  This paper presents a solution for the Multilingual Text Detoxification task
in the PAN-2024 competition of the SmurfCat team. Using data augmentation
through machine translation and a special filtering procedure, we collected an
additional multilingual parallel dataset for text detoxification. Using the
obtained data, we fine-tuned several multilingual sequence-to-sequence models,
such as mT0 and Aya, on a text detoxification task. We applied the ORPO
alignment technique to the final model. Our final model has only 3.7 billion
parameters and achieves state-of-the-art results for the Ukrainian language and
near state-of-the-art results for other languages. In the competition, our team
achieved first place in the automated evaluation with a score of 0.52 and
second place in the final human evaluation with a score of 0.74.
