Update README.md

README.md

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 [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#model-usage)  [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376)
 
-We released InternVL-Chat-V1-1, featuring a structure similar to LLaVA, including a ViT, an MLP projector, and an LLM. In this version, we explored increasing the resolution to 448x448, enhancing OCR capabilities, and improving support for Chinese conversations.
+We released InternVL-Chat-V1-1, featuring a structure similar to LLaVA, including a ViT, an MLP projector, and an LLM. As shown in the figure below, we connected our InternViT-6B to LLaMA2-13B through a simple MLP projector. Note that the LLaMA2-13B used here is not the original model but an internal chat version obtained by incrementally pre-training and fine-tuning the LLaMA2-13B base model for Chinese language tasks. Overall, our model has a total of 19 billion parameters.
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+<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/HD29tU-g0An9FpQn1yK8X.png" style="width: 50%;">
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+In this version, we explored increasing the resolution to 448 × 448, enhancing OCR capabilities, and improving support for Chinese conversations. Since the 448 × 448 input image generates 1024 visual tokens after passing through the ViT, leading to a significant computational burden, we use a pixel shuffle operation to reduce the 1024 tokens to 256 tokens.
 
 ## Model Details
 - **Model Type:** multimodal large language model (MLLM)