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

本研究介绍了第一个大型波斯语语言模型 PersianLLaMA，在波斯文本和数据集上训练，通过最新的评估方法在波斯文本的生成和理解任务中表现出色，对于发展波斯语自然语言处理，特别是文本生成方面如聊天机器人、问答、机器翻译和文本摘要等任务可作为宝贵资源。