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Model: OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO
Source: Original Platform

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README.md

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+---
+license: cc-by-nc-4.0
+language:
+- ro
+base_model:
+- OpenLLM-Ro/RoLlama3.1-8b-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/RoLlama3.1-8b-Instruct-DPO-2025-04-23
+      results:
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: Score
+              type: Score
+              value: 7.00
+        - task:
+            type: text-generation
+          dataset:
+            name: RoCulturaBench
+            type: RoCulturaBench
+          metrics:
+            - name: Score
+              type: Score
+              value: 4.73
+        - task:
+            type: text-generation
+          dataset:
+            name: Romanian_Academic_Benchmarks
+            type: Romanian_Academic_Benchmarks
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 53.76
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 51.09
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 56.22
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 66.77
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 59.38
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 31.54
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_truthfulqa
+            type: OpenLLM-Ro/ro_truthfulqa
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 57.56
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 96.87
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 60.75
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 20.30
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 18.57
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average exact_match
+              type: exact_match
+              value: 9.22
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average f1
+              type: f1
+              value: 22.75
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average spearman
+              type: spearman
+              value: 30.82
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average pearson
+              type: pearson
+              value: 20.25
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: First turn
+              type: Score
+              value: 7.30
+            - name: Second turn
+              type: Score
+              value: 6.70
+        - 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: 52.10
+            - name: 3-shot 
+              type: accuracy
+              value: 50.99
+            - name: 5-shot 
+              type: accuracy
+              value: 50.81
+            - name: 10-shot 
+              type: accuracy
+              value: 49.70
+            - 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: 56.88
+            - name: 1-shot 
+              type: accuracy
+              value: 55.61
+            - name: 3-shot 
+              type: accuracy
+              value: 56.06
+            - name: 5-shot 
+              type: accuracy
+              value: 56.31
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 65.67
+            - name: 1-shot 
+              type: accuracy
+              value: 66.30
+            - name: 3-shot 
+              type: accuracy
+              value: 67.40
+            - name: 5-shot 
+              type: accuracy
+              value: 67.72
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 60.53
+            - name: 1-shot 
+              type: accuracy
+              value: 60.37
+            - name: 3-shot 
+              type: accuracy
+              value: 58.20
+            - name: 5-shot 
+              type: accuracy
+              value: 58.18
+            - name: 10-shot 
+              type: accuracy
+              value: 59.61
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: 1-shot 
+              type: accuracy
+              value: 25.09
+            - name: 3-shot 
+              type: accuracy
+              value: 30.02
+            - name: 5-shot 
+              type: accuracy
+              value: 39.50
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 95.39
+            - name: 1-shot 
+              type: macro-f1
+              value: 95.90
+            - name: 3-shot 
+              type: macro-f1
+              value: 98.00
+            - name: 5-shot 
+              type: macro-f1
+              value: 98.17
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 60.30
+            - name: 1-shot 
+              type: macro-f1
+              value: 64.73
+            - name: 3-shot 
+              type: macro-f1
+              value: 58.69
+            - name: 5-shot 
+              type: macro-f1
+              value: 59.30
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 5.46
+            - name: 1-shot 
+              type: bleu
+              value: 26.08
+            - name: 3-shot 
+              type: bleu
+              value: 25.90
+            - name: 5-shot 
+              type: bleu
+              value: 23.76
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 2.74
+            - name: 1-shot 
+              type: bleu
+              value: 20.95
+            - name: 3-shot 
+              type: bleu
+              value: 31.53
+            - name: 5-shot 
+              type: bleu
+              value: 19.05
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_EM
+            type: XQuAD_EM
+          metrics:
+            - name: 0-shot 
+              type: exact_match
+              value: 12.27
+            - name: 1-shot 
+              type: exact_match
+              value: 17.98
+            - name: 3-shot 
+              type: exact_match
+              value: 5.04
+            - name: 5-shot 
+              type: exact_match
+              value: 1.60
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_F1
+            type: XQuAD_F1
+          metrics:
+            - name: 0-shot 
+              type: f1
+              value: 26.24
+            - name: 1-shot 
+              type: f1
+              value: 32.54
+            - name: 3-shot 
+              type: f1
+              value: 18.00
+            - name: 5-shot 
+              type: f1
+              value: 14.22
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Spearman
+            type: STS_Spearman
+          metrics:
+            - name: 1-shot 
+              type: spearman
+              value: 76.70
+            - name: 3-shot 
+              type: spearman
+              value: 2.82
+            - name: 5-shot 
+              type: spearman
+              value: 12.95
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Pearson
+            type: STS_Pearson
+          metrics:
+            - name: 1-shot 
+              type: pearson
+              value: 77.30
+            - name: 3-shot 
+              type: pearson
+              value: -14.56
+            - name: 5-shot 
+              type: pearson
+              value: -1.99
+
+---
+
+# Model Card for Model ID
+
+*Built with Meta Llama 3.1*
+
+This model points/is identical to [RoLlama3.1-8b-Instruct-DPO-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO-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 **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-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:** [RoLlama3.1-8b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-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
+
+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-DPO")
+model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-DPO")
+
+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>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>53.36</center></td><td><center>48.97</center></td><td><center>55.17</center></td><td><center>66.52</center></td><td><center><strong>60.73</strong></center></td><td><center>42.03</center></td><td><center>46.71</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><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>53.76</strong></em></center></td><td><center><em><strong>51.09</strong></em></center></td><td><center><em><strong>56.22</strong></em></center></td><td><center><em><strong>66.77</strong></em></center></td><td><center><em>59.38</em></center></td><td><center><em>31.54</em></center></td><td><center><em><strong>57.56</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-3.1-8B-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>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>95.32</center></td><td><center><strong>60.84</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>23.18</strong></center></td><td><center>25.11</center></td><td><center>-</center></td><td><center>-</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><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>96.87</strong></em></center></td><td><center><em>60.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>20.30</em></center></td><td><center><em>18.57</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-3.1-8B-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>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>10.74</center></td><td><center>19.75</center></td><td><center>-</center></td><td><center>-</center></td><td><center>73.53</center></td><td><center>74.93</center></td><td><center>-</center></td><td><center>-</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><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em>9.22</em></center></td><td><center><em>22.75</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>30.82</em></center></td><td><center><em>20.25</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>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>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>6.43</center></td><td><center>6.78</center></td><td><center>6.09</center></td><td><center><strong>160/160</strong></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><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>7.00</strong></em></center></td><td><center><em><strong>7.30</strong></em></center></td><td><center><em><strong>6.70</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-3.1-8B-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>RoLlama3.1-8b-Instruct-2025-04-23</td><td><center>4.28</center></td><td><center><strong>100/100</strong></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><em>RoLlama3.1-8b-Instruct-DPO-2025-04-23</em></td><td><center><em><strong>4.73</strong></em></center></td><td><center><em><strong>100/100</strong></em></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]  -->

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special_tokens_map.json

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