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 — 7B parameters for high-accuracy safety evaluation
- 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
# 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
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 by Alibaba Cloud
- Uses LLaMA Guard 3 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.