Enhance model card: Add pipeline tag, library name, and usage examples (#2)

- Enhance model card: Add pipeline tag, library name, and usage examples (31309f792ebfa03fc0700470f6ad351ef5e589d5)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,7 +1,9 @@
 ---
-license: apache-2.0
 base_model:
 - OpenGVLab/InternVL3-8B
+license: apache-2.0
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 
 **EN** | [中文](README_CN.md) 
@@ -127,3 +129,73 @@ which achieve state-of-the-art performance among open-source models of comparabl
 ## What's Next?
 We will release the accompanying technical report shortly. Please stay tuned!
 
+## 🛠️ QuickStart
+
+### Installation
+
+We recommend using [uv](https://docs.astral.sh/uv/) to manage the environment.
+
+> uv installation guide: <https://docs.astral.sh/uv/getting-started/installation/#installing-uv>
+
+```bash
+git clone git@github.com:OpenSenseNova/SenseNova-SI.git
+cd SenseNova-SI/
+uv sync --extra cu124 # or one of [cu118|cu121|cu124|cu126|cu128|cu129], depending on your CUDA version
+uv sync
+source .venv/bin/activate
+```
+
+### How to Use
+
+Here's an example demonstrating how to use the SenseNova-SI model for multi-image visual question answering with the `transformers` library.
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoModel, AutoProcessor
+
+model_path = "sensenova/SenseNova-SI-1.1-InternVL3-8B"
+
+# Load processor and model
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True).eval()
+
+# Example: Pos-Obj-Obj subset of MMSI-Bench (from GitHub examples)
+# These images should be available locally or loaded from a source
+# For demonstration, assuming they are accessible via relative paths:
+# (Note: In a real scenario, ensure 'examples/Q1_1.png' and 'examples/Q1_2.png' exist from the GitHub repo)
+try:
+    image1 = Image.open("./examples/Q1_1.png").convert("RGB")
+    image2 = Image.open("./examples/Q1_2.png").convert("RGB")
+except FileNotFoundError:
+    print("Example images not found. Please ensure 'examples/Q1_1.png' and 'examples/Q1_2.png' are available, or provide your own images.")
+    # Fallback for demonstration if images are not present
+    image1 = Image.new('RGB', (500, 500), color = 'red')
+    image2 = Image.new('RGB', (500, 500), color = 'blue')
+
+question = "<image><image>
+You are standing in front of the dice pattern and observing it. Where is the desk lamp approximately located relative to you?
+Options: A: 90 degrees counterclockwise, B: 90 degrees clockwise, C: 135 degrees counterclockwise, D: 135 degrees clockwise"
+
+# Prepare inputs
+inputs = processor(text=question, images=[image1, image2], return_tensors="pt").to(model.device)
+
+# Generate response
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=100)
+    response = processor.batch_decode(output, skip_special_tokens=True)[0]
+
+print(f"Question: {question}")
+print(f"Answer: {response}")
+```
+
+## 🖊️ Citation
+
+```bib
+@article{sensenova-si,
+  title = {Scaling Spatial Intelligence with Multimodal Foundation Models},
+  author = {Cai, Zhongang and Wang, Ruisi and Gu, Chenyang and Pu, Fanyi and Xu, Junxiang and Wang, Yubo and Yin, Wanqi and Yang, Zhitao and Wei, Chen and Sun, Qingping and Zhou, Tongxi and Li, Jiaqi and Pang, Hui En and Qian, Oscar and Wei, Yukun and Lin, Zhiqian and Shi, Xuanke and Deng, Kewang and Han, Xiaoyang and Chen, Zukai and Fan, Xiangyu and Deng, Hanming and Lu, Lewei and Pan, Liang and Li, Bo and Liu, Ziwei and Wang, Quan and Lin, Dahua and Yang, Lei},
+  journal = {arXiv preprint arXiv:2511.13719},
+  year = {2025}
+}
+```