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

.gitattributes

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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))
+
+```

added_tokens.json

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chat_template.json

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+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "/aifs4su/yaodong/wenqi/projects/first-time_safety/output_models/Qwen2-VL-7B-Instruct-safe_thinking_w_retri_0302/slice_end",
+  "architectures": [
+    "Qwen2VLForConditionalGeneration"
+  ],
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+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2_vl",
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+}

configuration.json

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+{"framework": "pytorch", "task": "others", "allow_remote": true}

preprocessor_config.json

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+{
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+  "do_resize": true,
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special_tokens_map.json

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+}