---
title: safe-o1-v-7b
canonical_url: "https://www.modelscope.ai/models/PKU-Alignment/safe-o1-v-7b"
md_url: "https://www.modelscope.ai/models/PKU-Alignment/safe-o1-v-7b.md"
repository: PKU-Alignment/safe-o1-v-7b
last_updated: 2026-01-06
license: cc-by-4.0
model_type:
  - qwen2_vl
architectures:
  - Qwen2VLForConditionalGeneration
base_model:
  - Qwen/Qwen2-VL-7B-Instruct
base_model_relation: finetune
library_name:
  - transformer
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 125
stars: 0
---

# safe-o1-v-7b

> safe-o1-v-7b - An open-source model by PKU-Alignment on ModelScope. Safe-o1-V Model Card 🤖✨

PKU-Alignment/safe-o1-v-7b is a machine learning model on ModelScope. licensed under cc-by-4.0. derived from Qwen/Qwen2-VL-7B-Instruct.

- **Repository**: PKU-Alignment/safe-o1-v-7b
- **License**: cc-by-4.0
- **Base model**: Qwen/Qwen2-VL-7B-Instruct
- **Downloads**: 125
- **Stars**: 0
- **Last updated**: 2026-01-06

Source: https://www.modelscope.ai/models/PKU-Alignment/safe-o1-v-7b

---

# 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))

```