Upload README.md with huggingface_hub

README.md

@@ -1,761 +1,718 @@
----
-license: cc-by-nc-4.0
-language:
-- ro
-base_model:
-- mistralai/Mistral-7B-v0.1
-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
-model-index:
-    - name: OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09
-      results:
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: Score
-              type: Score
-              value: 5.29
-        - task:
-            type: text-generation
-          dataset:
-            name: RoCulturaBench
-            type: RoCulturaBench
-          metrics:
-            - name: Score
-              type: Score
-              value: 3.99
-        - task:
-            type: text-generation
-          dataset:
-            name: Romanian_Academic_Benchmarks
-            type: Romanian_Academic_Benchmarks
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 52.91
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 52.27
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 49.33
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 70.03
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 62.88
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 32.42
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_truthfulqa
-            type: OpenLLM-Ro/ro_truthfulqa
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 50.51
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 95.56
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 67.83
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary_finetuned
-            type: LaRoSeDa_binary_finetuned
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 99.00
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass_finetuned
-            type: LaRoSeDa_multiclass_finetuned
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 87.57
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 28.28
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 6.10
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO_finetuned
-            type: WMT_EN-RO_finetuned
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 27.70
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN_finetuned
-            type: WMT_RO-EN_finetuned
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 40.36
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average exact_match
-              type: exact_match
-              value: 41.09
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average f1
-              type: f1
-              value: 63.21
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_finetuned
-            type: XQuAD_finetuned
-          metrics:
-            - name: Average exact_match
-              type: exact_match
-              value: 47.56
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_finetuned
-            type: XQuAD_finetuned
-          metrics:
-            - name: Average f1
-              type: f1
-              value: 62.69
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average spearman
-              type: spearman
-              value: 78.47
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average pearson
-              type: pearson
-              value: 77.24
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_finetuned
-            type: STS_finetuned
-          metrics:
-            - name: Average spearman
-              type: spearman
-              value: 87.28
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_finetuned
-            type: STS_finetuned
-          metrics:
-            - name: Average pearson
-              type: pearson
-              value: 87.88
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: First turn
-              type: Score
-              value: 5.86
-            - name: Second turn
-              type: Score
-              value: 4.72
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 52.10
-            - name: 1-shot 
-              type: accuracy
-              value: 49.87
-            - name: 3-shot 
-              type: accuracy
-              value: 51.76
-            - name: 5-shot 
-              type: accuracy
-              value: 52.10
-            - name: 10-shot 
-              type: accuracy
-              value: 53.64
-            - name: 25-shot 
-              type: accuracy
-              value: 54.16
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 43.86
-            - name: 1-shot 
-              type: accuracy
-              value: 47.70
-            - name: 3-shot 
-              type: accuracy
-              value: 52.48
-            - name: 5-shot 
-              type: accuracy
-              value: 53.29
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 68.27
-            - name: 1-shot 
-              type: accuracy
-              value: 69.30
-            - name: 3-shot 
-              type: accuracy
-              value: 70.56
-            - name: 5-shot 
-              type: accuracy
-              value: 71.98
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 63.03
-            - name: 1-shot 
-              type: accuracy
-              value: 62.39
-            - name: 3-shot 
-              type: accuracy
-              value: 62.54
-            - name: 5-shot 
-              type: accuracy
-              value: 62.95
-            - name: 10-shot 
-              type: accuracy
-              value: 63.47
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: 1-shot 
-              type: accuracy
-              value: 25.47
-            - name: 3-shot 
-              type: accuracy
-              value: 33.06
-            - name: 5-shot 
-              type: accuracy
-              value: 38.74
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 88.87
-            - name: 1-shot 
-              type: macro-f1
-              value: 97.40
-            - name: 3-shot 
-              type: macro-f1
-              value: 98.13
-            - name: 5-shot 
-              type: macro-f1
-              value: 97.83
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 66.79
-            - name: 1-shot 
-              type: macro-f1
-              value: 67.00
-            - name: 3-shot 
-              type: macro-f1
-              value: 67.63
-            - name: 5-shot 
-              type: macro-f1
-              value: 69.88
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 23.84
-            - name: 1-shot 
-              type: bleu
-              value: 29.49
-            - name: 3-shot 
-              type: bleu
-              value: 30.29
-            - name: 5-shot 
-              type: bleu
-              value: 29.49
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 3.14
-            - name: 1-shot 
-              type: bleu
-              value: 3.18
-            - name: 3-shot 
-              type: bleu
-              value: 6.72
-            - name: 5-shot 
-              type: bleu
-              value: 11.35
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_EM
-            type: XQuAD_EM
-          metrics:
-            - name: 0-shot 
-              type: exact_match
-              value: 35.21
-            - name: 1-shot 
-              type: exact_match
-              value: 40.76
-            - name: 3-shot 
-              type: exact_match
-              value: 43.70
-            - name: 5-shot 
-              type: exact_match
-              value: 44.71
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_F1
-            type: XQuAD_F1
-          metrics:
-            - name: 0-shot 
-              type: f1
-              value: 57.74
-            - name: 1-shot 
-              type: f1
-              value: 61.96
-            - name: 3-shot 
-              type: f1
-              value: 65.55
-            - name: 5-shot 
-              type: f1
-              value: 67.59
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Spearman
-            type: STS_Spearman
-          metrics:
-            - name: 1-shot 
-              type: spearman
-              value: 77.38
-            - name: 3-shot 
-              type: spearman
-              value: 79.28
-            - name: 5-shot 
-              type: spearman
-              value: 78.75
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Pearson
-            type: STS_Pearson
-          metrics:
-            - name: 1-shot 
-              type: pearson
-              value: 77.10
-            - name: 3-shot 
-              type: pearson
-              value: 77.70
-            - name: 5-shot 
-              type: pearson
-              value: 76.91
-
-
----
-
-# Model Card for Model ID
-
-
-This model points/is identical to [RoMistral-7b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09).
-
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-RoMistral is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **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:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-- **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)
-
-
-<!-- - **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")
-model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoMistral-7b-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>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><strong>51.61</strong></center></td><td><center>66.48</center></td><td><center>60.27</center></td><td><center><strong>34.19</strong></center></td><td><center>52.30</center></td>
-</tr>
-<tr>
-<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em><strong>52.91</strong></em></center></td><td><center><em><strong>52.27</strong></em></center></td><td><center><em>49.33</em></center></td><td><center><em><strong>70.03</strong></em></center></td><td><center><em><strong>62.88</strong></em></center></td><td><center><em>32.42</em></center></td><td><center><em>50.51</em></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>
-</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><strong>97.36</strong></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><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>95.56</em></center></td><td><center><em><strong>67.83</strong></em></center></td><td><center><em><strong>99.00</strong></em></center></td><td><center><em>87.57</em></center></td><td><center><em><strong>28.28</strong></em></center></td><td><center><em>6.10</em></center></td><td><center><em>27.70</em></center></td><td><center><em>40.36</em></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>
-</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><strong>43.66</strong></center></td><td><center><strong>63.70</strong></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><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>41.09</em></center></td><td><center><em>63.21</em></center></td><td><center><em>47.56</em></center></td><td><center><em>62.69</em></center></td><td><center><em><strong>78.47</strong></em></center></td><td><center><em>77.24</em></center></td><td><center><em><strong>87.28</strong></em></center></td><td><center><em><strong>87.88</strong></em></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>
-</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><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>5.29</em></center></td><td><center><em>5.86</em></center></td><td><center><em>4.72</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
-</tr>
-<tr>
-<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center><strong>5.88</strong></center></td><td><center><strong>6.44</strong></center></td><td><center><strong>5.33</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>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><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>3.99</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
-</tr>
-<tr>
-<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center><strong>4.72</strong></center></td><td><center><strong>100/100</strong></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-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-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:**
-
+---
+license: cc-by-nc-4.0
+language:
+- ro
+base_model:
+- mistralai/Mistral-7B-v0.3
+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/RoMistral-7b-Instruct-2025-04-23
+      results:
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: Score
+              type: Score
+              value: 6.24
+        - task:
+            type: text-generation
+          dataset:
+            name: RoCulturaBench
+            type: RoCulturaBench
+          metrics:
+            - name: Score
+              type: Score
+              value: 4.36
+        - task:
+            type: text-generation
+          dataset:
+            name: Romanian_Academic_Benchmarks
+            type: Romanian_Academic_Benchmarks
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 54.40
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 52.86
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 52.33
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 68.57
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 63.50
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 38.15
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_truthfulqa
+            type: OpenLLM-Ro/ro_truthfulqa
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 51.01
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 97.67
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 61.79
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 28.69
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 19.23
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average exact_match
+              type: exact_match
+              value: 49.05
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average f1
+              type: f1
+              value: 69.11
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average spearman
+              type: spearman
+              value: 78.67
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average pearson
+              type: pearson
+              value: 77.08
+        - 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: 5.70
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 50.04
+            - name: 1-shot 
+              type: accuracy
+              value: 50.99
+            - name: 3-shot 
+              type: accuracy
+              value: 53.30
+            - name: 5-shot 
+              type: accuracy
+              value: 53.73
+            - name: 10-shot 
+              type: accuracy
+              value: 54.07
+            - name: 25-shot 
+              type: accuracy
+              value: 55.01
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 51.04
+            - name: 1-shot 
+              type: accuracy
+              value: 52.53
+            - name: 3-shot 
+              type: accuracy
+              value: 53.22
+            - name: 5-shot 
+              type: accuracy
+              value: 52.52
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 66.38
+            - name: 1-shot 
+              type: accuracy
+              value: 68.90
+            - name: 3-shot 
+              type: accuracy
+              value: 68.82
+            - name: 5-shot 
+              type: accuracy
+              value: 70.17
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 62.61
+            - name: 1-shot 
+              type: accuracy
+              value: 63.19
+            - name: 3-shot 
+              type: accuracy
+              value: 63.46
+            - name: 5-shot 
+              type: accuracy
+              value: 63.92
+            - name: 10-shot 
+              type: accuracy
+              value: 64.34
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: 1-shot 
+              type: accuracy
+              value: 27.98
+            - name: 3-shot 
+              type: accuracy
+              value: 40.46
+            - name: 5-shot 
+              type: accuracy
+              value: 46.02
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 97.87
+            - name: 1-shot 
+              type: macro-f1
+              value: 96.73
+            - name: 3-shot 
+              type: macro-f1
+              value: 98.20
+            - name: 5-shot 
+              type: macro-f1
+              value: 97.87
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 45.15
+            - name: 1-shot 
+              type: macro-f1
+              value: 65.77
+            - name: 3-shot 
+              type: macro-f1
+              value: 66.57
+            - name: 5-shot 
+              type: macro-f1
+              value: 69.66
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 28.92
+            - name: 1-shot 
+              type: bleu
+              value: 28.42
+            - name: 3-shot 
+              type: bleu
+              value: 28.85
+            - name: 5-shot 
+              type: bleu
+              value: 28.58
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 3.56
+            - name: 1-shot 
+              type: bleu
+              value: 9.60
+            - name: 3-shot 
+              type: bleu
+              value: 29.53
+            - name: 5-shot 
+              type: bleu
+              value: 34.25
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_EM
+            type: XQuAD_EM
+          metrics:
+            - name: 0-shot 
+              type: exact_match
+              value: 45.21
+            - name: 1-shot 
+              type: exact_match
+              value: 49.83
+            - name: 3-shot 
+              type: exact_match
+              value: 50.34
+            - name: 5-shot 
+              type: exact_match
+              value: 50.84
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_F1
+            type: XQuAD_F1
+          metrics:
+            - name: 0-shot 
+              type: f1
+              value: 66.40
+            - name: 1-shot 
+              type: f1
+              value: 68.92
+            - name: 3-shot 
+              type: f1
+              value: 70.68
+            - name: 5-shot 
+              type: f1
+              value: 70.44
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Spearman
+            type: STS_Spearman
+          metrics:
+            - name: 1-shot 
+              type: spearman
+              value: 79.08
+            - name: 3-shot 
+              type: spearman
+              value: 78.65
+            - name: 5-shot 
+              type: spearman
+              value: 78.29
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Pearson
+            type: STS_Pearson
+          metrics:
+            - name: 1-shot 
+              type: pearson
+              value: 77.79
+            - name: 3-shot 
+              type: pearson
+              value: 76.89
+            - name: 5-shot 
+              type: pearson
+              value: 76.57
+
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+RoMistral is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **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:** [Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3)
+- **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)
+
+
+<!-- - **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-2025-04-23")
+model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoMistral-7b-Instruct-2025-04-23")
+
+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><em>RoMistral-7b-Instruct-2025-04-23</em></td><td><center><em>54.40</em></center></td><td><center><em>52.86</em></center></td><td><center><em>52.33</em></center></td><td><center><em>68.57</em></center></td><td><center><em>63.50</em></center></td><td><center><em>38.15</em></center></td><td><center><em>51.01</em></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>RoMistral-7b-Instruct-DPO-2025-04-23</td><td><center><strong>56.62</strong></center></td><td><center><strong>55.51</strong></center></td><td><center><strong>52.61</strong></center></td><td><center>68.04</center></td><td><center><strong>64.97</strong></center></td><td><center><strong>41.07</strong></center></td><td><center>57.55</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><em>RoMistral-7b-Instruct-2025-04-23</em></td><td><center><em>97.67</em></center></td><td><center><em>61.79</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>28.69</strong></em></center></td><td><center><em>19.23</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></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>RoMistral-7b-Instruct-DPO-2025-04-23</td><td><center><strong>97.94</strong></center></td><td><center>66.13</center></td><td><center>-</center></td><td><center>-</center></td><td><center>27.24</center></td><td><center>18.41</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>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><em>RoMistral-7b-Instruct-2025-04-23</em></td><td><center><em><strong>49.05</strong></em></center></td><td><center><em><strong>69.11</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>78.67</strong></em></center></td><td><center><em>77.08</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></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>RoMistral-7b-Instruct-DPO-2025-04-23</td><td><center>40.86</center></td><td><center>62.24</center></td><td><center>-</center></td><td><center>-</center></td><td><center>77.89</center></td><td><center>76.40</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>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><em>RoMistral-7b-Instruct-2025-04-23</em></td><td><center><em>6.24</em></center></td><td><center><em>6.78</em></center></td><td><center><em>5.70</em></center></td><td><center><em><strong>160/160</strong></em></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>RoMistral-7b-Instruct-DPO-2025-04-23</td><td><center><strong>6.61</strong></center></td><td><center><strong>6.86</strong></center></td><td><center><strong>6.35</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>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><em>RoMistral-7b-Instruct-2025-04-23</em></td><td><center><em>4.36</em></center></td><td><center><em><strong>100/100</strong></em></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>RoMistral-7b-Instruct-DPO-2025-04-23</td><td><center><strong>4.93</strong></center></td><td><center><strong>100/100</strong></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]  -->