Model Card for Model ID

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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

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.

Model Sources

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

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-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

Model
Average
ARC
MMLU
Winogrande
Hellaswag
GSM8k
TruthfulQA
Llama-3-8B-Instruct
50.62
43.69
52.04
59.33
53.19
43.87
51.59
RoLlama3-8b-Instruct-2024-06-28
50.56
44.70
52.19
67.23
57.69
30.23
51.34
RoLlama3-8b-Instruct-2024-10-09
52.21
47.94
53.50
66.06
59.72
40.16
45.90
RoLlama3-8b-Instruct-DPO-2024-10-09
49.96
46.29
53.29
65.57
58.15
34.77
41.70

Downstream tasks

LaRoSeDa
WMT
Few-shot
Finetuned
Few-shot
Finetuned
Model
Binary
(Macro F1)
Multiclass
(Macro F1)
Binary
(Macro F1)
Multiclass
(Macro F1)
EN-RO
(Bleu)
RO-EN
(Bleu)
EN-RO
(Bleu)
RO-EN
(Bleu)
Llama-3-8B-Instruct
95.88
56.21
98.53
86.19
18.88
30.98
28.02
40.28
RoLlama3-8b-Instruct-2024-06-28
97.52
67.41
94.15
87.13
24.01
27.36
26.53
40.36
RoLlama3-8b-Instruct-2024-10-09
95.58
61.20
96.46
87.26
22.92
24.28
27.31
40.52
RoLlama3-8b-Instruct-DPO-2024-10-09
97.48
54.00
-
-
22.09
23.00
-
-
XQuAD
STS
Few-shot
Finetuned
Few-shot
Finetuned
Model
(EM)
(F1)
(EM)
(F1)
(Spearman)
(Pearson)
(Spearman)
(Pearson)
Llama-3-8B-Instruct
39.47
58.67
67.65
82.77
73.04
72.36
83.49
84.06
RoLlama3-8b-Instruct-2024-06-28
39.43
59.50
44.45
59.76
77.20
77.87
85.80
86.05
RoLlama3-8b-Instruct-2024-10-09
18.89
31.79
50.84
65.18
77.60
76.86
86.70
87.09
RoLlama3-8b-Instruct-DPO-2024-10-09
26.05
42.77
-
-
79.64
79.52
-
-

MT-Bench

Model
Average
1st turn
2nd turn
Answers in Ro
Llama-3-8B-Instruct
5.96
6.16
5.76
158/160
RoLlama3-8b-Instruct-2024-06-28
5.15
6.03
4.28
160/160
RoLlama3-8b-Instruct-2024-10-09
5.38
6.09
4.67
160/160
RoLlama3-8b-Instruct-DPO-2024-10-09
5.87
6.22
5.49
160/160

RoCulturaBench

Model
Average
Answers in Ro
Llama-3-8B-Instruct
4.62
100/100
RoLlama3-8b-Instruct-2024-06-28
3.71
100/100
RoLlama3-8b-Instruct-2024-10-09
3.81
100/100
RoLlama3-8b-Instruct-DPO-2024-10-09
4.40
100/100

RoLlama3 Model Family

Model Link
RoLlama3-8b-Instruct-2024-06-28 link
RoLlama3-8b-Instruct-2024-10-09 link
RoLlama3-8b-Instruct-DPO-2024-10-09 link

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}, 
}
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Datasets used to train OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28

Collection including OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28

Evaluation results