Improve model card: Add paper link and descriptive tags

README.md

@@ -1,23 +1,28 @@
 ---
-license: apache-2.0
-pipeline_tag: image-text-to-text
-library_name: transformers
 base_model:
-  - OpenGVLab/InternViT-300M-448px-V2_5
-  - openai/gpt-oss-20b
-base_model_relation: merge
+- OpenGVLab/InternViT-300M-448px-V2_5
+- openai/gpt-oss-20b
 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
+- vision-language-model
+- reasoning
+base_model_relation: merge
 ---
 
 # InternVL3_5-GPT-OSS-20B-A4B-Preview
 
+This repository contains the model as described in the paper [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](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/)
@@ -28,7 +33,7 @@ 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)
 
@@ -142,7 +147,7 @@ Compared to InternVL3.5, InternVL3.5-Flash further integrates the *Visual Resolu
 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)
@@ -495,7 +500,9 @@ 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)
 ```
 
@@ -597,4 +604,4 @@ If you find this project useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2508.18265},
   year={2025}
 }
-```
+```