Update README.md

README.md

@@ -11,10 +11,6 @@ language:
   - multilingual
 tags:
   - internvl
-  - vision
-  - ocr
-  - multi-image
-  - video
   - custom_code
 ---
 
@@ -81,8 +77,6 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 
 - We simultaneously use [InternVL](https://github.com/OpenGVLab/InternVL) and [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet (GPT-4-0613), and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, MMVet (GPT-4-Turbo), and MathVista were evaluated using the VLMEvalKit.
 
-- Please note that evaluating the same model using different testing toolkits like [InternVL](https://github.com/OpenGVLab/InternVL) and [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) can result in slight differences, which is normal. Updates to code versions and variations in environment and hardware can also cause minor discrepancies in results.
-
 ### Video Benchmarks
 
 |          Benchmark          | GPT-4V | LLaVA-NeXT-Video | InternVL-Chat-V1-5 | InternVL2-26B |
@@ -119,16 +113,10 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 
 Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
 
-### Invitation to Evaluate InternVL
-
-We welcome MLLM benchmark developers to assess our InternVL1.5 and InternVL2 series models. If you need to add your evaluation results here, please contact me at [[email protected]](mailto:[email protected]).
-
 ## Quick Start
 
 We provide an example code to run InternVL2-26B using `transformers`.
 
-We also welcome you to experience the InternVL2 series models in our [online demo](https://internvl.opengvlab.com/).
-
 > Please use transformers>=4.37.2 to ensure the model works normally.
 
 ### Model Loading
@@ -599,7 +587,7 @@ print(response)
 
 ## License
 
-This project is released under the MIT license, while InternLM2 is licensed under the Apache-2.0 license.
+This project is released under the MIT License. This project uses the pre-trained internlm2-chat-20b as a component, which is licensed under the Apache License 2.0.
 
 ## Citation
 
@@ -612,16 +600,16 @@ If you find this project useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2410.16261},
   year={2024}
 }
-@article{chen2023internvl,
-  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
-  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
-  journal={arXiv preprint arXiv:2312.14238},
-  year={2023}
-}
 @article{chen2024far,
   title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
   author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
   journal={arXiv preprint arXiv:2404.16821},
   year={2024}
 }
+@article{chen2023internvl,
+  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
+  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
+  journal={arXiv preprint arXiv:2312.14238},
+  year={2023}
+}
 ```