Improve model card: Add multimodal tag and Hugging Face Space link

Improved the model card by:
- Added `multimodal` to the tags for better discoverability, as the model is described as an "advanced multimodal large language model".
- Added an explicit link to the Hugging Face Space project page (`https://huggingface.co/spaces/OpenGVLab/InternVL`) in the introductory section, complementing the existing GitHub and chat demo links.

README.md

@@ -1,23 +1,24 @@
 ---
-license: apache-2.0
-pipeline_tag: image-text-to-text
-library_name: transformers
 base_model:
-  - OpenGVLab/InternVL3_5-241B-A28B-MPO
-base_model_relation: finetune
+- OpenGVLab/InternVL3_5-241B-A28B-MPO
 datasets:
-  - OpenGVLab/MMPR-v1.2
-  - OpenGVLab/MMPR-Tiny
+- OpenGVLab/MMPR-v1.2
+- OpenGVLab/MMPR-Tiny
 language:
-  - multilingual
+- multilingual
+library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
-  - internvl
-  - custom_code
+- internvl
+- custom_code
+- multimodal
+base_model_relation: finetune
 ---
 
 # InternVL3_5-241B-A28B
 
-[\[📂 GitHub\]](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)  [\[🚀 Hugging Face Space\]](https://huggingface.co/spaces/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/)
 
@@ -27,7 +28,7 @@ tags:
 
 ## Introduction
 
-We introduce *InternVL3.5*, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the *Cascade Reinforcement Learning (Cascade RL)* framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a *Visual Resolution Router (ViR)* that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled *Vision-Language Deployment (DvD)* strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning performance and a 4.05 \\(\times\\) inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e.,  InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
+We introduce *InternVL3.5*, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the *Cascade Reinforcement Learning (Cascade RL)* framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a *Visual Resolution Router (ViR)* that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled *Vision-Language Deployment (DvD)* strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 \\(\times\\) inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
 
 ![image/jpg](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B/resolve/main/images/performance.jpg)
 
@@ -137,11 +138,11 @@ The Dynamic High Resolution strategy introduced in InternVL1.5 is also retained
 
 
 `InternVL3.5-Flash`:
-Compared to InternVL3.5, InternVL3.5-Flash further integrates the *Visual Resolution Router (ViR)*, thus yielding a series of  efficient variants friendly  suitable for  resource-constrained scenarios. 
+Compared to InternVL3.5, InternVL3.5-Flash further integrates the *Visual Resolution Router (ViR)*, thus yielding a series of efficient variants friendly suitable for resource-constrained scenarios. 
 Specifically, in InternVL3.5, each image patch is initially represented as 1024 visual tokens for the vision encoder, which are then compressed into 256 tokens via a pixel shuffle module before being passed to the Large Language Model (LLM).
 In InternVL3.5-Flash, as shown in the Figure below, an additional pixel shuffle module with a higher compression rate is included, enabling the compression of visual tokens down to 64 tokens.
 For each patch, the patch router determines the appropriate compression rate by assessing its semantic richness, and routes it to the corresponding pixel shuffle module accordingly.
-Benefiting from this patch-aware compression mechanism, InternVL3.5-Flash is able to reduce the number of visual tokens by 50\% while maintaining nearly 100\% of the performance of InternVL3.5.
+Benefiting from this patch-aware compression mechanism, InternVL3.5-Flash is able to reduce the number of visual tokens by 50% while maintaining nearly 100% of the performance of InternVL3.5.
 
 
 ![image/jpg](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B/resolve/main/images/architecture.jpg)
@@ -156,8 +157,8 @@ $$
     \mathcal{L}_{i}=-\log p_\theta\left(x_i \mid x_1, \ldots, x_{i-1}\right),
 $$
 
-where \\(x_i\\) is the predicted token and  prefix tokens in \\(\{x_1, x_2, \ldots, x_{i-1}\}\\) can be either  text tokens or  image tokens. Notably, for conversation samples, only response tokens  are included for the calculation of the loss.
-Additionally, to mitigate bias toward either longer or shorter responses during training, we adopt the square averaging to re-weight the NTP loss  as follows:
+where \\(x_i\\) is the predicted token and prefix tokens in \\(\{x_1, x_2, \ldots, x_{i-1}\}\\) can be either text tokens or image tokens. Notably, for conversation samples, only response tokens are included for the calculation of the loss.
+Additionally, to mitigate bias toward either longer or shorter responses during training, we adopt the square averaging to re-weight the NTP loss as follows:
 
 $$
 \mathcal{L}_{i}^{'} = \frac{w_i}{\sum_j w_j} \cdot \mathcal{L}_i, \quad w_i = \frac{1}{N^{0.5}},
@@ -167,8 +168,8 @@ where \\(N\\) denotes the number of tokens in the training sample on which the l
 
 ### Supervised Fine-Tuning
 
-During the SFT phase, we adopt the same objective as in the pre-training stage and use the  square-root averaging strategy to calculate the final loss.  In this stage, the context window is set to 32K tokens to adapt long-context information.
-Compared to InternVL3, the SFT stage of InternVL3.5 contains  more high-quality and  diverse training data derived from three sources: 
+During the SFT phase, we adopt the same objective as in the pre-training stage and use the square-root averaging strategy to calculate the final loss. In this stage, the context window is set to 32K tokens to adapt long-context information.
+Compared to InternVL3, the SFT stage of InternVL3.5 contains more high-quality and diverse training data derived from three sources: 
 
 (1) Instruction-following data from InternVL3, which are reused to preserve broad coverage of vision–language tasks. 
 
@@ -180,7 +181,7 @@ Compared to InternVL3, the SFT stage of InternVL3.5 contains  more high-quality
 
 Cascade RL aims to combine the benefits of offline RL and online RL to progressively facilitate the post-training of MLLMs in an efficient manner.
 Specifically, we first fine-tune the model using an offline RL algorithm as an efficient warm-up stage to reach a satisfied results, which can guarantee the high-quality rollouts for the latter stage. 
-Subsequently, we employ an online RL algorithm to further refine the output distribution based on rollouts generated by the model itself.  Compared to the single offline or online RL stage, our cascaded RL achieves significant performance improvements at a fraction of the GPU time cost.
+Subsequently, we employ an online RL algorithm to further refine the output distribution based on rollouts generated by the model itself. Compared to the single offline or online RL stage, our cascaded RL achieves significant performance improvements at a fraction of the GPU time cost.
 
 
 
@@ -233,7 +234,7 @@ $$
 \Bigg],
 $$
 
-where \\(\mathrm{KL}\) denotes the KL divergence and \(\xi\) denotes the compression rate, which is uniformly sampled from \(\{\frac{1}{4},\frac{1}{16}\}\). The image \(I_\xi\) is represented as 256 tokens when \(\xi=\frac{1}{4}\) and 64 tokens when \(\xi=\frac{1}{16}\). Notably, the reference model always performs inference with \(\xi=\frac{1}{4}\).
+where \\(\mathrm{KL}\\) denotes the KL divergence and \(\xi\) denotes the compression rate, which is uniformly sampled from \(\{\frac{1}{4},\frac{1}{16}\}\). The image \(I_\xi\) is represented as 256 tokens when \(\xi=\frac{1}{4}\) and 64 tokens when \(\xi=\frac{1}{16}\). Notably, the reference model always performs inference with \(\xi=\frac{1}{4}\).
 
 
 `Router training`:
@@ -257,7 +258,7 @@ y_i^\text{router} =
 \end{cases}
 $$
 
-where \(y_i^{\text{router}}=0\) and \(y_i^{\text{router}}=1\)  indicate that the compression rate \(\xi\) is set to \(\tfrac{1}{16}\) and \(\tfrac{1}{4}\), respectively.
+where \(y_i^{\text{router}}=0\) and \(y_i^{\text{router}}=1\) indicate that the compression rate \(\xi\) is set to \(\tfrac{1}{16}\) and \(\tfrac{1}{4}\), respectively.
 
 > Please see [our paper](https://huggingface.co/papers/2508.18265) for more technical and experimental details.
 
@@ -278,7 +279,7 @@ This approach improves reasoning breadth.
 
 ### Decoupled Vision-Language Deployment
 
-In multimodal inference, the vision encoder and language model have distinct computational characteristics. The vision encoder that transforms images into semantic features is highly parallelizable and does not rely on long-term history state.  In contrast,  the language model adopts the inference in an autoregressive manner, which requires previous states to compute the next one. This sequential property makes the language part more sensitive to memory bandwidth and latency. 
+In multimodal inference, the vision encoder and language model have distinct computational characteristics. The vision encoder that transforms images into semantic features is highly parallelizable and does not rely on long-term history state. In contrast, the language model adopts the inference in an autoregressive manner, which requires previous states to compute the next one. This sequential property makes the language part more sensitive to memory bandwidth and latency. 
 When MLLMs are deployed online at scale, the vision and language models often block each other, thus incurring additional inference cost. This effect becomes more pronounced with larger vision models or higher-resolution images.
 
 ![image/jpg](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B/resolve/main/images/DvD.jpg)
@@ -529,40 +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()
@@ -570,17 +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()
@@ -588,13 +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):
@@ -632,17 +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
@@ -726,7 +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)
 ```
 
@@ -828,4 +854,59 @@ If you find this project useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2508.18265},
   year={2025}
 }
+@article{zhu2025internvl3,
+  title={Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models},
+  author={Zhu, Jinguo and Wang, Weiyun and Chen, Zhe and Liu, Zhaoyang and Ye, Shenglong and Gu, Lixin and Tian, Hao and Duan, Yuchen and Su, Weijie and Shao, Jie and others},
+  journal={arXiv preprint arXiv:2504.10479},
+  year={2025}
+}
+@article{chen2024expanding,
+  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
+  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
+  journal={arXiv preprint arXiv:2412.05271},
+  year={2024}
+}
+@article{wang2024mpo,
+  title={Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization},
+  author={Wang, Weiyun and Chen, Zhe and Wang, Wenhai and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Zhu, Jinguo and Zhu, Xizhou and Lu, Lewei and Qiao, Yu and Dai, Jifeng},
+  journal={arXiv preprint arXiv:2411.10442},
+  year={2024}
+}
+@article{gao2024mini,
+  title={Mini-InternVL: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance},
+  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
+  journal={Visual Intelligence},
+  volume={2},
+  number={1},
+  pages={1--17},
+  year={2024},
+  publisher={Springer}
+}
+@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={Science China Information Sciences},
+  volume={67},
+  number={12},
+  pages={220101},
+  year={2024},
+  publisher={Springer}
+}
+@inproceedings{chen2024internvl,
+  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, Xinguo and Lu, Lewei and others},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={24185--24198},
+  year={2024}
+}
 ```
+
+## Acknowledgement
+
+InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!
+
+______________________________________________________________________
+
+Scan the following QR Code, join our WeChat group.
+
+<p align="center"><img width="300" alt="image" src="https://github.com/user-attachments/assets/f776df09-ebba-4fd5-80c2-fec4ff1518be"></p>