初始化项目，由ModelHub XC社区提供模型

Model: ydeng9/OpenVLThinker-7B
Source: Original Platform

README.md

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+---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+## Overview
+OpenVLThinker-7B is a vision-language reasoning model designed to handle multimodal tasks. It is especially tuned for visual mathematical problem-solving.
+
+For more details: [Blog](https://yihe-deng.notion.site/openvlthinker), [GitHub](https://github.com/yihedeng9/OpenVLThinker)
+
+## How to use
+```python
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+import torch
+from qwen_vl_utils import process_vision_info
+import requests
+from PIL import Image
+
+# 1. Define model and processor names
+model_name = "ydeng9/OpenVLThinker-7B"
+processor_name = "Qwen/Qwen2.5-VL-7B-Instruct"
+
+# 2. Load the OpenVLThinker-7B model and processor
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map=device
+)
+processor = AutoProcessor.from_pretrained(processor_name)
+
+# 3. Define a sample image URL and an instruction
+image_url = "https://example.com/sample_image.jpg"  # replace with your image URL
+instruction = "Example question"
+
+# 4. Create a multimodal prompt using a chat message structure
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image_url},
+            {"type": "text", "text": instruction},
+        ],
+    }
+]
+
+# 5. Generate a text prompt from the chat messages
+text_prompt = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+# 6. Process image (and video) inputs from the messages
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text_prompt],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+).to(device)
+
+# 7. Generate the model's response (with specified generation parameters)
+generated_ids = model.generate(
+    **inputs,
+    do_sample=True,
+    max_new_tokens=2048,
+    top_p=0.001,
+    top_k=1,
+    temperature=0.01,
+    repetition_penalty=1.0,
+)
+
+# 8. Decode the generated tokens into human-readable text
+generated_text = processor.batch_decode(
+    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)[0]
+
+# 9. Print the generated response
+print("Generated Response:")
+print(generated_text)
+```
+
+### Citation
+```text
+@misc{deng2025openvlthinker,
+      title={OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement}, 
+      author={Yihe Deng and Hritik Bansal and Fan Yin and Nanyun Peng and Wei Wang and Kai-Wei Chang},
+      year={2025},
+      eprint={2503.17352},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.17352}, 
+}
+```