Improve model card: add library name, paper link, and code link

README.md

@@ -1,14 +1,31 @@
 ---
 license: apache-2.0
 pipeline_tag: image-text-to-text
+library_name: transformers
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+tags:
+- 3d
+- spatial-reasoning
+- vlm
+- qwen2.5-vl
 ---
+
+# 3DThinker-Mindcube
+
+This repository contains the stage 1 model checkpoint for **3DThinker**, as presented in the paper [Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views](https://huggingface.co/papers/2510.18632).
+
+3DThinker is a framework that enables Vision-Language Models (VLMs) to exploit geometric information within images for 3D spatial reasoning, simulating human-like spatial imagination without requiring explicit 3D prior inputs or labeled 3D training data.
+
+- **Paper:** [Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views](https://huggingface.co/papers/2510.18632)
+- **Code:** [GitHub - zhangquanchen/3DThinker](https://github.com/zhangquanchen/3DThinker)
+
 ## Introduction
-* The model was trained on Mindcube_Train and tested on MindCube-Tiny.
-* This model corresponds to stage 1 training of Qwen2.5-3B-VL.
-* Note that Tab. 2 is trained on a different training data.
+* The model was trained on **Mindcube_Train** and tested on **MindCube-Tiny**.
+* This model corresponds to **stage 1** training (supervised alignment of 3D latents) of Qwen2.5-3B-VL.
+* Note that Tab. 2 in the paper is trained on a different training data configuration.
   
 ## Bibtex
-If you find 3DThinker helpful for your work, please cite
+If you find 3DThinker helpful for your work, please cite:
 
 ```
 @article{chen2025think,