### Key Technical Specifications

#### Model Architecture
Built on the GPT-OSS architecture with 20B total parameters. Fine-tuned specifically for safety classification tasks with support for the Harmony response format, which separates reasoning into dedicated channels for auditability and transparency.

#### Performance Metrics
GPT-OSS-Safeguard is designed to interpret and enforce written policies:
* Policy-following model that reliably interprets custom safety standards
* Harmony format for structured reasoning with low/medium/high reasoning effort
* Handles nuanced content with explicit reasoning explanations
* Adapts to contextual factors without retraining

### Key Use Cases

#### Trust & Safety Content Moderation
Classify posts, messages, or media metadata for policy violations with nuanced, context-aware decision-making. Integrates with real-time ingestion pipelines, review queues, and moderation consoles.

#### Policy-Based Classification
Use your written policies as governing logic for content decisions. Update or test new policies instantly without model retraining, enabling rapid iteration on safety standards.

#### Automated Triage & Moderation Assistant
Acts as a reasoning agent that evaluates content, explains decisions, cites specific policy rules, and surfaces cases requiring human judgment to reduce moderator cognitive load.

#### Policy Testing & Experimentation
Simulate how content will be labeled before rolling out new policies. A/B test alternative definitions in production and identify overly broad rules or unclear examples.

### Best Practices

* Structure policy prompts with four sections: Instructions, Definitions, Criteria, and Examples for optimal performance.
* Keep policies between 400-600 tokens for best results.
* Place static content (policies, definitions) first and dynamic content (user queries) last to optimize for prompt caching.
* Require explicit output formats with rationales and policy citations for maximum reasoning transparency.
* Use low reasoning effort for simple classifications and high effort for complex, nuanced decisions.

### Get Started with GPT-OSS-Safeguard 20B
Experience `openai/gpt-oss-safeguard-20b` on Groq:

Example Output
```json
{
  "violation": 1,
  "category": "Direct Override",
  "rationale": "The input explicitly attempts to override system instructions by introducing the 'DAN' persona and requesting unrestricted behavior, which constitutes a clear prompt injection attack."
}
```

---

## Mistral Saba 24b: Model (tsx)