  The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multimodal 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 LLM, using web-scale image-text data from various sources. This model
can be broadly applied to and achieve state-of-the-art performance on 32
generic visual-linguistic benchmarks including 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. It has powerful visual
capabilities and can be a good alternative to the ViT-22B. We hope that our
research could contribute to the development of multi-modal large models. Code
and models are available at https://github.com/OpenGVLab/InternVL.
