  Multimodal large language models (MLLMs) have proven effective in a wide
range of tasks requiring complex reasoning and linguistic comprehension.
However, due to a lack of high-quality multimodal resources in languages other
than English, success of MLLMs remains relatively limited to English-based
settings. This poses significant challenges in developing comparable models for
other languages, including even those with large speaker populations such as
Arabic. To alleviate this challenge, we introduce a comprehensive family of
Arabic MLLMs, dubbed \textit{Peacock}, with strong vision and language
capabilities. Through comprehensive qualitative and quantitative analysis, we
demonstrate the solid performance of our models on various visual reasoning
tasks and further show their emerging dialectal potential. Additionally, we
introduce ~\textit{Henna}, a new benchmark specifically designed for assessing
MLLMs on aspects related to Arabic culture, setting the first stone for
culturally-aware Arabic MLLMs.The GitHub repository for the \textit{Peacock}
project is available at \url{https://github.com/UBC-NLP/peacock}.
