Update license to MIT and clarify component licenses

This PR updates the model card to accurately reflect the project's license. The upstream GitHub repository ([https://github.com/OpenGVLab/InternVL](https://github.com/OpenGVLab/InternVL)) specifies the MIT license in its `LICENSE` file and README.

The metadata `license` tag has been changed from `apache-2.0` to `mit`. Additionally, the "License" section in the Markdown content has been updated to clarify that the overall project is under the MIT license, while still mentioning that components like Qwen3 are licensed under Apache-2.0. A direct link to the MIT license file on GitHub is also included for full transparency.

All other elements of the model card (paper link, code link, project page, sample usage, pipeline tag, library name) were found to be accurate and complete.

README.md

@@ -1,24 +1,24 @@
 ---
-license: apache-2.0
-pipeline_tag: image-text-to-text
-library_name: transformers
 base_model:
-  - OpenGVLab/InternViT-300M-448px-V2_5
-  - Qwen/Qwen3-0.6B
-base_model_relation: merge
+- OpenGVLab/InternViT-300M-448px-V2_5
+- Qwen/Qwen3-0.6B
 datasets:
-  - OpenGVLab/MMPR-v1.2
-  - OpenGVLab/MMPR-Tiny
+- OpenGVLab/MMPR-v1.2
+- OpenGVLab/MMPR-Tiny
 language:
-  - multilingual
+- multilingual
+library_name: transformers
+license: mit
+pipeline_tag: image-text-to-text
 tags:
-  - internvl
-  - custom_code
+- internvl
+- custom_code
+base_model_relation: merge
 ---
 
 # InternVL3_5-1B-Pretrained
 
-[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)  [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238)  [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821)  [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)  [\[📜 InternVL2.5-MPO\]](https://huggingface.co/papers/2411.10442)  [\[📜 InternVL3\]](https://huggingface.co/papers/2504.10479) [\[📜 InternVL3.5\]](https://huggingface.co/papers/2508.18265)
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)  [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238)  [\[📜 InternVL 1.5\\]](https://huggingface.co/papers/2404.16821)  [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)  [\[📜 InternVL2.5-MPO\]](https://huggingface.co/papers/2411.10442)  [\[📜 InternVL3\]](https://huggingface.co/papers/2504.10479) [\[📜 InternVL3.5\]](https://huggingface.co/papers/2508.18265)
 
 [\[🆕 Blog\]](https://internvl.github.io/blog/)  [\[🗨️ Chat Demo\]](https://chat.intern-ai.org.cn/)  [\[🚀 Quick Start\]](#quick-start)  [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
 
@@ -423,7 +423,7 @@ You are an AI assistant that rigorously follows this response protocol:
 Ensure that the thinking process is thorough but remains focused on the query. The final answer should be standalone and not reference the thinking section.
 """.strip()
 
-model.system_message = R1_SYSTEMP_PROMPT
+model.system_message = R1_SYSTEM_PROMPT
 ```
 
 ### Inference with Transformers
@@ -530,40 +530,50 @@ generation_config = dict(max_new_tokens=1024, do_sample=True)
 # pure-text conversation (纯文本对话)
 question = 'Hello, who are you?'
 response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 question = 'Can you tell me a story?'
 response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 # single-image single-round conversation (单图单轮对话)
-question = '<image>\nPlease describe the image shortly.'
+question = '<image>
+Please describe the image shortly.'
 response = model.chat(tokenizer, pixel_values, question, generation_config)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 # single-image multi-round conversation (单图多轮对话)
-question = '<image>\nPlease describe the image in detail.'
+question = '<image>
+Please describe the image in detail.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 question = 'Please write a poem according to the image.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 # multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
 pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
 pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
 pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
 
-question = '<image>\nDescribe the two images in detail.'
+question = '<image>
+Describe the two images in detail.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 question = 'What are the similarities and differences between these two images.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 # multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
 pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
@@ -571,17 +581,21 @@ pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat1
 pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
 num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
 
-question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
+question = 'Image-1: <image>
+Image-2: <image>
+Describe the two images in detail.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list,
                                history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 question = 'What are the similarities and differences between these two images.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list,
                                history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 # batch inference, single image per sample (单图批处理)
 pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
@@ -589,13 +603,15 @@ pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat1
 num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
 pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
 
-questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
+questions = ['<image>
+Describe the image in detail.'] * len(num_patches_list)
 responses = model.batch_chat(tokenizer, pixel_values,
                              num_patches_list=num_patches_list,
                              questions=questions,
                              generation_config=generation_config)
 for question, response in zip(questions, responses):
-    print(f'User: {question}\nAssistant: {response}')
+    print(f'User: {question}
+Assistant: {response}')
 
 # video multi-round conversation (视频多轮对话)
 def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
@@ -633,17 +649,24 @@ def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=3
 video_path = './examples/red-panda.mp4'
 pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
 pixel_values = pixel_values.to(torch.bfloat16).cuda()
-video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+video_prefix = ''.join([f'Frame{i+1}: <image>
+' for i in range(len(num_patches_list))])
 question = video_prefix + 'What is the red panda doing?'
-# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\n{question}
+# Frame1: <image>
+Frame2: <image>
+...
+Frame8: <image>
+{question}
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list, history=None, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 
 question = 'Describe this video in detail.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list, history=history, return_history=True)
-print(f'User: {question}\nAssistant: {response}')
+print(f'User: {question}
+Assistant: {response}')
 ```
 
 #### Streaming Output
@@ -727,7 +750,9 @@ image_urls=[
 
 images = [load_image(img_url) for img_url in image_urls]
 # Numbering images improves multi-image conversations
-response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images))
+response = pipe((f'Image-1: {IMAGE_TOKEN}
+Image-2: {IMAGE_TOKEN}
+describe these two images', images))
 print(response.text)
 ```
 
@@ -816,7 +841,7 @@ print(response)
 
 ## License
 
-This project is released under the apache-2.0 License. This project uses the pre-trained Qwen3 as a component, which is licensed under the apache-2.0 License.
+This project is released under the [MIT License](https://github.com/OpenGVLab/InternVL/blob/main/LICENSE). Parts of this project, such as the pre-trained Qwen3 component, are licensed under the Apache-2.0 License.
 
 ## Citation
 
@@ -829,4 +854,4 @@ If you find this project useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2508.18265},
   year={2025}
 }
-```
+```