Improve model card and add metadata

This PR adds missing metadata to the model card, including the `pipeline_tag`, `library_name`, and license. It also enhances the model description with a summary of the model and its purpose, taken from the README. Quick Start information is also added for better usability.

README.md

@@ -1,3 +1,58 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
+
+
+This model, VLAA-Thinker-Qwen2VL-7B, is a vision-language model fine-tuned on the VLAA-Thinking dataset. As described in [](https://huggingface.co/papers/2504.11468), it leverages a combination of supervised fine-tuning (SFT) and reinforcement learning (RL) to improve reasoning capabilities in LLMs. The model excels in multimodal reasoning tasks, achieving state-of-the-art performance on the OpenCompass Multimodal Reasoning Leaderboard as of April 7th, 2025.
+
+<p align="center">
+  🌐 <a href="https://ucsc-vlaa.github.io/VLAA-Thinking/" target="_blank">Project Page</a>  
+  • <img src="./assets/ar.svg" alt="Arxiv Logo" style="height: 1em; vertical-align: middle; margin-right: 0.3em;">
+  <a href="./assets/VLAA-Thinker.pdf" target="_blank">Arxiv</a>  
+  • 💻  <a href="https://github.com/UCSC-VLAA/VLAA-Thinking" target="_blank">Code</a>  
+</p>
+
+
+Both **VLAA-Thinker-Qwen2.5-3B** and **VLAA-Thinker-Qwen2.5-7B** achieve **SOTA** performance on [OpenCompass Multimodal Reasoning Leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal-reasoning/?m=REALTIME) as of April 7th, 2025.
+<img src="assets/opencompass_4b_box.png" width = "640" alt="pipeline" align=center />
+
+-----
+
+<img src="assets/opencompass_7b_box.png" width = "640" alt="pipeline" align=center />
+
+
+
+
+## Quick Start 🚀
+### Inference
+Run `python inference.py`. Note that our model is trained with a system prompt. Please ensure that it is included for inference.
+
+
+### Dataset Download
+Run `bash ./utils/download_dataset.sh`. Specify the dataset root with absolute path. The dataset should be ordered as follows:
+```
+├── VLAA-Thinking-SFT-126K.json
+├── VLAA-Thinking-GRPO-25K.json
+└── images
+    ├── allava_laion
+    ├── arxivqa
+    ├── chartqa
+    ├── clevr_math
+    ├── coco
+    │   └── train2017
+    ├── docvqa
+    ├── geoqa170k
+    ├── synthesis
+    ├── vg
+    │   ├── VG_100K
+    │   └── VG_100K_2
+    └── vizwiz
+```
+### Training
+Code coming soon!
+
+(Rest of the README content can be kept as is)