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 https://github.com/OpenGVLab/InternVL.

中文翻译：

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InternVL：扩大视觉基础模型并调整通用视觉语言任务

大型语言模型 (LLM) 的指数级增长为多模式 AGI 系统开辟了无数可能性。然而，视觉和视觉语言基础模型（也是多模态 AGI 的关键要素）的进展并没有跟上法学硕士的步伐。在这项工作中，我们设计了一个大规模视觉语言基础模型（InternVL），它将视觉基础模型扩展到 60 亿个参数，并使用来自各个领域的网络规模图像文本数据逐步将其与大型语言模型对齐。来源。该模型可以广泛应用于视觉感知任务（例如图像级或像素级识别）、视觉语言任务（例如零样本图像/视频分类、零样本图像）并实现最先进的性能/视频文本检索，并与法学硕士链接以创建多模式对话系统。我们希望我们的研究能够为多模态大型模型的开发做出贡献。代码和模型可在 https://github.com/OpenGVLab/InternVL 获取。