README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

This model is a Bits&Bytes 4 bits quantization of the https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct model.

The main advantages of this model are :
- it runs on a GPU with 6GB of free ram. (So usually a user-grade gpu with 8 Gb VRAM, versus the standard model which needs 48+GB).
- it is 2-3 times faster in inference time/token

The main drawback is that is less accurate than the full(original) model, although is up to you to decide if the compromise is a good fit for your use-case.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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-3-8B-Instruct
Trained using: RoAlpaca, RoAlpacaGPT4, RoDolly, RoSelfInstruct, RoNoRobots, RoOrca, RoCamel, RoOpenAssistant, RoUltraChat


### Model Sources [optional]

Repository: https://github.com/OpenLLM-Ro/LLaMA-Factory
Paper: https://arxiv.org/abs/2406.18266

ntended 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
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/RoLlama3-8b-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct")

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>RoLlama-3-8B-Instruct-4Bit</td><td><center>NA</center></td><td><center>40.38</center></td><td><center>NA</center></td><td><center>NA</center></td><td><center>NA</center></td><td><center><strong>NA</strong></center></td><td><center><strong>50.93</strong></center></td>
</tr>
<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>RoLlama3-8b-Instruct-2024-06-28</td><td><center>50.56</center></td><td><center>44.70</center></td><td><center>52.19</center></td><td><center><strong>67.23</strong></center></td><td><center>57.69</center></td><td><center>30.23</center></td><td><center>51.34</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em><strong>52.21</strong></em></center></td><td><center><em><strong>47.94</strong></em></center></td><td><center><em><strong>53.50</strong></em></center></td><td><center><em>66.06</em></center></td><td><center><em><strong>59.72</strong></em></center></td><td><center><em>40.16</em></center></td><td><center><em>45.90</em></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>RoLlama3-8b-Instruct-2024-06-28</td><td><center><strong>97.52</strong></center></td><td><center><strong>67.41</strong></center></td><td><center>94.15</center></td><td><center>87.13</center></td><td><center><strong>24.01</strong></center></td><td><center>27.36</center></td><td><center>26.53</center></td><td><center>40.36</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>95.58</em></center></td><td><center><em>61.20</em></center></td><td><center><em>96.46</em></center></td><td><center><em><strong>87.26</strong></em></center></td><td><center><em>22.92</em></center></td><td><center><em>24.28</em></center></td><td><center><em>27.31</em></center></td><td><center><em><strong>40.52</strong></em></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>RoLlama-3-8B-Instruct-4Bit - F5 Scores</td><td><center><strong>NA</strong></center></td><td><center>NA</center></td><td><center><strong>NA</strong></center></td><td><center><strong>NA</strong></center></td><td><center>68.58</center></td><td><center>NA</center></td><td><center>70.57</center></td><td><center>NA</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>RoLlama3-8b-Instruct-2024-06-28</td><td><center>39.43</center></td><td><center><strong>59.50</strong></center></td><td><center>44.45</center></td><td><center>59.76</center></td><td><center>77.20</center></td><td><center>77.87</center></td><td><center>85.80</center></td><td><center>86.05</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>18.89</em></center></td><td><center><em>31.79</em></center></td><td><center><em>50.84</em></center></td><td><center><em>65.18</em></center></td><td><center><em>77.60</em></center></td><td><center><em>76.86</em></center></td><td><center><em><strong>86.70</strong></em></center></td><td><center><em><strong>87.09</strong></em></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>



#### Hardware

Nvidia RTX 4090 16GB, Laptop Version

#### Software

[More Information Needed]

## 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 

```
@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}, 
}