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README.md

@@ -1,750 +1,693 @@
----
-license: cc-by-nc-4.0
-language:
-- ro
-base_model:
-- google/gemma-7b
-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/RoGemma-7b-Instruct-2024-10-09
-      results:
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: Score
-              type: Score
-              value: 5.24
-        - task:
-            type: text-generation
-          dataset:
-            name: RoCulturaBench
-            type: RoCulturaBench
-          metrics:
-            - name: Score
-              type: Score
-              value: 3.51
-        - task:
-            type: text-generation
-          dataset:
-            name: Romanian_Academic_Benchmarks
-            type: Romanian_Academic_Benchmarks
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 50.48
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 52.01
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 52.37
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 66.97
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 56.34
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 25.98
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_truthfulqa
-            type: OpenLLM-Ro/ro_truthfulqa
-          metrics:
-            - name: Average accuracy
-              type: accuracy
-              value: 49.18
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 86.96
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 56.72
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary_finetuned
-            type: LaRoSeDa_binary_finetuned
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 98.80
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass_finetuned
-            type: LaRoSeDa_multiclass_finetuned
-          metrics:
-            - name: Average macro-f1
-              type: macro-f1
-              value: 85.81
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 24.45
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 14.20
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO_finetuned
-            type: WMT_EN-RO_finetuned
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 25.96
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN_finetuned
-            type: WMT_RO-EN_finetuned
-          metrics:
-            - name: Average bleu
-              type: bleu
-              value: 39.07
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average exact_match
-              type: exact_match
-              value: 26.03
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD
-            type: XQuAD
-          metrics:
-            - name: Average f1
-              type: f1
-              value: 41.58
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_finetuned
-            type: XQuAD_finetuned
-          metrics:
-            - name: Average exact_match
-              type: exact_match
-              value: 46.72
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_finetuned
-            type: XQuAD_finetuned
-          metrics:
-            - name: Average f1
-              type: f1
-              value: 60.79
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average spearman
-              type: spearman
-              value: 73.23
-        - task:
-            type: text-generation
-          dataset:
-            name: STS
-            type: STS
-          metrics:
-            - name: Average pearson
-              type: pearson
-              value: 71.58
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_finetuned
-            type: STS_finetuned
-          metrics:
-            - name: Average spearman
-              type: spearman
-              value: 88.42
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_finetuned
-            type: STS_finetuned
-          metrics:
-            - name: Average pearson
-              type: pearson
-              value: 88.45
-        - task:
-            type: text-generation
-          dataset:
-            name: RoMT-Bench
-            type: RoMT-Bench
-          metrics:
-            - name: First turn
-              type: Score
-              value: 5.55
-            - name: Second turn
-              type: Score
-              value: 4.94
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_arc_challenge
-            type: OpenLLM-Ro/ro_arc_challenge
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 49.53
-            - name: 1-shot 
-              type: accuracy
-              value: 52.53
-            - name: 3-shot 
-              type: accuracy
-              value: 51.50
-            - name: 5-shot 
-              type: accuracy
-              value: 53.56
-            - name: 10-shot 
-              type: accuracy
-              value: 52.53
-            - name: 25-shot 
-              type: accuracy
-              value: 52.44
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_mmlu
-            type: OpenLLM-Ro/ro_mmlu
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 51.81
-            - name: 1-shot 
-              type: accuracy
-              value: 52.45
-            - name: 3-shot 
-              type: accuracy
-              value: 52.52
-            - name: 5-shot 
-              type: accuracy
-              value: 52.70
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_winogrande
-            type: OpenLLM-Ro/ro_winogrande
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 66.54
-            - name: 1-shot 
-              type: accuracy
-              value: 66.69
-            - name: 3-shot 
-              type: accuracy
-              value: 67.09
-            - name: 5-shot 
-              type: accuracy
-              value: 67.56
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_hellaswag
-            type: OpenLLM-Ro/ro_hellaswag
-          metrics:
-            - name: 0-shot 
-              type: accuracy
-              value: 58.80
-            - name: 1-shot 
-              type: accuracy
-              value: 57.04
-            - name: 3-shot 
-              type: accuracy
-              value: 55.85
-            - name: 5-shot 
-              type: accuracy
-              value: 54.15
-            - name: 10-shot 
-              type: accuracy
-              value: 55.88
-        - task:
-            type: text-generation
-          dataset:
-            name: OpenLLM-Ro/ro_gsm8k
-            type: OpenLLM-Ro/ro_gsm8k
-          metrics:
-            - name: 1-shot 
-              type: accuracy
-              value: 22.06
-            - name: 3-shot 
-              type: accuracy
-              value: 25.40
-            - name: 5-shot 
-              type: accuracy
-              value: 30.48
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_binary
-            type: LaRoSeDa_binary
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 87.28
-            - name: 1-shot 
-              type: macro-f1
-              value: 86.40
-            - name: 3-shot 
-              type: macro-f1
-              value: 87.95
-            - name: 5-shot 
-              type: macro-f1
-              value: 86.20
-        - task:
-            type: text-generation
-          dataset:
-            name: LaRoSeDa_multiclass
-            type: LaRoSeDa_multiclass
-          metrics:
-            - name: 0-shot 
-              type: macro-f1
-              value: 38.35
-            - name: 1-shot 
-              type: macro-f1
-              value: 63.86
-            - name: 3-shot 
-              type: macro-f1
-              value: 62.03
-            - name: 5-shot 
-              type: macro-f1
-              value: 62.62
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_EN-RO
-            type: WMT_EN-RO
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 11.39
-            - name: 1-shot 
-              type: bleu
-              value: 28.08
-            - name: 3-shot 
-              type: bleu
-              value: 29.18
-            - name: 5-shot 
-              type: bleu
-              value: 29.13
-        - task:
-            type: text-generation
-          dataset:
-            name: WMT_RO-EN
-            type: WMT_RO-EN
-          metrics:
-            - name: 0-shot 
-              type: bleu
-              value: 1.92
-            - name: 1-shot 
-              type: bleu
-              value: 9.39
-            - name: 3-shot 
-              type: bleu
-              value: 21.81
-            - name: 5-shot 
-              type: bleu
-              value: 23.66
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_EM
-            type: XQuAD_EM
-          metrics:
-            - name: 0-shot 
-              type: exact_match
-              value: 32.77
-            - name: 1-shot 
-              type: exact_match
-              value: 20.25
-            - name: 3-shot 
-              type: exact_match
-              value: 18.49
-            - name: 5-shot 
-              type: exact_match
-              value: 32.60
-        - task:
-            type: text-generation
-          dataset:
-            name: XQuAD_F1
-            type: XQuAD_F1
-          metrics:
-            - name: 0-shot 
-              type: f1
-              value: 47.98
-            - name: 1-shot 
-              type: f1
-              value: 34.92
-            - name: 3-shot 
-              type: f1
-              value: 33.27
-            - name: 5-shot 
-              type: f1
-              value: 50.14
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Spearman
-            type: STS_Spearman
-          metrics:
-            - name: 1-shot 
-              type: spearman
-              value: 71.75
-            - name: 3-shot 
-              type: spearman
-              value: 71.83
-            - name: 5-shot 
-              type: spearman
-              value: 76.11
-        - task:
-            type: text-generation
-          dataset:
-            name: STS_Pearson
-            type: STS_Pearson
-          metrics:
-            - name: 1-shot 
-              type: pearson
-              value: 69.97
-            - name: 3-shot 
-              type: pearson
-              value: 69.87
-            - name: 5-shot 
-              type: pearson
-              value: 74.89
-
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-This model points/is identical to [RoGemma-7b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09).
-
-RoGemma 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:** [gemma-7b](https://huggingface.co/google/gemma-7b)
-- **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)
-
-
-### 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
-
-RoGemma 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/RoGemma-7b-Instruct")
-model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-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>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>50.48</em></center></td><td><center><em>52.01</em></center></td><td><center><em>52.37</em></center></td><td><center><em>66.97</em></center></td><td><center><em>56.34</em></center></td><td><center><em>25.98</em></center></td><td><center><em>49.18</em></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>48.27</center></td><td><center>46.66</center></td><td><center><strong>54.45</strong></center></td><td><center>63.73</center></td><td><center>49.33</center></td><td><center><strong>34.98</strong></center></td><td><center>40.45</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>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>86.96</em></center></td><td><center><em>56.72</em></center></td><td><center><em><strong>98.80</strong></em></center></td><td><center><em>85.81</em></center></td><td><center><em>24.45</em></center></td><td><center><em>14.20</em></center></td><td><center><em>25.96</em></center></td><td><center><em>39.07</em></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>96.45</center></td><td><center>63.23</center></td><td><center>-</center></td><td><center>-</center></td><td><center>20.73</center></td><td><center>7.87</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>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center><strong>73.96</strong></center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td>
-</tr>
-<tr>
-<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>26.03</em></center></td><td><center><em>41.58</em></center></td><td><center><em>46.72</em></center></td><td><center><em>60.79</em></center></td><td><center><em>73.23</em></center></td><td><center><em>71.58</em></center></td><td><center><em><strong>88.42</strong></em></center></td><td><center><em><strong>88.45</strong></em></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>19.14</center></td><td><center>38.10</center></td><td><center>-</center></td><td><center>-</center></td><td><center>69.38</center></td><td><center>69.34</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>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center><strong>5.92</strong></center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>5.24</em></center></td><td><center><em>5.55</em></center></td><td><center><em>4.94</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>5.47</strong></center></td><td><center><strong>5.92</strong></center></td><td><center><strong>5.03</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>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td>
-</tr>
-<tr>
-<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>3.51</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
-</tr>
-<tr>
-<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>3.94</strong></center></td><td><center><strong>100/100</strong></center></td>
-</tr>
-</tbody>
-</table>
-
-## RoGemma Model Family
-
-| Model              | Link  |
-|--------------------|:--------:|
-|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) |
-|*RoGemma-7b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
-|RoGemma-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-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:
+- google/gemma-7b
+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/RoGemma-7b-Instruct-2025-04-23
+      results:
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: Score
+              type: Score
+              value: 6.28
+        - task:
+            type: text-generation
+          dataset:
+            name: RoCulturaBench
+            type: RoCulturaBench
+          metrics:
+            - name: Score
+              type: Score
+              value: 3.65
+        - task:
+            type: text-generation
+          dataset:
+            name: Romanian_Academic_Benchmarks
+            type: Romanian_Academic_Benchmarks
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 50.52
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 47.70
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 51.66
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 66.32
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 53.59
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 36.04
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_truthfulqa
+            type: OpenLLM-Ro/ro_truthfulqa
+          metrics:
+            - name: Average accuracy
+              type: accuracy
+              value: 47.81
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 95.44
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: Average macro-f1
+              type: macro-f1
+              value: 59.24
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 25.17
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: Average bleu
+              type: bleu
+              value: 21.17
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average exact_match
+              type: exact_match
+              value: 15.88
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD
+            type: XQuAD
+          metrics:
+            - name: Average f1
+              type: f1
+              value: 29.16
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average spearman
+              type: spearman
+              value: 75.90
+        - task:
+            type: text-generation
+          dataset:
+            name: STS
+            type: STS
+          metrics:
+            - name: Average pearson
+              type: pearson
+              value: 75.16
+        - task:
+            type: text-generation
+          dataset:
+            name: RoMT-Bench
+            type: RoMT-Bench
+          metrics:
+            - name: First turn
+              type: Score
+              value: 6.97
+            - name: Second turn
+              type: Score
+              value: 5.58
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_arc_challenge
+            type: OpenLLM-Ro/ro_arc_challenge
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 46.19
+            - name: 1-shot 
+              type: accuracy
+              value: 46.53
+            - name: 3-shot 
+              type: accuracy
+              value: 46.02
+            - name: 5-shot 
+              type: accuracy
+              value: 48.33
+            - name: 10-shot 
+              type: accuracy
+              value: 49.27
+            - name: 25-shot 
+              type: accuracy
+              value: 49.87
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_mmlu
+            type: OpenLLM-Ro/ro_mmlu
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 51.13
+            - name: 1-shot 
+              type: accuracy
+              value: 50.94
+            - name: 3-shot 
+              type: accuracy
+              value: 52.67
+            - name: 5-shot 
+              type: accuracy
+              value: 51.90
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_winogrande
+            type: OpenLLM-Ro/ro_winogrande
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 67.40
+            - name: 1-shot 
+              type: accuracy
+              value: 65.04
+            - name: 3-shot 
+              type: accuracy
+              value: 65.67
+            - name: 5-shot 
+              type: accuracy
+              value: 67.17
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_hellaswag
+            type: OpenLLM-Ro/ro_hellaswag
+          metrics:
+            - name: 0-shot 
+              type: accuracy
+              value: 58.03
+            - name: 1-shot 
+              type: accuracy
+              value: 56.63
+            - name: 3-shot 
+              type: accuracy
+              value: 52.47
+            - name: 5-shot 
+              type: accuracy
+              value: 48.63
+            - name: 10-shot 
+              type: accuracy
+              value: 52.18
+        - task:
+            type: text-generation
+          dataset:
+            name: OpenLLM-Ro/ro_gsm8k
+            type: OpenLLM-Ro/ro_gsm8k
+          metrics:
+            - name: 1-shot 
+              type: accuracy
+              value: 24.11
+            - name: 3-shot 
+              type: accuracy
+              value: 37.76
+            - name: 5-shot 
+              type: accuracy
+              value: 46.25
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_binary
+            type: LaRoSeDa_binary
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 96.33
+            - name: 1-shot 
+              type: macro-f1
+              value: 94.62
+            - name: 3-shot 
+              type: macro-f1
+              value: 95.06
+            - name: 5-shot 
+              type: macro-f1
+              value: 95.76
+        - task:
+            type: text-generation
+          dataset:
+            name: LaRoSeDa_multiclass
+            type: LaRoSeDa_multiclass
+          metrics:
+            - name: 0-shot 
+              type: macro-f1
+              value: 43.65
+            - name: 1-shot 
+              type: macro-f1
+              value: 64.30
+            - name: 3-shot 
+              type: macro-f1
+              value: 64.22
+            - name: 5-shot 
+              type: macro-f1
+              value: 64.81
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_EN-RO
+            type: WMT_EN-RO
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 13.30
+            - name: 1-shot 
+              type: bleu
+              value: 28.59
+            - name: 3-shot 
+              type: bleu
+              value: 29.48
+            - name: 5-shot 
+              type: bleu
+              value: 29.31
+        - task:
+            type: text-generation
+          dataset:
+            name: WMT_RO-EN
+            type: WMT_RO-EN
+          metrics:
+            - name: 0-shot 
+              type: bleu
+              value: 1.11
+            - name: 1-shot 
+              type: bleu
+              value: 18.97
+            - name: 3-shot 
+              type: bleu
+              value: 31.99
+            - name: 5-shot 
+              type: bleu
+              value: 32.60
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_EM
+            type: XQuAD_EM
+          metrics:
+            - name: 0-shot 
+              type: exact_match
+              value: 17.31
+            - name: 1-shot 
+              type: exact_match
+              value: 12.44
+            - name: 3-shot 
+              type: exact_match
+              value: 13.11
+            - name: 5-shot 
+              type: exact_match
+              value: 20.67
+        - task:
+            type: text-generation
+          dataset:
+            name: XQuAD_F1
+            type: XQuAD_F1
+          metrics:
+            - name: 0-shot 
+              type: f1
+              value: 29.90
+            - name: 1-shot 
+              type: f1
+              value: 24.24
+            - name: 3-shot 
+              type: f1
+              value: 25.64
+            - name: 5-shot 
+              type: f1
+              value: 36.86
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Spearman
+            type: STS_Spearman
+          metrics:
+            - name: 1-shot 
+              type: spearman
+              value: 76.50
+            - name: 3-shot 
+              type: spearman
+              value: 73.63
+            - name: 5-shot 
+              type: spearman
+              value: 77.58
+        - task:
+            type: text-generation
+          dataset:
+            name: STS_Pearson
+            type: STS_Pearson
+          metrics:
+            - name: 1-shot 
+              type: pearson
+              value: 75.15
+            - name: 3-shot 
+              type: pearson
+              value: 72.69
+            - name: 5-shot 
+              type: pearson
+              value: 77.63
+
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+RoGemma 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:** [gemma-7b](https://huggingface.co/google/gemma-7b)
+- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat), [RoMagpiePro](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_mt), [RoMagpieReasoning](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_reasoning)
+
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
+- **Paper:** https://arxiv.org/abs/2406.18266
+
+## Intended Use
+
+### Intended Use Cases
+
+RoGemma 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/RoGemma-7b-Instruct-2025-04-23")
+model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-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>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>50.48</center></td><td><center>52.01</center></td><td><center>52.37</center></td><td><center>66.97</center></td><td><center>56.34</center></td><td><center>25.98</center></td><td><center>49.18</center></td>
+</tr>
+<tr>
+<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>50.52</em></center></td><td><center><em>47.70</em></center></td><td><center><em>51.66</em></center></td><td><center><em>66.32</em></center></td><td><center><em>53.59</em></center></td><td><center><em><strong>36.04</strong></em></center></td><td><center><em>47.81</em></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>48.27</center></td><td><center>46.66</center></td><td><center><strong>54.45</strong></center></td><td><center>63.73</center></td><td><center>49.33</center></td><td><center>34.98</center></td><td><center>40.45</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>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>86.96</center></td><td><center>56.72</center></td><td><center><strong>98.80</strong></center></td><td><center>85.81</center></td><td><center>24.45</center></td><td><center>14.20</center></td><td><center>25.96</center></td><td><center>39.07</center></td>
+</tr>
+<tr>
+<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>95.44</em></center></td><td><center><em>59.24</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>25.17</em></center></td><td><center><em>21.17</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>96.45</center></td><td><center>63.23</center></td><td><center>-</center></td><td><center>-</center></td><td><center>20.73</center></td><td><center>7.87</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>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center>73.96</center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>26.03</center></td><td><center>41.58</center></td><td><center>46.72</center></td><td><center>60.79</center></td><td><center>73.23</center></td><td><center>71.58</center></td><td><center><strong>88.42</strong></center></td><td><center><strong>88.45</strong></center></td>
+</tr>
+<tr>
+<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>15.88</em></center></td><td><center><em>29.16</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em><strong>75.90</strong></em></center></td><td><center><em><strong>75.16</strong></em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>19.14</center></td><td><center>38.10</center></td><td><center>-</center></td><td><center>-</center></td><td><center>69.38</center></td><td><center>69.34</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>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center>5.92</center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>5.24</center></td><td><center>5.55</center></td><td><center>4.94</center></td><td><center><strong>160/160</strong></center></td>
+</tr>
+<tr>
+<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em><strong>6.28</strong></em></center></td><td><center><em><strong>6.97</strong></em></center></td><td><center><em><strong>5.58</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>5.47</center></td><td><center>5.92</center></td><td><center>5.03</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>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>3.51</center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+<tr>
+<td><em>RoGemma-7b-Instruct-2025-04-23</em></td><td><center><em>3.65</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
+</tr>
+<tr>
+<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>3.94</strong></center></td><td><center><strong>100/100</strong></center></td>
+</tr>
+</tbody>
+</table>
+
+## RoGemma Model Family
+
+| Model              | Link  |
+|--------------------|:--------:|
+|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) |
+|RoGemma-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
+|*RoGemma-7b-Instruct-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2025-04-23) |
+|RoGemma-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-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:**
+
 [More Information Needed]  -->