8B Generative Safety Moderation Model
🌐 Website • 🤗 Hugging Face • 📄 Paper • 📖 Documentation
--- ## Introduction **Zen Guard Gen** is an 8B parameter generative safety classification model for comprehensive prompt and response moderation. It's the larger variant of the Zen Guard family, providing highest accuracy for batch processing scenarios. ## Features - 🛡️ **8B Parameters**: Maximum accuracy for safety classification - 🌍 **119 Languages**: Multilingual safety moderation - 🚦 **Three-Tier Classification**: Safe, Controversial, Unsafe - 📊 **9 Safety Categories**: Comprehensive content analysis - ⚡ **120ms Latency**: Optimized for batch processing ## Model Specifications | Specification | Value | |---------------|-------| | Parameters | 8B | | Type | Generative | | Base Model | Zen Guard | | Context Length | 32,768 tokens | | Languages | 119 | | Latency | ~120ms | | VRAM (FP16) | 16GB | | VRAM (INT8) | 8GB | ## Quick Start ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "zenlm/zen-guard-gen" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # Prompt moderation prompt = "How do I learn programming?" 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) print(result) # Output: Safety: Safe # Categories: None # Response moderation response = "Here's a Python tutorial..." messages = [ {"role": "user", "content": prompt}, {"role": "assistant", "content": response} ] 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) print(result) # Output: Safety: Safe # Categories: None # Refusal: No ``` ## Deployment ```bash # SGLang python -m sglang.launch_server --model-path zenlm/zen-guard-gen --port 30000 # vLLM vllm serve zenlm/zen-guard-gen --port 8000 --max-model-len 32768 ``` ## Performance | Metric | Zen Guard Gen | |--------|---------------| | Accuracy | 96.8% | | F1 Score | 94.2% | | False Positive | 2.1% | ## Related Models - [zen-guard](https://huggingface.co/zenlm/zen-guard) - 4B base model - [zen-guard-stream](https://huggingface.co/zenlm/zen-guard-stream) - 4B streaming model ## License Apache 2.0 ## Citation ```bibtex @misc{zenguardgen2025, title={Zen Guard Gen: 8B Generative Safety Moderation}, author={Hanzo AI and Zoo Labs Foundation}, year={2025}, publisher={HuggingFace}, howpublished={\url{https://huggingface.co/zenlm/zen-guard-gen}} } ``` tags: - zen - zenlm ---
Zen AI - Clarity Through Intelligence
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