RoLlama3-8b-Instruct-DPO

Model Card for Model ID

Built with Meta Llama 3

This model points/is identical to RoLlama3-8b-Instruct-DPO-2025-04-23.

RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the human aligned 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 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: RoLlama3-8b-Instruct-2025-04-23
• Trained using: RoHelpSteer, RoUltraFeedback, RoMagpieDPO, RoArgillaMagpie, RoHelpSteer2

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 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.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-DPO")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-DPO")

instruction = "Care este cel mai înalt vârf muntos din România?"
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)

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-3-8B-Instruct</td><td><center>50.62</center></td><td><center>43.69</center></td><td><center>52.04</center></td><td><center>59.33</center></td><td><center>53.19</center></td><td><center>43.87</center></td><td><center>51.59</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>50.56</center></td><td><center>44.70</center></td><td><center>52.19</center></td><td><center><strong>67.23</strong></center></td><td><center>57.69</center></td><td><center>30.23</center></td><td><center>51.34</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>52.21</center></td><td><center>47.94</center></td><td><center>53.50</center></td><td><center>66.06</center></td><td><center>59.72</center></td><td><center>40.16</center></td><td><center>45.90</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2025-04-23</td><td><center>54.66</center></td><td><center>50.31</center></td><td><center><strong>55.91</strong></center></td><td><center>67.01</center></td><td><center><strong>61.73</strong></center></td><td><center><strong>47.41</strong></center></td><td><center>45.61</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>49.96</center></td><td><center>46.29</center></td><td><center>53.29</center></td><td><center>65.57</center></td><td><center>58.15</center></td><td><center>34.77</center></td><td><center>41.70</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>55.86</strong></em></center></td><td><center><em><strong>52.26</strong></em></center></td><td><center><em>55.35</em></center></td><td><center><em>66.62</em></center></td><td><center><em>59.93</em></center></td><td><center><em>43.95</em></center></td><td><center><em><strong>57.06</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>Llama-3-8B-Instruct</td><td><center>95.88</center></td><td><center>56.21</center></td><td><center><strong>98.53</strong></center></td><td><center>86.19</center></td><td><center>18.88</center></td><td><center><strong>30.98</strong></center></td><td><center><strong>28.02</strong></center></td><td><center>40.28</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>97.52</center></td><td><center><strong>67.41</strong></center></td><td><center>94.15</center></td><td><center>87.13</center></td><td><center><strong>24.01</strong></center></td><td><center>27.36</center></td><td><center>26.53</center></td><td><center>40.36</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>95.58</center></td><td><center>61.20</center></td><td><center>96.46</center></td><td><center><strong>87.26</strong></center></td><td><center>22.92</center></td><td><center>24.28</center></td><td><center>27.31</center></td><td><center><strong>40.52</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2025-04-23</td><td><center>96.21</center></td><td><center>59.15</center></td><td><center>-</center></td><td><center>-</center></td><td><center>23.32</center></td><td><center>22.50</center></td><td><center>-</center></td><td><center>-</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>97.48</center></td><td><center>54.00</center></td><td><center>-</center></td><td><center>-</center></td><td><center>22.09</center></td><td><center>23.00</center></td><td><center>-</center></td><td><center>-</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>97.60</strong></em></center></td><td><center><em>62.16</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>18.14</em></center></td><td><center><em>14.13</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>Llama-3-8B-Instruct</td><td><center><strong>39.47</strong></center></td><td><center>58.67</center></td><td><center><strong>67.65</strong></center></td><td><center><strong>82.77</strong></center></td><td><center>73.04</center></td><td><center>72.36</center></td><td><center>83.49</center></td><td><center>84.06</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>39.43</center></td><td><center><strong>59.50</strong></center></td><td><center>44.45</center></td><td><center>59.76</center></td><td><center>77.20</center></td><td><center>77.87</center></td><td><center>85.80</center></td><td><center>86.05</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>18.89</center></td><td><center>31.79</center></td><td><center>50.84</center></td><td><center>65.18</center></td><td><center>77.60</center></td><td><center>76.86</center></td><td><center><strong>86.70</strong></center></td><td><center><strong>87.09</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2025-04-23</td><td><center>11.01</center></td><td><center>23.55</center></td><td><center>-</center></td><td><center>-</center></td><td><center>76.78</center></td><td><center>74.36</center></td><td><center>-</center></td><td><center>-</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>26.05</center></td><td><center>42.77</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>79.64</strong></center></td><td><center><strong>79.52</strong></center></td><td><center>-</center></td><td><center>-</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-DPO-2025-04-23</em></td><td><center><em>30.65</em></center></td><td><center><em>46.29</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>67.62</em></center></td><td><center><em>67.82</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td> </tr> </tbody> </table>

Romanian 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-3-8B-Instruct</td><td><center>5.96</center></td><td><center>6.16</center></td><td><center>5.76</center></td><td><center>158/160</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>5.15</center></td><td><center>6.03</center></td><td><center>4.28</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>5.38</center></td><td><center>6.09</center></td><td><center>4.67</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2025-04-23</td><td><center>6.39</center></td><td><center><strong>7.12</strong></center></td><td><center>5.66</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>5.87</center></td><td><center>6.22</center></td><td><center>5.49</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>6.67</strong></em></center></td><td><center><em>6.81</em></center></td><td><center><em><strong>6.54</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>Llama-3-8B-Instruct</td><td><center>4.62</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>3.71</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>3.81</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2025-04-23</td><td><center>4.05</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>4.40</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>4.83</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td> </tr> </tbody> </table>

RoLlama3 Model Family

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] -->