In this report, we introduce InternVL 1.5, an open-source multimodal large
language model (MLLM) to bridge the capability gap between open-source and
proprietary commercial models in multimodal understanding. We introduce three
simple improvements: (1) Strong Vision Encoder: we explored a continuous
learning strategy for the large-scale vision foundation model -- InternViT-6B,
boosting its visual understanding capabilities, and making it can be
transferred and reused in different LLMs. (2) Dynamic High-Resolution: we
divide images into tiles ranging from 1 to 40 of 448$\times$448 pixels
according to the aspect ratio and resolution of the input images, which
supports up to 4K resolution input. (3) High-Quality Bilingual Dataset: we
carefully collected a high-quality bilingual dataset that covers common scenes,
document images, and annotated them with English and Chinese question-answer
pairs, significantly enhancing performance in OCR- and Chinese-related tasks.
We evaluate InternVL 1.5 through a series of benchmarks and comparative
studies. Compared to both open-source and proprietary models, InternVL 1.5
shows competitive performance, achieving state-of-the-art results in 8 of 18
benchmarks. Code has been released at this https URL

InternVL 1.5 是一种开源的多模态大型语言模型，通过引入强大的视觉编码器、动态高分辨率和高质量双语数据集三个简单改进，提升了多模态理解的能力，在 OCR 和中文相关任务中达到与传统型和专有模型相竞争的性能。