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初始化项目，由ModelHub XC社区提供模型 Model: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28 Source: Original Platform
2026-07-28 04:45:13 +08:00
---
license: cc-by-nc-4.0
language:
- ro
base_model:
- meta-llama/Meta-Llama-3-8B
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
model-index:
- name: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 5.15
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 3.71
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 50.56
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 44.70
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 52.19
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 67.23
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 57.69
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 30.23
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 51.34
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 97.52
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 67.41
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 94.15
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 87.13
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 24.01
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 27.36
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 26.53
- 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: 39.43
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 59.50
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 44.45
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 59.76
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 77.20
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 77.87
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 85.80
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 86.05
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 6.03
- name: Second turn
type: Score
value: 4.28
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 41.90
- name: 1-shot
type: accuracy
value: 44.30
- name: 3-shot
type: accuracy
value: 44.56
- name: 5-shot
type: accuracy
value: 45.50
- name: 10-shot
type: accuracy
value: 46.10
- name: 25-shot
type: accuracy
value: 45.84
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 50.85
- name: 1-shot
type: accuracy
value: 51.24
- name: 3-shot
type: accuracy
value: 53.30
- name: 5-shot
type: accuracy
value: 53.39
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 65.19
- name: 1-shot
type: accuracy
value: 66.54
- name: 3-shot
type: accuracy
value: 67.88
- name: 5-shot
type: accuracy
value: 69.30
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 56.12
- name: 1-shot
type: accuracy
value: 57.37
- name: 3-shot
type: accuracy
value: 57.92
- name: 5-shot
type: accuracy
value: 58.18
- name: 10-shot
type: accuracy
value: 58.85
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 29.42
- name: 3-shot
type: accuracy
value: 30.02
- name: 5-shot
type: accuracy
value: 31.24
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 97.43
- name: 1-shot
type: macro-f1
value: 96.60
- name: 3-shot
type: macro-f1
value: 97.90
- 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: 63.77
- name: 1-shot
type: macro-f1
value: 68.91
- name: 3-shot
type: macro-f1
value: 66.36
- name: 5-shot
type: macro-f1
value: 70.61
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 6.92
- name: 1-shot
type: bleu
value: 29.33
- name: 3-shot
type: bleu
value: 29.79
- name: 5-shot
type: bleu
value: 30.02
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 4.50
- name: 1-shot
type: bleu
value: 30.30
- name: 3-shot
type: bleu
value: 36.96
- name: 5-shot
type: bleu
value: 37.70
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 4.45
- name: 1-shot
type: exact_match
value: 48.24
- name: 3-shot
type: exact_match
value: 52.03
- name: 5-shot
type: exact_match
value: 53.03
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 26.08
- name: 1-shot
type: f1
value: 68.40
- name: 3-shot
type: f1
value: 71.92
- name: 5-shot
type: f1
value: 71.60
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 77.76
- name: 3-shot
type: spearman
value: 76.72
- name: 5-shot
type: spearman
value: 77.12
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 77.83
- name: 3-shot
type: pearson
value: 77.64
- name: 5-shot
type: pearson
value: 78.13
---
# Model Card for Model ID
*Built with Meta Llama 3*
<!-- Provide a quick summary of what the model is/does. -->
RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 8B 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:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **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)
### 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
RoLlama3 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/RoLlama3-8b-Instruct-2024-06-28")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
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, 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>Llama-3-8B-Instruct</td><td><center>50.62</center></td><td><center>43.69</center></td><td><center>52.04</center></td><td><center>59.33</center></td><td><center>53.19</center></td><td><center><strong>43.87</strong></center></td><td><center><strong>51.59</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>50.56</em></center></td><td><center><em>44.70</em></center></td><td><center><em>52.19</em></center></td><td><center><em><strong>67.23</strong></em></center></td><td><center><em>57.69</em></center></td><td><center><em>30.23</em></center></td><td><center><em>51.34</em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center><strong>52.21</strong></center></td><td><center><strong>47.94</strong></center></td><td><center><strong>53.50</strong></center></td><td><center>66.06</center></td><td><center><strong>59.72</strong></center></td><td><center>40.16</center></td><td><center>45.90</center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>49.96</center></td><td><center>46.29</center></td><td><center>53.29</center></td><td><center>65.57</center></td><td><center>58.15</center></td><td><center>34.77</center></td><td><center>41.70</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-3-8B-Instruct</td><td><center>95.88</center></td><td><center>56.21</center></td><td><center><strong>98.53</strong></center></td><td><center>86.19</center></td><td><center>18.88</center></td><td><center><strong>30.98</strong></center></td><td><center><strong>28.02</strong></center></td><td><center>40.28</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em><strong>97.52</strong></em></center></td><td><center><em><strong>67.41</strong></em></center></td><td><center><em>94.15</em></center></td><td><center><em>87.13</em></center></td><td><center><em><strong>24.01</strong></em></center></td><td><center><em>27.36</em></center></td><td><center><em>26.53</em></center></td><td><center><em>40.36</em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>95.58</center></td><td><center>61.20</center></td><td><center>96.46</center></td><td><center><strong>87.26</strong></center></td><td><center>22.92</center></td><td><center>24.28</center></td><td><center>27.31</center></td><td><center><strong>40.52</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>97.48</center></td><td><center>54.00</center></td><td><center>-</center></td><td><center>-</center></td><td><center>22.09</center></td><td><center>23.00</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-3-8B-Instruct</td><td><center><strong>39.47</strong></center></td><td><center>58.67</center></td><td><center><strong>67.65</strong></center></td><td><center><strong>82.77</strong></center></td><td><center>73.04</center></td><td><center>72.36</center></td><td><center>83.49</center></td><td><center>84.06</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>39.43</em></center></td><td><center><em><strong>59.50</strong></em></center></td><td><center><em>44.45</em></center></td><td><center><em>59.76</em></center></td><td><center><em>77.20</em></center></td><td><center><em>77.87</em></center></td><td><center><em>85.80</em></center></td><td><center><em>86.05</em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>18.89</center></td><td><center>31.79</center></td><td><center>50.84</center></td><td><center>65.18</center></td><td><center>77.60</center></td><td><center>76.86</center></td><td><center><strong>86.70</strong></center></td><td><center><strong>87.09</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>26.05</center></td><td><center>42.77</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>79.64</strong></center></td><td><center><strong>79.52</strong></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>Llama-3-8B-Instruct</td><td><center><strong>5.96</strong></center></td><td><center>6.16</center></td><td><center><strong>5.76</strong></center></td><td><center>158/160</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>5.15</em></center></td><td><center><em>6.03</em></center></td><td><center><em>4.28</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>5.38</center></td><td><center>6.09</center></td><td><center>4.67</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>5.87</center></td><td><center><strong>6.22</strong></center></td><td><center>5.49</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-3-8B-Instruct</td><td><center><strong>4.62</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>3.71</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>3.81</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>4.40</center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>
## RoLlama3 Model Family
| Model | Link |
|--------------------|:--------:|
|*RoLlama3-8b-Instruct-2024-06-28*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) |
|RoLlama3-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09) |
|RoLlama3-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-DPO-2024-10-09) |
## Citation
```
@inproceedings{masala-etal-2024-vorbesti,
title = "``Vorbe\c{s}ti Rom{\^a}ne\c{s}te?'' A Recipe to Train Powerful {R}omanian {LLM}s with {E}nglish Instructions",
author = "Masala, Mihai and Ilie-Ablachim, Denis and Dima, Alexandru and Corlatescu, Dragos Georgian and Zavelca, Miruna-Andreea and Olaru, Ovio and Terian, Simina-Maria and Terian, Andrei and Leordeanu, Marius and Velicu, Horia and Popescu, Marius and Dascalu, Mihai and Rebedea, Traian",
editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.681/",
doi = "10.18653/v1/2024.findings-emnlp.681",
pages = "11632--11647"
}
```
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