---
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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## How to Get Started with the Model

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## Training Details

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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