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Model: anonymous-omniguard/OmniGuard-7B
Source: Original Platform

README.md

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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.*