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Unlike standard LLMs, this model has been fine-tuned to avoid harmful, deceptive, or unethical behavior — making it ideal for sensitive applications like education, mental health, and customer service.
| Model | Role | Best For |
|---|---|---|
| Qwen3Guard-Stream-4B | ⚡ Input filter | Real-time moderation of user input |
| Qwen3Guard-Gen-4B | 🧠 Safe generator | Output-safe generation without alignment |
| Qwen3-4B-SafeRL | 🤝 Fully aligned agent | Ethical, multi-turn conversations |
User Input
↓
[Optional: Qwen3Guard-Stream-4B] ← optional pre-filter
↓
[Qwen3-4B-SafeRL]
↓
Aligned Response| Level | Size | RAM Usage | Use Case |
|---|---|---|---|
| Q2_K | ~1.8 GB | ~2.0 GB | Only on weak hardware |
| Q3_K_S | ~2.1 GB | ~2.3 GB | Minimal viability |
| Q4_K_M | ~2.8 GB | ~3.0 GB | ✅ Balanced choice |
| Q5_K_M | ~3.1 GB | ~3.3 GB | ✅✅ Highest quality |
| Q6_K | ~3.5 GB | ~3.8 GB | Near-FP16 fidelity |
| Q8_0 | ~4.5 GB | ~5.0 GB | Maximum accuracy |
💡 Recommendation: Use Q5_K_M for best balance of ethical reasoning and response quality.
llama.cpp, Ollama, or TGI