>RoLlama3.1-8b-Instruct-2025-04-23</em></td><td><center><em>4.28</em></center></td><td><center><em><strong>100/100</strong></em></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>4.42</center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| <tr> |
| <td>RoLlama3.1-8b-Instruct-DPO-2025-04-23</td><td><center><strong>4.73</strong></center></td><td><center><strong>100/100</strong></center></td> |
| </tr> |
| </tbody> |
| </table> |
|
|
|
|
|
|
| ## RoLlama3.1 Model Family |
|
|
| | Model | Link | |
| |--------------------|:--------:| |
| |RoLlama3.1-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) | |
| |*RoLlama3.1-8b-Instruct-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) | |
| |RoLlama3.1-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2024-10-09) | |
| |RoLlama3.1-8b-Instruct-DPO-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-2025-04-23) | |
|
|
|
|
| ## Citation |
|
|
| ``` |
| @inproceedings{masala-etal-2024-vorbesti, |
| title = "``Vorbe\c{s}ti Rom{\^a}ne\c{s}te?'' A Recipe to Train Powerful {R}omanian {LLM}s with {E}nglish Instructions", |
| author = "Masala, Mihai and Ilie-Ablachim, Denis and Dima, Alexandru and Corlatescu, Dragos Georgian and Zavelca, Miruna-Andreea and Olaru, Ovio and Terian, Simina-Maria and Terian, Andrei and Leordeanu, Marius and Velicu, Horia and Popescu, Marius and Dascalu, Mihai and Rebedea, Traian", |
| editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", |
| month = nov, |
| year = "2024", |
| address = "Miami, Florida, USA", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2024.findings-emnlp.681/", |
| doi = "10.18653/v1/2024.findings-emnlp.681", |
| pages = "11632--11647" |
| } |
| ``` |
| <!-- **APA:** |
|
|
| [More Information Needed] --> |
