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license: cc-by-4.0 |
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language: |
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- en |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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--- |
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# Safe-o1 Model Card 🤖✨ |
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## Model Overview 📝 |
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`Safe-o1` is an innovative language model that introduces a **self-monitoring thinking process** to detect and filter unsafe content, achieving more robust safety performance 🚀. |
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## Features and Highlights 🌟 |
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- **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. |
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- **Enhanced Robustness** 💡: Compared to traditional models, `Safe-o1` performs more stably in complex scenarios, reducing unexpected "derailments." |
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- **User-Friendly** 😊: Designed to provide users with a trustworthy conversational partner, suitable for various application scenarios, striking a balance between helpfulness and harmfulness. |
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## Usage 🚀 |
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You can load `Safe-o1` using the Hugging Face `transformers` library: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("PKU-Alignment/Safe-o1") |
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model = AutoModelForCausalLM.from_pretrained("PKU-Alignment/Safe-o1") |
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input_text = "Hello, World!" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
