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---
license: cc-by-nc-4.0
language:
- ro
base_model:
- mistralai/Mistral-7B-v0.1
datasets:
- OpenLLM-Ro/ro_sft_alpaca
- OpenLLM-Ro/ro_sft_alpaca_gpt4
- OpenLLM-Ro/ro_sft_dolly
- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
- OpenLLM-Ro/ro_sft_norobots
- OpenLLM-Ro/ro_sft_orca
- OpenLLM-Ro/ro_sft_camel
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
model-index:
- name: OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 5.29
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 3.99
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 52.91
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 52.27
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 49.33
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 70.03
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 62.88
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 32.42
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 50.51
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 95.56
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 67.83
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 99.00
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 87.57
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 28.28
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 6.10
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 27.70
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- name: Average bleu
type: bleu
value: 40.36
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 41.09
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 63.21
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 47.56
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 62.69
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 78.47
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 77.24
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 87.28
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 87.88
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 5.86
- name: Second turn
type: Score
value: 4.72
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 52.10
- name: 1-shot
type: accuracy
value: 49.87
- name: 3-shot
type: accuracy
value: 51.76
- name: 5-shot
type: accuracy
value: 52.10
- name: 10-shot
type: accuracy
value: 53.64
- name: 25-shot
type: accuracy
value: 54.16
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 43.86
- name: 1-shot
type: accuracy
value: 47.70
- name: 3-shot
type: accuracy
value: 52.48
- name: 5-shot
type: accuracy
value: 53.29
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 68.27
- name: 1-shot
type: accuracy
value: 69.30
- name: 3-shot
type: accuracy
value: 70.56
- name: 5-shot
type: accuracy
value: 71.98
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 63.03
- name: 1-shot
type: accuracy
value: 62.39
- name: 3-shot
type: accuracy
value: 62.54
- name: 5-shot
type: accuracy
value: 62.95
- name: 10-shot
type: accuracy
value: 63.47
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 25.47
- name: 3-shot
type: accuracy
value: 33.06
- name: 5-shot
type: accuracy
value: 38.74
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 88.87
- name: 1-shot
type: macro-f1
value: 97.40
- name: 3-shot
type: macro-f1
value: 98.13
- name: 5-shot
type: macro-f1
value: 97.83
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 66.79
- name: 1-shot
type: macro-f1
value: 67.00
- name: 3-shot
type: macro-f1
value: 67.63
- name: 5-shot
type: macro-f1
value: 69.88
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 23.84
- name: 1-shot
type: bleu
value: 29.49
- name: 3-shot
type: bleu
value: 30.29
- name: 5-shot
type: bleu
value: 29.49
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 3.14
- name: 1-shot
type: bleu
value: 3.18
- name: 3-shot
type: bleu
value: 6.72
- name: 5-shot
type: bleu
value: 11.35
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 35.21
- name: 1-shot
type: exact_match
value: 40.76
- name: 3-shot
type: exact_match
value: 43.70
- name: 5-shot
type: exact_match
value: 44.71
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 57.74
- name: 1-shot
type: f1
value: 61.96
- name: 3-shot
type: f1
value: 65.55
- name: 5-shot
type: f1
value: 67.59
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 77.38
- name: 3-shot
type: spearman
value: 79.28
- name: 5-shot
type: spearman
value: 78.75
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 77.10
- name: 3-shot
type: pearson
value: 77.70
- name: 5-shot
type: pearson
value: 76.91
---
# Model Card for Model ID
This model points/is identical to [RoMistral-7b-Instruct-2024-10-09](https://huggingface.co./OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09).
<!-- Provide a quick summary of what the model is/does. -->
RoMistral is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 7B model**. Links to other models can be found at the bottom of this page.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1)
- **Trained using:** [RoAlpaca](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co./datasets/OpenLLM-Ro/ro_sft_ultrachat)
<!-- - **Finetuned from model [optional]:** [More Information Needed] -->
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266
## Intended Use
### Intended Use Cases
RoMistral is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoMistral-7b-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoMistral-7b-Instruct")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
## Academic Benchmarks
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>Mistral-7B-Instruct-v0.2</td><td><center>47.40</center></td><td><center>46.29</center></td><td><center>47.00</center></td><td><center>58.78</center></td><td><center>54.27</center></td><td><center>13.47</center></td><td><center><strong>64.59</strong></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-2024-05-17</td><td><center>52.54</center></td><td><center>50.41</center></td><td><center><strong>51.61</strong></center></td><td><center>66.48</center></td><td><center>60.27</center></td><td><center><strong>34.19</strong></center></td><td><center>52.30</center></td>
</tr>
<tr>
<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em><strong>52.91</strong></em></center></td><td><center><em><strong>52.27</strong></em></center></td><td><center><em>49.33</em></center></td><td><center><em><strong>70.03</strong></em></center></td><td><center><em><strong>62.88</strong></em></center></td><td><center><em>32.42</em></center></td><td><center><em>50.51</em></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>51.95</center></td><td><center>50.73</center></td><td><center>47.88</center></td><td><center>68.41</center></td><td><center>62.27</center></td><td><center>32.27</center></td><td><center>50.12</center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>Mistral-7B-Instruct-v0.2</td><td><center>96.97</center></td><td><center>56.66</center></td><td><center>98.83</center></td><td><center>87.32</center></td><td><center>18.60</center></td><td><center><strong>33.99</strong></center></td><td><center>26.19</center></td><td><center>39.88</center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-2024-05-17</td><td><center><strong>97.36</strong></center></td><td><center>67.55</center></td><td><center>98.80</center></td><td><center><strong>88.28</strong></center></td><td><center>27.93</center></td><td><center>13.21</center></td><td><center><strong>28.72</strong></center></td><td><center><strong>40.86</strong></center></td>
</tr>
<tr>
<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>95.56</em></center></td><td><center><em><strong>67.83</strong></em></center></td><td><center><em><strong>99.00</strong></em></center></td><td><center><em>87.57</em></center></td><td><center><em><strong>28.28</strong></em></center></td><td><center><em>6.10</em></center></td><td><center><em>27.70</em></center></td><td><center><em>40.36</em></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>82.13</center></td><td><center>65.24</center></td><td><center>-</center></td><td><center>-</center></td><td><center>26.25</center></td><td><center>6.09</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>Mistral-7B-Instruct-v0.2</td><td><center>27.92</center></td><td><center>50.71</center></td><td><center><strong>65.46</strong></center></td><td><center><strong>79.73</strong></center></td><td><center>62.62</center></td><td><center>60.86</center></td><td><center>84.92</center></td><td><center>85.44</center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-2024-05-17</td><td><center><strong>43.66</strong></center></td><td><center><strong>63.70</strong></center></td><td><center>55.04</center></td><td><center>72.31</center></td><td><center>77.43</center></td><td><center><strong>78.43</strong></center></td><td><center>87.25</center></td><td><center>87.79</center></td>
</tr>
<tr>
<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>41.09</em></center></td><td><center><em>63.21</em></center></td><td><center><em>47.56</em></center></td><td><center><em>62.69</em></center></td><td><center><em><strong>78.47</strong></em></center></td><td><center><em>77.24</em></center></td><td><center><em><strong>87.28</strong></em></center></td><td><center><em><strong>87.88</strong></em></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center>23.40</center></td><td><center>45.80</center></td><td><center>-</center></td><td><center>-</center></td><td><center>77.33</center></td><td><center>76.60</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
## MT-Bench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Mistral-7B-Instruct-v0.2</td><td><center>5.03</center></td><td><center>5.05</center></td><td><center>5.00</center></td><td><center>154/160</center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-2024-05-17</td><td><center>4.99</center></td><td><center>5.46</center></td><td><center>4.53</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>5.29</em></center></td><td><center><em>5.86</em></center></td><td><center><em>4.72</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center><strong>5.88</strong></center></td><td><center><strong>6.44</strong></center></td><td><center><strong>5.33</strong></center></td><td><center><strong>160/160</strong></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Mistral-7B-Instruct-v0.2</td><td><center>3.68</center></td><td><center>97/100</center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-2024-05-17</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoMistral-7b-Instruct-2024-10-09</em></td><td><center><em>3.99</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoMistral-7b-Instruct-DPO-2024-10-09</td><td><center><strong>4.72</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>
## RoMistral Model Family
| Model | Link |
|--------------------|:--------:|
|RoMistral-7b-Instruct-2024-05-17| [link](https://huggingface.co./OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17) |
|*RoMistral-7b-Instruct-2024-10-09*| [link](https://huggingface.co./OpenLLM-Ro/RoMistral-7b-Instruct-2024-10-09) |
|RoMistral-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co./OpenLLM-Ro/RoMistral-7b-Instruct-DPO-2024-10-09) |
## Citation
```
@misc{masala2024vorbecstiromanecsterecipetrain,
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2406.18266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.18266},
}
```
<!-- **APA:**
[More Information Needed] --> |