README.md

---
license: llama2
language:
- ro
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14
  results:
  - task:
      type: text-generation
    dataset:
      name: Romanian_Academic_Benchmarks
      type: Romanian_Academic_Benchmarks
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 38.03
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_arc_challenge
      type: OpenLLM-Ro/ro_arc_challenge
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 37.95
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_mmlu
      type: OpenLLM-Ro/ro_mmlu
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 27.22
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_winogrande
      type: OpenLLM-Ro/ro_winogrande
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 59.29
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_hellaswag
      type: OpenLLM-Ro/ro_hellaswag
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 57.22
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_gsm8k
      type: OpenLLM-Ro/ro_gsm8k
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 2.53
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_truthfulqa
      type: OpenLLM-Ro/ro_truthfulqa
    metrics:
    - name: Average accuracy
      type: accuracy
      value: 44
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary
      type: LaRoSeDa_binary
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 83.25
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass
      type: LaRoSeDa_multiclass
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 61.04
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary_finetuned
      type: LaRoSeDa_binary_finetuned
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 98.97
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass_finetuned
      type: LaRoSeDa_multiclass_finetuned
    metrics:
    - name: Average macro-f1
      type: macro-f1
      value: 87.72
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO
      type: WMT_EN-RO
    metrics:
    - name: Average bleu
      type: bleu
      value: 10.01
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN
      type: WMT_RO-EN
    metrics:
    - name: Average bleu
      type: bleu
      value: 13.03
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO_finetuned
      type: WMT_EN-RO_finetuned
    metrics:
    - name: Average bleu
      type: bleu
      value: 27.85
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN_finetuned
      type: WMT_RO-EN_finetuned
    metrics:
    - name: Average bleu
      type: bleu
      value: 39.3
  - task:
      type: text-generation
    dataset:
      name: XQuAD
      type: XQuAD
    metrics:
    - name: Average exact_match
      type: exact_match
      value: 30.15
  - task:
      type: text-generation
    dataset:
      name: XQuAD
      type: XQuAD
    metrics:
    - name: Average f1
      type: f1
      value: 47.03
  - task:
      type: text-generation
    dataset:
      name: XQuAD_finetuned
      type: XQuAD_finetuned
    metrics:
    - name: Average exact_match
      type: exact_match
      value: 67.06
  - task:
      type: text-generation
    dataset:
      name: XQuAD_finetuned
      type: XQuAD_finetuned
    metrics:
    - name: Average f1
      type: f1
      value: 79.96
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: Average spearman
      type: spearman
      value: 7.89
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: Average pearson
      type: pearson
      value: 7.98
  - task:
      type: text-generation
    dataset:
      name: STS_finetuned
      type: STS_finetuned
    metrics:
    - name: Average spearman
      type: spearman
      value: 71.75
  - task:
      type: text-generation
    dataset:
      name: STS_finetuned
      type: STS_finetuned
    metrics:
    - name: Average pearson
      type: pearson
      value: 71.99
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_arc_challenge
      type: OpenLLM-Ro/ro_arc_challenge
    metrics:
    - name: 0-shot
      type: accuracy
      value: 35.56
    - name: 1-shot
      type: accuracy
      value: 36.42
    - name: 3-shot
      type: accuracy
      value: 38.56
    - name: 5-shot
      type: accuracy
      value: 38.39
    - name: 10-shot
      type: accuracy
      value: 39.07
    - name: 25-shot
      type: accuracy
      value: 39.67
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_mmlu
      type: OpenLLM-Ro/ro_mmlu
    metrics:
    - name: 0-shot
      type: accuracy
      value: 25.82
    - name: 1-shot
      type: accuracy
      value: 25.48
    - name: 3-shot
      type: accuracy
      value: 27.61
    - name: 5-shot
      type: accuracy
      value: 29.96
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_winogrande
      type: OpenLLM-Ro/ro_winogrande
    metrics:
    - name: 0-shot
      type: accuracy
      value: 58.72
    - name: 1-shot
      type: accuracy
      value: 58.88
    - name: 3-shot
      type: accuracy
      value: 60.38
    - name: 5-shot
      type: accuracy
      value: 59.19
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_hellaswag
      type: OpenLLM-Ro/ro_hellaswag
    metrics:
    - name: 0-shot
      type: accuracy
      value: 55.85
    - name: 1-shot
      type: accuracy
      value: 57.06
    - name: 3-shot
      type: accuracy
      value: 57.52
    - name: 5-shot
      type: accuracy
      value: 57.89
    - name: 10-shot
      type: accuracy
      value: 57.79
  - task:
      type: text-generation
    dataset:
      name: OpenLLM-Ro/ro_gsm8k
      type: OpenLLM-Ro/ro_gsm8k
    metrics:
    - name: 0-shot
      type: accuracy
      value: 0
    - name: 1-shot
      type: accuracy
      value: 2.96
    - name: 3-shot
      type: accuracy
      value: 4.62
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_binary
      type: LaRoSeDa_binary
    metrics:
    - name: 0-shot
      type: macro-f1
      value: 42.78
    - name: 1-shot
      type: macro-f1
      value: 98
    - name: 3-shot
      type: macro-f1
      value: 95.13
    - name: 5-shot
      type: macro-f1
      value: 97.07
  - task:
      type: text-generation
    dataset:
      name: LaRoSeDa_multiclass
      type: LaRoSeDa_multiclass
    metrics:
    - name: 0-shot
      type: macro-f1
      value: 46.41
    - name: 1-shot
      type: macro-f1
      value: 67.36
    - name: 3-shot
      type: macro-f1
      value: 65.16
    - name: 5-shot
      type: macro-f1
      value: 65.23
  - task:
      type: text-generation
    dataset:
      name: WMT_EN-RO
      type: WMT_EN-RO
    metrics:
    - name: 0-shot
      type: bleu
      value: 4.45
    - name: 1-shot
      type: bleu
      value: 8.61
    - name: 3-shot
      type: bleu
      value: 12.25
    - name: 5-shot
      type: bleu
      value: 14.73
  - task:
      type: text-generation
    dataset:
      name: WMT_RO-EN
      type: WMT_RO-EN
    metrics:
    - name: 0-shot
      type: bleu
      value: 1.29
    - name: 1-shot
      type: bleu
      value: 10.78
    - name: 3-shot
      type: bleu
      value: 16.82
    - name: 5-shot
      type: bleu
      value: 23.24
  - task:
      type: text-generation
    dataset:
      name: XQuAD_EM
      type: XQuAD_EM
    metrics:
    - name: 0-shot
      type: exact_match
      value: 5.29
    - name: 1-shot
      type: exact_match
      value: 33.95
    - name: 3-shot
      type: exact_match
      value: 39.24
    - name: 5-shot
      type: exact_match
      value: 42.1
  - task:
      type: text-generation
    dataset:
      name: XQuAD_F1
      type: XQuAD_F1
    metrics:
    - name: 0-shot
      type: f1
      value: 16.17
    - name: 1-shot
      type: f1
      value: 51.84
    - name: 3-shot
      type: f1
      value: 58.82
    - name: 5-shot
      type: f1
      value: 61.29
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: 0-shot
      type: spearman
      value: -1.74
    - name: 1-shot
      type: spearman
      value: 15.47
    - name: 3-shot
      type: spearman
      value: 9.93
  - task:
      type: text-generation
    dataset:
      name: STS
      type: STS
    metrics:
    - name: 0-shot
      type: pearson
      value: -1.4
    - name: 1-shot
      type: pearson
      value: 15
    - name: 3-shot
      type: pearson
      value: 10.33
datasets:
- uonlp/CulturaX
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model points/is identical to [RoLlama2-7b-Base-2024-05-14](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14).

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

<!-- 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:** Llama2 Community License Agreement
- **Continual pretrained from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Trained using:** [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX)


### Model Sources

<!-- Provide the basic links for the model. -->

- **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

<!-- 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-Base")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base")

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

| 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:**

[More Information Needed] -->