The nopenet/nope-edge-mini is a 1.7 billion parameter language model developed by NopeNet, fine-tuned for detecting crisis signals in text. Built on the Qwen3-1.7B base model, it specializes in identifying suicidal ideation, self-harm, abuse, and violence. This model provides detailed XML outputs including chain-of-thought reflections and structured risk classifications, making it suitable for high-volume, cost-sensitive safety-critical applications.
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Overview
nopenet/nope-edge-mini is a specialized 1.7 billion parameter language model developed by NopeNet, designed for crisis classification in text. It is fine-tuned to identify and categorize safety-critical content such as suicidal ideation, self-harm, abuse, and violence. The model provides structured XML outputs, including a <reflection> for chain-of-thought reasoning and <risks> for detailed classification with attributes like subject, type, severity, and imminence.
Key Capabilities
- Crisis Detection: Identifies various crisis types including suicide, self-harm, violence, abuse, and exploitation.
- Chain-of-Thought Reasoning: Explains its classifications through a "reflection" component, detailing detected signals and contextual factors.
- Structured Output: Provides machine-readable XML with specific risk attributes for easy parsing and integration.
- Optimized for Volume: As the "mini" variant, it's designed for high-volume and cost-sensitive applications, requiring ~4GB VRAM.
Good For
- Content Moderation: Automatically flagging safety-critical text in user-generated content.
- Mental Health Support Systems: Identifying users expressing distress or at risk.
- Research and Academic Use: Analyzing large datasets for crisis signals.
- Early Warning Systems: Detecting potential harm in online communications.