Guardion-1-8B Model Card
Guardion-1-8B is a pruned and quantized version of OpenAI gpt-oss-safeguard-20b, optimized for lower latency and local or specialized deployment while retaining 96% of the original model’s quality. The base modelgpt-oss-safeguard-20b is a 21B-parameter safety judge with 3.6B active parameters, designed for evaluating LLM outputs across safety, grounding, and policy compliance tasks. Guardion-1-8B distills this into a compact 8B-parameter model with 2.7B active parameters — making it suitable for on-premise, edge, and latency-sensitive use cases.
This is the model page for the Grounding and Custom guardrails. See the guardrail overviews in Grounding and Custom.
Overview
- Base model: OpenAI gpt-oss-safeguard-20b (21B params, 3.6B active)
- Parameters: 8 billion (2.7B active)
- Optimization: Pruned and quantized, retaining 96% quality
- Architecture: Decoder-only transformer
- License: Apache 2.0
- Developed by: Guardion AI
Key Features
- Multilingual — cross-lingual safety evaluation across diverse languages
- Custom policies — bring your own judging policies without additional training
- LoRA-compatible — suitable for LoRA adapters for task-specific fine-tuning
- Built-in safety policies — Aegis 2.0 taxonomy, RAG hallucination, function calling validation
Built-in Safety Policies
The model ships with built-in support for the Aegis 2.0 safety taxonomy: Core safety policies:
Fine-grained policies:
RAG policies:
Agentic workflow policies:
Available Versions
- guardion-1-8b — current stable version
Benchmark Results
RAG Hallucination — TRUE Benchmark (Balanced Accuracy)
Measures faithfulness of LLM responses to provided context.Function Calling Hallucination (Balanced Accuracy)
Evaluated on the FC Reward Bench dataset for detecting hallucinations in agentic tool-calling workflows.How to Use Guardion-1-8B
Combine Guardion-1-8B with a guard policy, then evaluate with that policy.Example: Grounding check
Related
- Grounding guardrail — configure policies for hallucination detection: /grounding
- Custom guardrail — define your own policies: /custom