  Despite the widespread use of the Persian language by millions globally,
limited efforts have been made in natural language processing for this
language. The use of large language models as effective tools in various
natural language processing tasks typically requires extensive textual data and
robust hardware resources. Consequently, the scarcity of Persian textual data
and the unavailability of powerful hardware resources have hindered the
development of large language models for Persian. This paper introduces the
first large Persian language model, named PersianLLaMA, trained on a collection
of Persian texts and datasets. This foundational model comes in two versions,
with 7 and 13 billion parameters, trained on formal and colloquial Persian
texts using two different approaches. PersianLLaMA has been evaluated for
natural language generation tasks based on the latest evaluation methods,
namely using larger language models, and for natural language understanding
tasks based on automated machine metrics. The results indicate that
PersianLLaMA significantly outperforms its competitors in both understanding
and generating Persian text. PersianLLaMA marks an important step in the
development of Persian natural language processing and can be a valuable
resource for the Persian-speaking community. This large language model can be
used for various natural language processing tasks, especially text generation
like chatbots, question-answering, machine translation, and text summarization
