---
license: apache-2.0
tags:
  - gguf
  - qwen
  - safety
  - guardrail
  - text-generation
  - tiny-llm
  - llama.cpp
base_model: Qwen/Qwen3Guard-Gen-0.6B
author: geoffmunn
pipeline_tag: text-generation
---

# Qwen3Guard-Gen-0.6B-GGUF

This is a **GGUF-quantized version** of **[Qwen3Guard-Gen-0.6B](https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B)**, a **tiny yet safety-aligned generative model** from Alibaba's Qwen team.

At just ~0.6B parameters, this model is optimized for:
- Ultra-fast inference
- Low-memory environments (phones, Raspberry Pi, embedded)
- Real-time filtering and response generation
- Privacy-first apps where small size matters

> ⚠️ This is a **generative model with built-in safety constraints**, designed to refuse harmful requests while running efficiently on-device.

## 🛡 What Is Qwen3Guard-Gen-0.6B?

It’s a **compact helpful assistant** trained to:
- Respond helpfully to simple queries
- Politely decline unsafe ones (e.g., illegal acts, self-harm)
- Avoid generating toxic content
- Run completely offline with minimal resources

Perfect for:
- Mobile AI assistants
- IoT devices
- Edge computing
- Fast pre-filter + response pipelines
- Educational tools on low-end hardware

## 🔗 Relationship to Other Safety Models

Part of the full Qwen3 safety stack:

| Model | Size | Role |
|------|------|------|
| **Qwen3Guard-Gen-0.6B** | 🟢 Tiny | Lightweight safe generator |
| **Qwen3Guard-Stream-4B/8B** | 🟡 Medium/Large | Streaming input filter |
| **Qwen3Guard-Gen-4B/8B** | 🟡 Large | High-quality safe generation |
| **Qwen3-4B-SafeRL** | 🟡 Large | Fully aligned ethical agent |

### Recommended Architecture
```
User Input
    ↓
[Optional: Qwen3Guard-Stream-4B] ← optional pre-filter
    ↓
[Qwen3Guard-Gen-0.6B]
    ↓
Fast, Safe Response
```

Use this when you need **speed and privacy over deep reasoning**.

## Available Quantizations

| Level   | Size  | RAM Usage | Use Case |
|--------|-------|-----------|----------|
| Q2_K   | ~0.45 GB | ~0.6 GB | Only on very weak devices |
| Q3_K_S | ~0.52 GB | ~0.7 GB | Minimal viability |
| Q3_K_M | ~0.59 GB | ~0.8 GB | Basic chat on microcontrollers |
| Q4_K_S | ~0.68 GB | ~0.9 GB | Good for edge devices |
| Q4_K_M | ~0.75 GB | ~1.0 GB | ✅ Best balance for most users |
| Q5_K_S | ~0.73 GB | ~0.95 GB | Slightly faster than Q5_K_M |
| Q5_K_M | ~0.75 GB | ~1.0 GB | ✅✅ Top quality for tiny model |
| Q6_K   | ~0.85 GB | ~1.1 GB | Near-original fidelity |
| Q8_0   | ~1.10 GB | ~1.3 GB | Maximum accuracy (research) |

> 💡 **Recommendation**: Use **Q4_K_M** or **Q5_K_M** for best trade-off between speed and safety reliability.

## Tools That Support It
- [LM Studio](https://lmstudio.ai) – load and test locally
- [OpenWebUI](https://openwebui.com) – deploy with RAG and tools
- [GPT4All](https://gpt4all.io) – private, offline AI chatbot
- Directly via `llama.cpp`, Ollama, or TGI

## Author
👤 Geoff Munn (@geoffmunn)  
🔗 [Hugging Face Profile](https://huggingface.co/geoffmunn)

## Disclaimer
Community conversion for local inference. Not affiliated with Alibaba Cloud.