File size: 3,486 Bytes
b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 b0d2ed9 af76660 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | ---
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-3B
- **Base Model**: Qwen2.5-Omni-3B
- **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-3B](https://huggingface.co/anonymousICML/OmniGuard-3B)** - 3B parameters for high-accuracy safety evaluation
- **[OmniGuard-3B](https://huggingface.co/anonymousICML/OmniGuard-7B)** - 7B 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)
- 8GB+ GPU memory for 3B model
- Flash Attention 2 (optional, for better performance)
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "anonymousICML/OmniGuard-3B"
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/icml-2026-sub
- HuggingFace: https://huggingface.co/anonymousICML
---
**Note**: This is an anonymous submission for ICML 2026. Full details will be released upon acceptance.
|