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1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base")
4model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base")
5
6input_text = "Mihai Eminescu a fost "
7input_ids = tokenizer(input_text, return_tensors="pt")
8
9outputs = model.generate(**input_ids, max_new_tokens=100)
10print(tokenizer.decode(outputs[0]))| Model | Average | ARC | MMLU | Winogrande | HellaSwag | GSM8k | TruthfulQA |
|---|---|---|---|---|---|---|---|
| Llama-2-7b | 37.11 | 36.09 | 33.67 | 57.60 | 48.00 | 5.08 | 42.23 |
| RoLlama2-7b-Base | 38.03 | 37.95 | 27.22 | 59.29 | 57.22 | 2.53 | 44.00 |
@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},
}