初始化项目，由ModelHub XC社区提供模型

Model: PKU-Alignment/safe-o1-v-7b
Source: Original Platform

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+base_model: Qwen/Qwen2-VL-7B-Instruct
+---
+
+# Safe-o1-V Model Card 🤖✨
+
+## Model Overview 📝
+`Safe-o1-V` is an innovative multi-modal language model that introduces a **self-monitoring thinking process** to detect and filter unsafe content, achieving more robust safety performance 🚀.
+
+---
+
+## Features and Highlights 🌟
+- **Safety First** 🔒: Through a self-monitoring mechanism, it detects potential unsafe content in the thinking process in real-time, ensuring outputs consistently align with ethical and safety standards.  
+- **Enhanced Robustness** 💡: Compared to traditional models, `Safe-o1-V` performs more stably in complex scenarios, reducing unexpected "derailments."  
+- **User-Friendly** 😊: Designed to provide users with a trustworthy conversational partner, suitable for various application scenarios, striking a balance between helpfulness and harmfulness.  
+![](https://github.com/D4YON3/images/blob/main/figs_2025-04-03%20214712.png?raw=true)
+
+---
+
+## Usage 🚀
+You can load `Safe-o1-V` using the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("PKU-Alignment/Safe-o1-V")
+model = AutoModelForCausalLM.from_pretrained("PKU-Alignment/Safe-o1-V")
+
+input_text = "Hello, World!"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```