library_name: transformers
license: apache-2.0
Introducing the GA Guard series — a family of open-weight moderation models built to help developers and organizations keep language models safe, compliant, and aligned with real-world use.
GA-Guard is designed to detect violations across the following seven categories:
- Illicit Activities – instructions or content related to crimes, weapons, or illegal substances.
- Hate & Abuse – harassment, slurs, dehumanization, or abusive language.
- PII & IP – exposure or solicitation of sensitive personal information, secrets, or intellectual property.
- Prompt Security – jailbreaks, prompt-injection, secret exfiltration, or obfuscation attempts.
- Sexual Content – sexually explicit or adult material.
- Misinformation – demonstrably false or deceptive claims presented as fact.
- Violence & Self-Harm – content that encourages violence, self-harm, or suicide.
The model outputs a structured token for each category (e.g., <policy_violation> or <policy_not_violation>).
Model Details
GA Guard Core features:
- Type: Causal Language Model
- Training: Full finetune
- Number of Parameters: 4.0B
- Number of Non-Embedding Parameters: 3.6B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 262,144 tokens
Model Description
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Uses
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Evaluation
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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