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The rise in popularity of ChatGPT and GPT-4 has significantly accelerated thedevelopment of large models, leading to the creation of numerous impressivelarge language models(LLMs) and multimodal large language models (MLLMs). Thesecutting-edge models owe their remarkable performance to high-quality data.However, the details of the training data used in leading paradigms are oftenkept confidential. This lack of transparency, coupled with the scarcity ofopen-source data, impedes further developments within the community. As aresponse, this paper presents "Wan Juan", a large-scale multimodal datasetcomposed of both Chinese and English data, collected from a wide range of websources. The dataset incorporates text, image-text, and video modalities, witha total volume exceeding 2TB. It was utilized in the training of InternLM, amodel that demonstrated significant advantages in multi-dimensional evaluationswhen compared to models of a similar scale. All data can be accessed athttps://opendatalab.org.cn/WanJuan1.0.

WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models