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---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09
datasets:
- OpenLLM-Ro/ro_dpo_helpsteer
model-index:
- name: OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 4.61
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 4.80
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 43.20
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 44.24
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 38.39
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 62.57
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 59.20
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 15.72
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 39.07
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 97.31
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 60.56
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 0.00
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 0.00
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 26.56
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 21.68
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 0.00
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- name: Average bleu
type: bleu
value: 0.00
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 35.78
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 59.31
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 0.00
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 0.00
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 61.22
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 58.41
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 0.00
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 0.00
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 5.15
- name: Second turn
type: Score
value: 4.06
- 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: 43.36
- name: 3-shot
type: accuracy
value: 44.13
- name: 5-shot
type: accuracy
value: 44.30
- 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: 36.62
- name: 1-shot
type: accuracy
value: 38.04
- name: 3-shot
type: accuracy
value: 39.52
- name: 5-shot
type: accuracy
value: 39.36
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 61.72
- name: 1-shot
type: accuracy
value: 62.04
- name: 3-shot
type: accuracy
value: 63.85
- name: 5-shot
type: accuracy
value: 62.67
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 58.75
- name: 1-shot
type: accuracy
value: 58.29
- name: 3-shot
type: accuracy
value: 59.28
- name: 5-shot
type: accuracy
value: 59.68
- name: 10-shot
type: accuracy
value: 60.01
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 11.14
- name: 3-shot
type: accuracy
value: 17.97
- name: 5-shot
type: accuracy
value: 18.04
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 98.03
- name: 1-shot
type: macro-f1
value: 95.96
- name: 3-shot
type: macro-f1
value: 97.33
- name: 5-shot
type: macro-f1
value: 97.90
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 60.67
- name: 1-shot
type: macro-f1
value: 51.37
- name: 3-shot
type: macro-f1
value: 62.49
- name: 5-shot
type: macro-f1
value: 67.70
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 19.83
- name: 1-shot
type: bleu
value: 29.04
- name: 3-shot
type: bleu
value: 28.90
- name: 5-shot
type: bleu
value: 28.47
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 1.74
- name: 1-shot
type: bleu
value: 15.28
- name: 3-shot
type: bleu
value: 34.13
- name: 5-shot
type: bleu
value: 35.56
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 26.97
- name: 1-shot
type: exact_match
value: 36.30
- name: 3-shot
type: exact_match
value: 40.25
- name: 5-shot
type: exact_match
value: 39.58
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 52.90
- name: 1-shot
type: f1
value: 60.05
- name: 3-shot
type: f1
value: 62.08
- name: 5-shot
type: f1
value: 62.22
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 62.07
- name: 3-shot
type: spearman
value: 59.47
- name: 5-shot
type: spearman
value: 62.12
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 60.60
- name: 3-shot
type: pearson
value: 56.44
- name: 5-shot
type: pearson
value: 58.18
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned 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-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09)
- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer)
### 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-DPO-2024-10-09")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09")
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>RoLlama2-7b-Instruct-2024-10-09</td><td><center>44.50</center></td><td><center><strong>44.73</strong></center></td><td><center><strong>40.39</strong></center></td><td><center>63.67</center></td><td><center>59.12</center></td><td><center>13.29</center></td><td><center><strong>45.78</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>43.20</em></center></td><td><center><em>44.24</em></center></td><td><center><em>38.39</em></center></td><td><center><em>62.57</em></center></td><td><center><em><strong>59.20</strong></em></center></td><td><center><em>15.72</em></center></td><td><center><em>39.07</em></center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>Llama-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>RoLlama2-7b-Instruct-2024-10-09</td><td><center><strong>97.66</strong></center></td><td><center>62.41</center></td><td><center>97.97</center></td><td><center>60.89</center></td><td><center>27.13</center></td><td><center>19.39</center></td><td><center><strong>27.63</strong></center></td><td><center>39.75</center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>97.31</em></center></td><td><center><em>60.56</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>26.56</em></center></td><td><center><em>21.68</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>Llama-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>RoLlama2-7b-Instruct-2024-10-09</td><td><center><strong>45.71</strong></center></td><td><center><strong>65.08</strong></center></td><td><center>59.24</center></td><td><center>74.25</center></td><td><center>59.69</center></td><td><center>57.16</center></td><td><center><strong>84.66</strong></center></td><td><center><strong>85.07</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>35.78</em></center></td><td><center><em>59.31</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>61.22</em></center></td><td><center><em>58.41</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>
## Romanian MT-Bench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-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>RoLlama2-7b-Instruct-2024-10-09</td><td><center>4.43</center></td><td><center>4.92</center></td><td><center>3.94</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>4.61</strong></em></center></td><td><center><em><strong>5.15</strong></em></center></td><td><center><em><strong>4.06</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-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>RoLlama2-7b-Instruct-2024-10-09</td><td><center>4.08</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoLlama2-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>4.80</strong></em></center></td><td><center><em><strong>100/100</strong></em></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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