Update README.md

README.md

@@ -17,7 +17,7 @@ pipeline_tag: image-feature-extraction
 
 \[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
 
-We developed InternViT-300M-448px by leveraging knowledge distillation from the strong vision foundation model, [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5). This update primarily focuses on the efficiency of the vision foundation model. Like its predecessor, the input resolution of this model is dynamic 448×448, where the basic tile size is 448×448, and the number of tiles ranges from 1 to 12 during training. Additionally, it inherits the powerful robustness, OCR capability, and high-resolution processing capability from InternViT-6B-448px-V1-5.
+This update primarily focuses on enhancing the efficiency of the vision foundation model. We developed InternViT-300M-448px by distilling knowledge from the robust vision foundation model, [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5). Like its predecessor, InternViT-300M-448px features a dynamic input resolution of 448×448, with a basic tile size of 448×448. During training, it allows for 1 to 12 tiles, and expands to 1 to 40 tiles during testing. Additionally, it inherits the powerful robustness, OCR capability, and high-resolution processing capacity from InternViT-6B-448px-V1-5.
 
 ## Model Details
 - **Model Type:** vision foundation model, feature backbone