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---
license: apache-2.0
language:
- en
- zh
tags:
- safety
- moderation
- multimodal
- omniguard
pipeline_tag: text-generation
---
# OmniGuard: Unified Omni-Modal Guardrails with Deliberate Reasoning
OmniGuard is a multimodal safety evaluation model designed to assess content safety across text, images, audio, and video. Built on the Qwen2.5-Omni architecture, it provides structured safety reasoning and policy enforcement.
## Model Information
- **Model Name**: OmniGuard-7B
- **Base Model**: Qwen2.5-Omni-7B
- **Model Type**: Multimodal Safety Moderation
- **Supported Languages**: English, Chinese
- **Supported Modalities**: Text, Image, Audio, Video
- **License**: Apache-2.0
## Model Variants
We provide two model sizes:
- **[OmniGuard-7B](https://huggingface.co/anonymous-omniguard/OmniGuard-7B)** — 7B parameters for high-accuracy safety evaluation
- **[OmniGuard-3B](https://huggingface.co/anonymous-omniguard/OmniGuard-3B)** — 3B parameters for resource-constrained environments
## Features
- **Omni-Modal Safety Assessment**: Evaluate safety across text, images, audio, and video inputs
- **Structured Reasoning**: Provides deliberate, structured safety analysis beyond binary classification
## Installation
### Quick Setup
```bash
# Install core dependencies
pip install torch transformers accelerate
# Install Qwen2.5-Omni support
pip uninstall transformers -y
pip install git+https://github.com/huggingface/transformers@v4.51.3-Qwen2.5-Omni-preview
```
### Requirements
- Python 3.8+
- PyTorch 2.0+
- CUDA-compatible GPU (recommended)
- 16GB+ GPU memory for 7B model
- Flash Attention 2 (optional, for better performance)
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "anonymous-omniguard/OmniGuard-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
attn_implementation="flash_attention_2" # Recommended
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Text-only safety evaluation
messages = [
{"role": "user", "content": "Please analyze the safety of this content: [Your content here]"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=False
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Ethical Considerations
- This model is designed for safety evaluation and content moderation
- Should be used as part of a comprehensive safety strategy
- May reflect biases present in training data
- Continuous monitoring and updates recommended for production use
- Users are responsible for compliance with applicable laws and regulations
## License
This model is released under the Apache-2.0 license.
## Acknowledgments
- Built on [Qwen2.5-Omni](https://github.com/QwenLM/Qwen2.5-Omni) by Alibaba Cloud
- Uses [LLaMA Guard 3](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) safety framework
- Trained on diverse multimodal safety datasets
## Support
For issues, questions, or contributions:
- Repository: https://github.com/anonymous-2654a/neurips-2026-sub
- HuggingFace: https://huggingface.co/anonymous-omniguard
---
*Anonymous release accompanying a paper currently under peer review.*

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{% set audio_count = namespace(value=0) %}{% 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
You are a helpful assistant.<|im_end|>
{% endif %}<|im_start|>{{ message['role'] }}
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
{% 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_bos|><|IMAGE|><|vision_eos|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<|audio_bos|><|AUDIO|><|audio_eos|>{% 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_bos|><|VIDEO|><|vision_eos|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
{% endif %}

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