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GLiGuard is our open-source small language model for safety moderation. At 300 million parameters, it acts as a safety layer between the user and a model, screening both prompts and responses for harmful content. Built on the GLiNER2 architecture, it reframes moderation as a classification problem and scores every safety dimension in a single forward pass, matching the accuracy of guard models 23 to 90 times its size while running up to 16 times faster.

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Inference GLiGuard in Pioneer

POST /v1/chat/completions Runs GLiGuard over the supplied messages and returns a classification for each task defined in schema. Pioneer exposes an OpenAI-compatible endpoint at https://api.pioneer.ai/v1, so you call GLiGuard through the standard chat completions route using the model ID fastino/gliguard-LLMGuardrails-300M. Request body
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string
required
The GLiGuard model ID: fastino/gliguard-LLMGuardrails-300M.
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object[]
required
The text to moderate, in standard OpenAI chat format.
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object
required
The classification schema. Contains a classifications array, where each object defines one moderation task with a task name (see the task table below), a set of candidate labels, a multi_label flag, and a confidence threshold. The example runs a single prompt_safety task with labels safe and unsafe, multi_label: false, and threshold: 0.5.
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boolean
Return a confidence score per label. Set to true in the example.

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Example: safety classification

This request runs the safety task on a single user message.

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Running the other moderation tasks

The example above runs a single prompt_safety task. GLiGuard supports all of the moderation tasks listed in the task table below, and can evaluate several in one pass by adding more entries to schema.classifications.

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What GLiGuard moderates

GLiGuard supports both prompt-side and response-side moderation, covering binary safety, harm categorization, jailbreak detection, and refusal classification. You compose these as tasks in a single request, and the model scores all of them in one pass.
  • Single-label tasks (prompt_safety, response_safety, response_refusal) return one label.
  • Multi-label tasks (prompt_toxicity, response_toxicity, jailbreak_detection) can return several labels at once.

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Labels

Each task scores the input against a fixed label set:
  • Safety (prompt_safety, response_safety): safe, unsafe
  • Refusal (response_refusal): refusal, compliance
  • Harm categories (prompt_toxicity, response_toxicity): violence_and_weapons, non_violent_crime, sexual_content, hate_and_discrimination, self_harm_and_suicide, pii_exposure, misinformation, copyright_violation, child_safety, political_manipulation, unethical_conduct, regulated_advice, privacy_violation, other, benign
  • Jailbreak strategies (jailbreak_detection): prompt_injection, jailbreak_attempt, policy_evasion, instruction_override, system_prompt_exfiltration, data_exfiltration, roleplay_bypass, hypothetical_bypass, obfuscated_attack, multi_step_attack, social_engineering, benign