This paper presents the findings from our participation in the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6) in 2024. Our specific focus was on the second task (Task 2), which involved translating text at the sentence level from five distinct Dialectal Arabic (DA) (Gulf, Egyptian, Levantine, Iraqi, and Maghrebi) into Modern Standard Arabic (MSA). Our team, Sirius_Translators, fine-tuned four AraT5 models namely; AraT5 base, AraT5v2-base-1024, AraT5-MSA-Small, and AraT5-MSA-Base for the Arabic machine translation (MT) task. These models were fine-tuned using a variety of parallel corpora containing Dialectal Arabic and Modern Standard Arabic. Based on the evaluation results of OSACT6 2024 Shared Task2, our fine-tuned AraT5v2-base-1024 model achieved an overall BLEU score of 21.0 on the development (Dev) set and 9.57 on the test set, respectively.