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---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoLlama2-7b-Base
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/RoLlama2-7b-Instruct-2024-10-09
  results:
  - task:
      type: text-generation
    dataset:
      name: RoMT-Bench
      type: RoMT-Bench
    metrics:
    - name: Score
      type: Score
      value: 4.43
  - task:
      type: text-generation
    dataset:
      name: RoCulturaBench
      type: RoCulturaBench
    metrics:
    - name: Score
      type: Score
      value: 4.08
  - task:
      type: text-generation
    dataset:
      name: Romanian_Academic_Benchmarks
      type: Romanian_Academic_Benchmarks
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 44.5
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_arc_challenge
      type: OpenLLM-Ro/ro_arc_challenge
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 44.73
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_mmlu
      type: OpenLLM-Ro/ro_mmlu
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 40.39
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_winogrande
      type: OpenLLM-Ro/ro_winogrande
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 63.67
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_hellaswag
      type: OpenLLM-Ro/ro_hellaswag
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 59.12
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_gsm8k
      type: OpenLLM-Ro/ro_gsm8k
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 13.29
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_truthfulqa
      type: OpenLLM-Ro/ro_truthfulqa
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 45.78
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary
      type: LaRoSeDa_binary
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 97.66
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass
      type: LaRoSeDa_multiclass
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 62.41
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary_finetuned
      type: LaRoSeDa_binary_finetuned
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 97.97
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass_finetuned
      type: LaRoSeDa_multiclass_finetuned
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 60.89
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO
      type: WMT_EN-RO
    metrics:
    - name: Average bleu
      type: bleu
      value: 27.13
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN
      type: WMT_RO-EN
    metrics:
    - name: Average bleu
      type: bleu
      value: 19.39
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO_finetuned
      type: WMT_EN-RO_finetuned
    metrics:
    - name: Average bleu
      type: bleu
      value: 27.63
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN_finetuned
      type: WMT_RO-EN_finetuned
    metrics:
    - name: Average bleu
      type: bleu
      value: 39.75
  - task:
      type: text-generation
    dataset:
      name: XQuAD
      type: XQuAD
    metrics:
    - name: Average exact_match
      type: exact_match
      value: 45.71
  - task:
      type: text-generation
    dataset:
      name: XQuAD
      type: XQuAD
    metrics:
    - name: Average f1
      type: f1
      value: 65.08
  - task:
      type: text-generation
    dataset:
      name: XQuAD_finetuned
      type: XQuAD_finetuned
    metrics:
    - name: Average exact_match
      type: exact_match
      value: 59.24
  - task:
      type: text-generation
    dataset:
      name: XQuAD_finetuned
      type: XQuAD_finetuned
    metrics:
    - name: Average f1
      type: f1
      value: 74.25
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: Average spearman
      type: spearman
      value: 59.69
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: Average pearson
      type: pearson
      value: 57.16
  - task:
      type: text-generation
    dataset:
      name: STS_finetuned
      type: STS_finetuned
    metrics:
    - name: Average spearman
      type: spearman
      value: 84.66
  - task:
      type: text-generation
    dataset:
      name: STS_finetuned
      type: STS_finetuned
    metrics:
    - name: Average pearson
      type: pearson
      value: 85.07
  - task:
      type: text-generation
    dataset:
      name: RoMT-Bench
      type: RoMT-Bench
    metrics:
    - name: First turn
      type: Score
      value: 4.92
    - name: Second turn
      type: Score
      value: 3.94
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_arc_challenge
      type: OpenLLM-Ro/ro_arc_challenge
    metrics:
    - name: 0-shot
      type: accuracy
      value: 42.67
    - name: 1-shot
      type: accuracy
      value: 44.64
    - name: 3-shot
      type: accuracy
      value: 44.9
    - name: 5-shot
      type: accuracy
      value: 45.16
    - name: 10-shot
      type: accuracy
      value: 45.67
    - name: 25-shot
      type: accuracy
      value: 45.33
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_mmlu
      type: OpenLLM-Ro/ro_mmlu
    metrics:
    - name: 0-shot
      type: accuracy
      value: 39.89
    - name: 1-shot
      type: accuracy
      value: 40.08
    - name: 3-shot
      type: accuracy
      value: 40.6
    - name: 5-shot
      type: accuracy
      value: 40.99
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_winogrande
      type: OpenLLM-Ro/ro_winogrande
    metrics:
    - name: 0-shot
      type: accuracy
      value: 63.06
    - name: 1-shot
      type: accuracy
      value: 62.98
    - name: 3-shot
      type: accuracy
      value: 65.19
    - name: 5-shot
      type: accuracy
      value: 63.46
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_hellaswag
      type: OpenLLM-Ro/ro_hellaswag
    metrics:
    - name: 0-shot
      type: accuracy
      value: 58.82
    - name: 1-shot
      type: accuracy
      value: 58.44
    - name: 3-shot
      type: accuracy
      value: 59.28
    - name: 5-shot
      type: accuracy
      value: 59.29
    - name: 10-shot
      type: accuracy
      value: 59.77
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_gsm8k
      type: OpenLLM-Ro/ro_gsm8k
    metrics:
    - name: 0-shot
      type: accuracy
      value: 6.14
    - name: 1-shot
      type: accuracy
      value: 15.01
    - name: 3-shot
      type: accuracy
      value: 18.72
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary
      type: LaRoSeDa_binary
    metrics:
    - name: 0-shot
      type: macro-f1
      value: 98.2
    - name: 1-shot
      type: macro-f1
      value: 96.63
    - name: 3-shot
      type: macro-f1
      value: 97.67
    - name: 5-shot
      type: macro-f1
      value: 98.13
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass
      type: LaRoSeDa_multiclass
    metrics:
    - name: 0-shot
      type: macro-f1
      value: 63.43
    - name: 1-shot
      type: macro-f1
      value: 53.58
    - name: 3-shot
      type: macro-f1
      value: 63.78
    - name: 5-shot
      type: macro-f1
      value: 68.85
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO
      type: WMT_EN-RO
    metrics:
    - name: 0-shot
      type: bleu
      value: 20.57
    - name: 1-shot
      type: bleu
      value: 29.59
    - name: 3-shot
      type: bleu
      value: 29.5
    - name: 5-shot
      type: bleu
      value: 28.88
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN
      type: WMT_RO-EN
    metrics:
    - name: 0-shot
      type: bleu
      value: 2.19
    - name: 1-shot
      type: bleu
      value: 9.97
    - name: 3-shot
      type: bleu
      value: 31.19
    - name: 5-shot
      type: bleu
      value: 34.23
  - task:
      type: text-generation
    dataset:
      name: XQuAD_EM
      type: XQuAD_EM
    metrics:
    - name: 0-shot
      type: exact_match
      value: 40.25
    - name: 1-shot
      type: exact_match
      value: 46.47
    - name: 3-shot
      type: exact_match
      value: 47.56
    - name: 5-shot
      type: exact_match
      value: 48.57
  - task:
      type: text-generation
    dataset:
      name: XQuAD_F1
      type: XQuAD_F1
    metrics:
    - name: 0-shot
      type: f1
      value: 62.24
    - name: 1-shot
      type: f1
      value: 65.33
    - name: 3-shot
      type: f1
      value: 65.89
    - name: 5-shot
      type: f1
      value: 66.86
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: 0-shot
      type: spearman
      value: 55.44
    - name: 1-shot
      type: spearman
      value: 61.98
    - name: 3-shot
      type: spearman
      value: 61.65
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: 0-shot
      type: pearson
      value: 56.18
    - name: 1-shot
      type: pearson
      value: 58.37
    - name: 3-shot
      type: pearson
      value: 56.94
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model points/is identical to [RoLlama2-7b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09).



RoLlama2 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 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:** [RoLlama2-7b-Base](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base)
- **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

RoLlama2 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/RoLlama2-7b-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Instruct")

instruction = "Care este cel mai înalt vârf muntos din România?"
chat = [
        {"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."},
        {"role": "user", "content": instruction},
        ]
prompt = tokenizer.apply_chat_template(chat, tokenize=False)

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```

## Academic Benchmarks

<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>Llama-2-7b-chat</td><td><center>36.84</center></td><td><center>37.03</center></td><td><center>33.80</center></td><td><center>55.87</center></td><td><center>45.36</center></td><td><center>4.90</center></td><td><center>44.09</center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center><strong>45.71</strong></center></td><td><center>43.66</center></td><td><center>39.70</center></td><td><center><strong>70.34</strong></center></td><td><center>57.36</center></td><td><center><strong>18.78</strong></center></td><td><center>44.44</center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-2024-10-09</em></td><td><center><em>44.50</em></center></td><td><center><em><strong>44.73</strong></em></center></td><td><center><em><strong>40.39</strong></em></center></td><td><center><em>63.67</em></center></td><td><center><em>59.12</em></center></td><td><center><em>13.29</em></center></td><td><center><em><strong>45.78</strong></em></center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>43.20</center></td><td><center>44.24</center></td><td><center>38.39</center></td><td><center>62.57</center></td><td><center><strong>59.20</strong></center></td><td><center>15.72</center></td><td><center>39.07</center></td>
</tr>
</tbody>
</table>

## Downstream tasks

                                  
v<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>Llama-2-7b-chat</td><td><center>87.78</center></td><td><center>52.81</center></td><td><center>97.27</center></td><td><center>82.02</center></td><td><center>15.55</center></td><td><center><strong>28.53</strong></center></td><td><center>19.99</center></td><td><center>31.48</center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>97.48</center></td><td><center><strong>65.26</strong></center></td><td><center><strong>98.83</strong></center></td><td><center><strong>87.28</strong></center></td><td><center><strong>27.38</strong></center></td><td><center>10.32</center></td><td><center>27.59</center></td><td><center><strong>40.13</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-2024-10-09</em></td><td><center><em><strong>97.66</strong></em></center></td><td><center><em>62.41</em></center></td><td><center><em>97.97</em></center></td><td><center><em>60.89</em></center></td><td><center><em>27.13</em></center></td><td><center><em>19.39</em></center></td><td><center><em><strong>27.63</strong></em></center></td><td><center><em>39.75</em></center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>97.31</center></td><td><center>60.56</center></td><td><center>-</center></td><td><center>-</center></td><td><center>26.56</center></td><td><center>21.68</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>


<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>Llama-2-7b-chat</td><td><center>32.35</center></td><td><center>54.00</center></td><td><center><strong>60.34</strong></center></td><td><center><strong>75.98</strong></center></td><td><center>32.56</center></td><td><center>31.99</center></td><td><center>74.08</center></td><td><center>72.64</center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>44.52</center></td><td><center>64.75</center></td><td><center>54.96</center></td><td><center>70.20</center></td><td><center><strong>65.50</strong></center></td><td><center><strong>67.79</strong></center></td><td><center>84.44</center></td><td><center>84.76</center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-2024-10-09</em></td><td><center><em><strong>45.71</strong></em></center></td><td><center><em><strong>65.08</strong></em></center></td><td><center><em>59.24</em></center></td><td><center><em>74.25</em></center></td><td><center><em>59.69</em></center></td><td><center><em>57.16</em></center></td><td><center><em><strong>84.66</strong></em></center></td><td><center><em><strong>85.07</strong></em></center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center>35.78</center></td><td><center>59.31</center></td><td><center>-</center></td><td><center>-</center></td><td><center>61.22</center></td><td><center>58.41</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>


## Romanian MT-Bench

<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-2-7b-chat</td><td><center>1.08</center></td><td><center>1.44</center></td><td><center>0.73</center></td><td><center>45/160</center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>3.86</center></td><td><center>4.67</center></td><td><center>3.04</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-2024-10-09</em></td><td><center><em>4.43</em></center></td><td><center><em>4.92</em></center></td><td><center><em>3.94</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center><strong>4.61</strong></center></td><td><center><strong>5.15</strong></center></td><td><center><strong>4.06</strong></center></td><td><center><strong>160/160</strong></center></td>
</tr>
</tbody>
</table>


## RoCulturaBench


<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-2-7b-chat</td><td><center>1.21</center></td><td><center>33/100</center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-2024-05-14</td><td><center>3.77</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-2024-10-09</em></td><td><center><em>4.08</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoLlama2-7b-Instruct-DPO-2024-10-09</td><td><center><strong>4.80</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>





## RoLlama2 Model Family

| Model              | Link  |
|--------------------|:--------:|
|RoLlama2-7b-Base-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14)    |
|RoLlama2-7b-Instruct-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-05-14)    |
|*RoLlama2-7b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09) |
|RoLlama2-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-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:**

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