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RoLlama3.1-8b-Instruct-2024-10-09
OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09
RoLlama3.1-8b-Instruct-2024-10-09 is a machine learning model from OpenLLM-Ro. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
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At a glance
- License
- cc-by-nc-4.0
- Model type
- llama
- Access
- Public
- Created
- Oct 1, 2024
- Updated
- Jun 4, 2026
- SHA
- 6250fb8a
Base models
Model card
From the Hugging Face model README
Model Card for Model ID
Built with Meta Llama 3.1
<!-- Provide a quick summary of what the model is/does. -->RoLlama3.1 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
- Language(s): Romanian
- License: cc-by-nc-4.0
- Finetuned from model: Meta-Llama-3.1-8B-Instruct
- Trained using: RoAlpaca, RoAlpacaGPT4, RoDolly, RoSelfInstruct, RoNoRobots, RoOrca, RoCamel, RoOpenAssistant, RoUltraChat
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
RoLlama3.1 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.
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09")
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-3.1-8B-Instruct</td><td><center>49.87</center></td><td><center>42.86</center></td><td><center>53.73</center></td><td><center>59.71</center></td><td><center>56.82</center></td><td><center>35.56</center></td><td><center><strong>50.54</strong></center></td> </tr> <tr> <td><em>RoLlama3.1-8b-Instruct-2024-10-09</em></td><td><center><em><strong>53.03</strong></em></center></td><td><center><em><strong>47.69</strong></em></center></td><td><center><em>54.57</em></center></td><td><center><em>65.84</em></center></td><td><center><em><strong>59.94</strong></em></center></td><td><center><em><strong>44.30</strong></em></center></td><td><center><em>45.82</em></center></td> </tr> <tr> <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>52.74</center></td><td><center>44.84</center></td><td><center><strong>55.06</strong></center></td><td><center><strong>65.87</strong></center></td><td><center>58.67</center></td><td><center>44.17</center></td><td><center>47.82</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-3.1-8B-Instruct</td><td><center>95.74</center></td><td><center>59.49</center></td><td><center><strong>98.57</strong></center></td><td><center>82.41</center></td><td><center>19.01</center></td><td><center><strong>27.77</strong></center></td><td><center><strong>29.02</strong></center></td><td><center>39.80</center></td> </tr> <tr> <td><em>RoLlama3.1-8b-Instruct-2024-10-09</em></td><td><center><em>94.56</em></center></td><td><center><em><strong>60.10</strong></em></center></td><td><center><em>95.12</em></center></td><td><center><em><strong>87.53</strong></em></center></td><td><center><em><strong>21.88</strong></em></center></td><td><center><em>23.99</em></center></td><td><center><em>28.27</em></center></td><td><center><em><strong>40.44</strong></em></center></td> </tr> <tr> <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center><strong>96.10</strong></center></td><td><center>55.37</center></td><td><center>-</center></td><td><center>-</center></td><td><center>21.29</center></td><td><center>21.86</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-3.1-8B-Instruct</td><td><center><strong>44.96</strong></center></td><td><center><strong>64.45</strong></center></td><td><center><strong>69.50</strong></center></td><td><center><strong>84.31</strong></center></td><td><center>72.11</center></td><td><center>71.64</center></td><td><center>84.59</center></td><td><center>84.96</center></td> </tr> <tr> <td><em>RoLlama3.1-8b-Instruct-2024-10-09</em></td><td><center><em>13.59</em></center></td><td><center><em>23.56</em></center></td><td><center><em>49.41</em></center></td><td><center><em>62.93</em></center></td><td><center><em>75.89</em></center></td><td><center><em>76.00</em></center></td><td><center><em><strong>86.86</strong></em></center></td><td><center><em><strong>87.05</strong></em></center></td> </tr> <tr> <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center>21.58</center></td><td><center>36.54</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>78.01</strong></center></td><td><center><strong>77.98</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-3.1-8B-Instruct</td><td><center>5.69</center></td><td><center>5.85</center></td><td><center>5.53</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td><em>RoLlama3.1-8b-Instruct-2024-10-09</em></td><td><center><em>5.42</em></center></td><td><center><em>5.95</em></center></td><td><center><em>4.89</em></center></td><td><center><em><strong>160/160</strong></em></center></td> </tr> <tr> <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center><strong>6.21</strong></center></td><td><center><strong>6.74</strong></center></td><td><center><strong>5.69</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-3.1-8B-Instruct</td><td><center>3.54</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td><em>RoLlama3.1-8b-Instruct-2024-10-09</em></td><td><center><em>3.55</em></center></td><td><center><em><strong>100/100</strong></em></center></td> </tr> <tr> <td>RoLlama3.1-8b-Instruct-DPO-2024-10-09</td><td><center><strong>4.42</strong></center></td><td><center><strong>100/100</strong></center></td> </tr> </tbody> </table>RoLlama3.1 Model Family
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"
}
- Type
- llama
- License
- cc-by-nc-4.0
- Languages
- ro
- Created
- Oct 1, 2024
- Updated
- Jun 4, 2026