license: cc-by-nc-4.0
language:
- ro
Model Card for Model ID
Built with Meta Llama 3
RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the instruct 7B 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.
- Developed by: OpenLLM-Ro
- Language(s): Romanian
- License: cc-by-nc-4.0
- Finetuned from model: Meta-Llama-3-8B
Model Sources
- Repository: https://github.com/OpenLLM-Ro/llama-recipes
- Paper: https://arxiv.org/abs/2406.18266
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")
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]))
Benchmarks
| Model | Average | ARC | MMLU | Winogrande | HellaSwag | GSM8k | TruthfulQA |
|---|---|---|---|---|---|---|---|
| Llama-3-8B-Instruct | 50.15 | 43.73 | 49.02 | 59.35 | 53.16 | 44.12 | 51.52 |
| RoLlama3-8b-Instruct | 50.61 | 44.66 | 52.19 | 67.58 | 57.65 | 30.20 | 51.39 |
MT-Bench
| Model | Average | 1st turn | 2nd turn | Answers in Ro |
|---|---|---|---|---|
| Llama-3-8B-Instruct | 5.92 | 6.36 | 5.49 | 158 / 160 |
| RoLlama3-8b-Instruct | 5.28 | 6.10 | 4.45 | 160 / 160 |
RoCulturaBench
| Model | Score | Answers in Ro |
|---|---|---|
| Llama-3-8B-Instruct | 4.61 | 100 / 100 |
| RoLlama3-8b-Instruct | 3.83 | 100 / 100 |
RoLlama3 Model Family
| Model | Link |
|---|---|
| RoLlama3-8b-Instruct | 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},
}