Instructions to use geoffmunn/Qwen3Guard-Gen-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use geoffmunn/Qwen3Guard-Gen-0.6B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="geoffmunn/Qwen3Guard-Gen-0.6B",
	filename="Qwen3Guard-Gen-0.6B-f16:Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use geoffmunn/Qwen3Guard-Gen-0.6B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Use Docker

docker model run hf.co/geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

LM Studio
Jan

vLLM

How to use geoffmunn/Qwen3Guard-Gen-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "geoffmunn/Qwen3Guard-Gen-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "geoffmunn/Qwen3Guard-Gen-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Ollama
How to use geoffmunn/Qwen3Guard-Gen-0.6B with Ollama:
```
ollama run hf.co/geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
```

Unsloth Studio

How to use geoffmunn/Qwen3Guard-Gen-0.6B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for geoffmunn/Qwen3Guard-Gen-0.6B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for geoffmunn/Qwen3Guard-Gen-0.6B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for geoffmunn/Qwen3Guard-Gen-0.6B to start chatting

Docker Model Runner
How to use geoffmunn/Qwen3Guard-Gen-0.6B with Docker Model Runner:
```
docker model run hf.co/geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M
```

Lemonade

How to use geoffmunn/Qwen3Guard-Gen-0.6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull geoffmunn/Qwen3Guard-Gen-0.6B:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3Guard-Gen-0.6B-Q4_K_M

List all available models

lemonade list

Qwen3Guard-Gen-0.6B-GGUF

This is a GGUF-quantized version of 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 – load and test locally
OpenWebUI – deploy with RAG and tools
GPT4All – private, offline AI chatbot
Directly via llama.cpp, Ollama, or TGI

Author

👤 Geoff Munn (@geoffmunn)
🔗 Hugging Face Profile

Disclaimer

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

Downloads last month: 50

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for geoffmunn/Qwen3Guard-Gen-0.6B

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

Qwen/Qwen3Guard-Gen-0.6B

Quantized

(6)

this model