RoLlama2-7b-Base-2024-05-14

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RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the foundational 7B model. Links to other models can be found at the bottom of this page.

Model Details

Model Description

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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

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• Language(s): Romanian
• License: Llama2 Community License Agreement
• Continual pretrained from model: Llama-2-7b
• Trained using: CulturaX

Model Sources

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• Repository: https://github.com/OpenLLM-Ro/llama-recipes
• 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

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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/RoLlama2-7b-Base-2024-05-14")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14")

input_text = "Mihai Eminescu a fost "
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**input_ids, max_new_tokens=100)
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</td><td><center>37.04</center></td><td><center>36.05</center></td><td><center><strong>33.66</strong></center></td><td><center>57.56</center></td><td><center>48.00</center></td><td><center><strong>4.75</strong></center></td><td><center>42.22</center></td> </tr> <tr> <td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em><strong>38.03</strong></em></center></td><td><center><em><strong>37.95</strong></em></center></td><td><center><em>27.22</em></center></td><td><center><em><strong>59.29</strong></em></center></td><td><center><em><strong>57.22</strong></em></center></td><td><center><em>2.53</em></center></td><td><center><em><strong>44.00</strong></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</td><td><center><strong>93.19</strong></center></td><td><center>54.11</center></td><td><center>98.43</center></td><td><center>87.22</center></td><td><center><strong>14.90</strong></center></td><td><center><strong>26.61</strong></center></td><td><center>24.95</center></td><td><center>39.09</center></td> </tr> <tr> <td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>83.25</em></center></td><td><center><em><strong>61.04</strong></em></center></td><td><center><em><strong>98.97</strong></em></center></td><td><center><em><strong>87.72</strong></em></center></td><td><center><em>10.01</em></center></td><td><center><em>13.03</em></center></td><td><center><em><strong>27.85</strong></em></center></td><td><center><em><strong>39.30</strong></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</td><td><center><strong>38.91</strong></center></td><td><center><strong>56.82</strong></center></td><td><center>65.46</center></td><td><center>79.42</center></td><td><center><strong>9.08</strong></center></td><td><center><strong>9.07</strong></center></td><td><center><strong>79.93</strong></center></td><td><center><strong>81.08</strong></center></td> </tr> <tr> <td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>30.15</em></center></td><td><center><em>47.03</em></center></td><td><center><em><strong>67.06</strong></em></center></td><td><center><em><strong>79.96</strong></em></center></td><td><center><em>7.89</em></center></td><td><center><em>7.98</em></center></td><td><center><em>71.75</em></center></td><td><center><em>71.99</em></center></td> </tr> </tbody> </table>

RoLlama2 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"
}

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