初始化项目，由ModelHub XC社区提供模型

Model: OX-PIXL/SpatialThinker-3B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- spatial-reasoning
+- multimodal
+- vision-language
+- scene-graph
+- reinforcement-learning
+base_model: Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+---
+
+# SpatialThinker-3B
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2511.07403">
+    <img src="https://img.shields.io/badge/arXiv-2511.07403-b31b1b.svg" alt="arXiv">
+  </a>
+  <a href="https://hunarbatra.com/SpatialThinker">
+    <img src="https://img.shields.io/badge/🌐%20Project%20Page-blue.svg" alt="Project Page">
+  </a>
+  <a href="https://github.com/hunarbatra/SpatialThinker">
+    <img src="https://img.shields.io/badge/GitHub-Repository-black.svg" alt="GitHub">
+  </a>
+</p>
+
+**SpatialThinker-3B** is a 3D-aware multimodal large language model (MLLM) trained with reinforcement learning to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards.
+
+## Model Description
+
+- **Base Model**: Qwen2.5-VL-3B-Instruct
+- **Training**: GRPO (Group Relative Policy Optimization) with dense spatial rewards
+- **Training Data**: STVQA-7K (7,587 spatial VQA samples)
+- **Authors**: Hunar Batra, Haoqin Tu, Hardy Chen, Yuanze Lin, Cihang Xie, Ronald Clark
+- **Institutions**: University of Oxford, UC Santa Cruz
+
+## Key Features
+
+- **Structured Spatial Reasoning**: Constructs question-focused scene subgraphs with objects, bounding boxes, and relations
+- **Dense Spatial Rewards**: Multi-objective reward function enforcing format, count, accuracy, and spatial grounding
+- **9 Spatial Reasoning Categories**: Relations, reach, size, orientation, instance location, depth, distance, count, and existence
+- **Outperforms GPT-4o**: On spatial understanding benchmarks while using only 7K training samples
+
+## Inference Template
+
+Use the following template for inference:
+
+```
+You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
+
+Image size: {Width} x {Height}
+```
+
+## Usage
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from PIL import Image
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "OX-PIXL/SpatialThinker-3B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("OX-PIXL/SpatialThinker-3B")
+
+# Load image
+image = Image.open("your_image.jpg")
+width, height = image.size
+
+# Prepare prompt with template
+template = f"""You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
+
+Image size: {width} x {height}"""
+
+question = "Where is the cat relative to the couch? (A) on top of (B) in front of (C) behind (D) beside"
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": template + "\n\n" + question},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(output)
+```
+
+## Citation
+
+```bibtex
+@misc{batra2025spatialthinkerreinforcing3dreasoning,  
+  title={SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards},  
+  author={Hunar Batra and Haoqin Tu and Hardy Chen and Yuanze Lin and Cihang Xie and Ronald Clark},  
+  year={2025},  
+  eprint={2511.07403},  
+  archivePrefix={arXiv},  
+  primaryClass={cs.CV},  
+  url={https://arxiv.org/abs/2511.07403},  
+}
+```
+
+## Links
+
+- 📄 **Paper**: [arXiv:2511.07403](https://arxiv.org/abs/2511.07403)
+- 🌐 **Project Page**: [hunarbatra.com/SpatialThinker](https://hunarbatra.com/SpatialThinker)
+- 💻 **GitHub**: [github.com/hunarbatra/SpatialThinker](https://github.com/hunarbatra/SpatialThinker)
+- 🤗 **Dataset**: [OX-PIXL/STVQA-7K](https://huggingface.co/datasets/OX-PIXL/STVQA-7K)

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chat_template.json

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+}

config.json

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+    "Qwen2_5_VLForConditionalGeneration"
+  ],
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+    "spatial_merge_size": 2,
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+    "temporal_patch_size": 2,
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+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

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+  "transformers_version": "4.51.0"
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+}

preprocessor_config.json

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+{
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+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_center_crop": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
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+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "return_tensors": null,
+  "size": {
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+    "shortest_edge": 3136
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+  "temporal_patch_size": 2
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
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+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
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+    "content": "<|im_end|>",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+{
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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "single_word": false,
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+      "special": true
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+    }
+  },
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+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "use_fast": true
+}