Update README.md

README.md

@@ -17,7 +17,7 @@ pipeline_tag: visual-question-answering
 
 > _Two interns holding hands, symbolizing the integration of InternViT and InternLM._
 
-\[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
+\[[InternVL 1.5 Technical Report](https://arxiv.org/abs/2404.16821)\]  \[[CVPR Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
 
 We 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 designs: 
@@ -34,13 +34,10 @@ We introduce three simple designs:
   - Params: 25.5B
 
 - **Training Strategy:**
-  - Pretraining Stage
-    - Learnable Component: ViT + MLP
-    - Data: Please see our technical report.
-  - SFT Stage
-    - Learnable Component: ViT + MLP + LLM
-    - Data: Please see our technical report.
-
+  - Learnable component in the pretraining stage: ViT + MLP
+  - Learnable component in the finetuning stage: ViT + MLP + LLM
+  - For more details on training hyperparameters, take a look at our code: [pretrain](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_pretrain.sh) | [finetune](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/shell/internlm2_20b_dynamic/internvl_chat_v1_5_internlm2_20b_dynamic_res_finetune.sh)
+  
 ## Released Models
 
 | Model                                                      | Vision Foundation Model                                                     | Release Date           |Note                                |
@@ -50,6 +47,10 @@ We introduce three simple designs:
 | InternVL-Chat-V1.2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) )      |InternViT-6B-448px-V1-2(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2))     |2024.02.11       |             scaling up LLM to 34B       |
 | InternVL-Chat-V1.1(🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1))      |InternViT-6B-448px-V1-0(🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0))    |2024.01.24         |   support Chinese and stronger OCR   |
 
+## Architecture
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YLvX3V-L0kwsyRn3Lhciw.png)
+
 ## Performance
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/4b85G7txoJ_LpT19SZJ4A.png)
@@ -58,29 +59,12 @@ We introduce three simple designs:
 
 ## Examples
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/R34jISP4K1U17m9yNP38O.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ChkU9XtlsjH0l2EqlO_is.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/1TFxIcf96ANRPLoy4-rbh.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/Wpjo1Sdwf7XcEDevqwcr-.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/kO4-J38sN8TFtmQ5mIBMS.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/qPnTe3Q9UBy8wbclOsmWk.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/l_BILRi13CbZNzbZYn6o6.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/2782y7RnvGBogYEIG__7S.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/RyO35PTH14OFiwyxtAZM2.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/xiLZXWL-JiCTVPnV_VxS2.png)
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-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/gqX46Tt5jvrcVqb0vcf06.png)
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/YVr-93mvVMR6UFpGezns7.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ivhj4QqcO2NHUa28DTDkK.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/18GeOW10QVcSt5g--TgDY.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/tGM_TwdV297H1fCxQ0PZU.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/FwlSRBpKgURAVkXNOLoSp.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/to3nOaAnyv-fGLEoNPLzz.png)
 
 ## Model Usage