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+ ---
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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-VL-7B-Instruct
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+ ---
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+
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+ # Safe-o1-V Model Card πŸ€–βœ¨
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+
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+ ## Model Overview πŸ“
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+ `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 πŸš€.
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+
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+ ---
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+
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+ ## Features and Highlights 🌟
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+ - **Safety First** πŸ”’: 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** πŸ’‘: Compared to traditional models, `Safe-o1-V` 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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+
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+ ---
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+
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+ ## Usage πŸš€
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+ You can load `Safe-o1-V` using the Hugging Face `transformers` library:
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ tokenizer = AutoTokenizer.from_pretrained("PKU-Alignment/Safe-o1-V")
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+ model = AutoModelForCausalLM.from_pretrained("PKU-Alignment/Safe-o1-V")
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+
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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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+
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+ ```