Model overview
qwen3guard-gen-4b is a compact safety and content moderation model built by ditto--ai that classifies text as Safe, Unsafe, or Controversial. With just 4 billion parameters, this model delivers efficient moderation across 119 languages, making it practical for integration into production systems. Unlike larger alternatives like Llama-Guard-3-8B, the model's smaller size enables faster inference while maintaining strong classification performance. It handles both prompt and response moderation with fine-grained category detection and can identify when content triggers refusal patterns in AI assistants.
Model inputs and outputs
The model accepts user prompts and optionally assistant responses, returning structured safety classifications with detailed category information. This dual-input capability allows you to moderate both what users request and what models generate, providing comprehensive safety coverage for conversational systems.
Inputs
- Prompt: The user message to moderate (required)
- Response: The assistant response to moderate (optional, enables response-level safety checks)
- Max New Tokens: Maximum generation length, range 1-256 tokens (default: 128)
Outputs
- Safety Label: Classification as Safe, Unsafe, or Controversial
- Categories: Fine-grained labels identifying specific content risks
- Refusal: Detected refusal patterns in responses (when applicable)
Capabilities
The model classifies content with precision across multiple safety dimensions. It detects harmful requests in prompts while simultaneously identifying when assistant responses fail to handle unsafe content properly. The refusal detection capability catches instances where AI systems refuse requests, helping you understand system behavior patterns. Support for 119 languages means moderation works globally without needing separate models per language.
What can I use it for?
Deploy this model to moderate user-generated content on community platforms, chat applications, and AI services. Content platforms can screen submissions before publication to reduce harmful material. AI service providers can monitor both user inputs and model outputs to maintain safety standards. Companies building chatbots can integrate this model to filter unsafe prompts before processing. The efficient 4B parameter size makes it suitable for real-time moderation in high-volume systems where latency matters. You can also use it to audit existing conversations or build safety reports across user interactions.
Things to try
Test the model on borderline cases that sit between clearly safe and obviously harmful content to understand where it draws lines. Run multilingual content through it to verify safety classification consistency across languages. Compare classifications of the same harmful intent expressed different ways to see how semantic variations affect detection. Pass refusal detection on problematic assistant responses to identify safety gaps in your own systems. Try pairing it with prompt-classifier outputs to see how different moderation approaches rank the same content, which helps calibrate your safety thresholds.
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