The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However, the progress in vision and
vision-language foundation models, which are also critical elements of
multi-modal AGI, has not kept pace with LLMs. In this work, we design a
large-scale vision-language foundation model (InternVL), which scales up the
vision foundation model to 6 billion parameters and progressively aligns it
with the large language model, using web-scale image-text data from various
sources. This model can be broadly applied to and achieve state-of-the-art
performance on visual perception tasks such as image-level or pixel-level
recognition, vision-language tasks such as zero-shot image/video
classification, zero-shot image/video-text retrieval, and link with LLMs to
create multi-modal dialogue systems. We hope that our research could contribute
to the development of multi-modal large models. Code and models are available
at this https URL

通过设计大规模的视觉语言基础模型 (InternVL)，其参数规模扩展到 60 亿，并逐步与大型语言模型对齐，该研究旨在推动视觉与视觉语言基础模型的发展与应用，以实现视觉感知任务的最新性能、多模式对话系统的构建和与大型语言模型的链接。