  Developing monolingual large Pre-trained Language Models (PLMs) is shown to
be very successful in handling different tasks in Natural Language Processing
(NLP). In this work, we present AraMUS, the largest Arabic PLM with 11B
parameters trained on 529GB of high-quality Arabic textual data. AraMUS
achieves state-of-the-art performances on a diverse set of Arabic
classification and generative tasks. Moreover, AraMUS shows impressive few-shot
learning abilities compared with the best existing Arabic PLMs.
