Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+  - en
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+  - vision-language-model
+  - multimodal
+  - panoramic-understanding
+  - 360-degree
+  - equirectangular-panorama
+  - spatial-reasoning
+  - panoworld
+base_model:
+  - Qwen/Qwen3.5-9B
+datasets:
+  - wcccp/Pano_dataset
+---
+
+
+# PanoWorld-Hstar
+
+PanoWorld-Hstar is a vision-language model based on **Qwen3.5-9B**, developed for 360-degree panoramic understanding and spatial reasoning.
+
+The model is part of the **PanoWorld** project, which focuses on ERP-native panoramic perception, global spatial topology understanding, and human-centric visual search in 360° scenes.
+
+* Project: https://github.com/wcpcp/PanoWorld
+* Model: https://huggingface.co/wcccp/PanoWorld
+* Dataset: https://huggingface.co/datasets/wcccp/Pano_dataset
+
+## Model Description
+
+PanoWorld-Hstar is fine-tuned for vision-language understanding in equirectangular panorama images. It is designed to improve model capability on panoramic scene captioning, spatial relation reasoning, direction understanding, and 360° visual question answering.
+
+## Intended Use
+
+This model is intended for research on:
+
+* 360° panoramic image understanding
+* panoramic visual question answering
+* spatial and directional reasoning
+* human-centric visual search in panoramic scenes
+* embodied AI and panoramic scene perception
+
+## Usage
+
+```python
+import torch
+from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
+
+model_id = "wcccp/PanoWorld-Hstar"
+
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+model = Qwen3_5ForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "example_panorama.jpg"},
+            {"type": "text", "text": "Describe this 360-degree panoramic scene."},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=512,
+)
+
+generated_ids_trimmed = [
+    output_ids[len(input_ids):]
+    for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+]
+
+response = processor.batch_decode(
+    generated_ids_trimmed,
+    skip_special_tokens=True,
+    clean_up_tokenization_spaces=False,
+)[0]
+
+print(response)
+```
+
+Please use a recent version of `transformers` that supports Qwen3.5.
+
+
+## Citation
+
+```bibtex
+@misc{wang2026panoworld,
+      title={PanoWorld: Towards Spatial Supersensing in 360$^\circ$ Panorama World}, 
+      author={Changpeng Wang and Xin Lin and Junhan Liu and Yuheng Liu and Zhen Wang and Donglian Qi and Yunfeng Yan and Xi Chen},
+      year={2026},
+      eprint={2605.13169},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2605.13169}, 
+}
+```