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anonymous-omniguard/
OmniGuard-7B

Name: OmniGuard-7B
Rating: 2.5 (104 reviews)

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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

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 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.