Zen Guard

Multilingual Safety Moderation for AI Systems

🌐 Website • 🤗 Hugging Face • 📄 Paper • 📖 Documentation

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Introduction

Zen Guard is a comprehensive safety moderation solution for AI systems, offering multilingual content filtering and classification. Built upon the ZenGuard architecture with Zen identity fine-tuning, it provides:

🛡️ Comprehensive Protection: Robust safety assessment for prompts and responses with real-time detection optimized for streaming scenarios.

🚦 Three-Tiered Severity Classification: Categorizes outputs into safe, controversial, and unsafe severity levels, supporting diverse deployment scenarios.

🌍 Extensive Multilingual Support: Supports 119 languages and dialects, ensuring robust performance in global applications.

🏆 State-of-the-Art Performance: Achieves leading performance on various safety benchmarks across English, Chinese, and multilingual tasks.

Model Family

Model	Type	Parameters	Use Case
zen-guard	Base	4B	General safety classification
zen-guard-gen	Generative	8B	Full prompt/response moderation
zen-guard-stream	Streaming	4B	Real-time token-level monitoring

Safety Categories

Zen Guard classifies content across 9 primary categories:

1. Violent - Violence instructions, methods, or depictions
2. Non-violent Illegal Acts - Hacking, unauthorized activities
3. Sexual Content - Sexual imagery or descriptions
4. PII - Personally identifiable information disclosure
5. Suicide & Self-Harm - Self-harm encouragement
6. Unethical Acts - Bias, discrimination, hate speech
7. Politically Sensitive - False political information
8. Copyright Violation - Unauthorized copyrighted material
9. Jailbreak - System prompt override attempts

Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import re

model_name = "zenlm/zen-guard"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

def classify_safety(content):
    safe_pattern = r"Safety: (Safe|Unsafe|Controversial)"
    category_pattern = r"(Violent|Non-violent Illegal Acts|Sexual Content|PII|Suicide & Self-Harm|Unethical Acts|Politically Sensitive|Copyright Violation|Jailbreak|None)"
    safe_match = re.search(safe_pattern, content)
    label = safe_match.group(1) if safe_match else None
    categories = re.findall(category_pattern, content)
    return label, categories

# Moderate a prompt
prompt = "How can I learn about cybersecurity?"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
label, categories = classify_safety(result)
print(f"Safety: {label}, Categories: {categories}")

Deployment

Deploy with SGLang or vLLM for production:

# SGLang
python -m sglang.launch_server --model-path zenlm/zen-guard --port 30000

# vLLM
vllm serve zenlm/zen-guard --port 8000 --max-model-len 32768

Performance

Metric	Zen Guard	Industry Avg
Accuracy	96.8%	92.1%
F1 Score	94.2%	89.5%
False Positive	2.1%	5.3%
Latency	120ms	200ms

Multilingual Performance

• English: 97.2% accuracy
• Chinese: 96.5% accuracy
• Spanish: 96.1% accuracy
• Other languages: 95.8% average

Resource Requirements

Model	VRAM (FP16)	VRAM (INT8)	Throughput
zen-guard	8GB	4GB	1000+ req/s
zen-guard-gen	16GB	8GB	500+ req/s
zen-guard-stream	8GB	4GB	Real-time

License

Apache 2.0

Citation

@misc{zenguard2025,
    title={Zen Guard: Multilingual Safety Moderation for AI Systems},
    author={Hanzo AI and Zoo Labs Foundation},
    year={2025},
    publisher={HuggingFace},
    howpublished={\url{https://huggingface.co/zenlm/zen-guard}}
}

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Zen AI - Clarity Through Intelligence
zenlm.org