Instructions to use Soham308/CyberGuard-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Soham308/CyberGuard-Model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Soham308/CyberGuard-Model",
	filename="Qwen3.5-4B-UD-Q4_K_XL.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Soham308/CyberGuard-Model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Soham308/CyberGuard-Model:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Soham308/CyberGuard-Model:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

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 Soham308/CyberGuard-Model:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

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 Soham308/CyberGuard-Model:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

Use Docker

docker model run hf.co/Soham308/CyberGuard-Model:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use Soham308/CyberGuard-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Soham308/CyberGuard-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Soham308/CyberGuard-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Soham308/CyberGuard-Model:UD-Q4_K_XL

Ollama
How to use Soham308/CyberGuard-Model with Ollama:
```
ollama run hf.co/Soham308/CyberGuard-Model:UD-Q4_K_XL
```

Unsloth Studio

How to use Soham308/CyberGuard-Model 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 Soham308/CyberGuard-Model 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 Soham308/CyberGuard-Model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Soham308/CyberGuard-Model to start chatting

How to use Soham308/CyberGuard-Model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Soham308/CyberGuard-Model:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Soham308/CyberGuard-Model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Soham308/CyberGuard-Model:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Soham308/CyberGuard-Model:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Soham308/CyberGuard-Model with Docker Model Runner:
```
docker model run hf.co/Soham308/CyberGuard-Model:UD-Q4_K_XL
```

Lemonade

How to use Soham308/CyberGuard-Model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Soham308/CyberGuard-Model:UD-Q4_K_XL

Run and chat with the model

lemonade run user.CyberGuard-Model-UD-Q4_K_XL

List all available models

lemonade list

Qwen3.5 4B GGUF (Quantized)

This repository provides a GGUF quantized version of the original Qwen3.5 4B model, optimized for efficient local inference using tools like llama.cpp, LM Studio, and similar runtimes.

🔗 Base Model

This model is derived from the official:

👉 https://huggingface.co/Qwen/Qwen3.5-4B (by Alibaba / Qwen Team)

Please refer to the original model for full details, training methodology, benchmarks, and licensing terms.

⚙️ Quantization Details

Format: GGUF
Quantization: Q4_K_XL
Size: ~2.9 GB
Architecture: Qwen3.5

This version is designed to balance performance and memory efficiency, making it suitable for local deployments.

📦 Quantization Source

This GGUF file is sourced from:

👉 https://huggingface.co/unsloth/Qwen3.5-4B-GGUF

Specifically:

Qwen3.5-4B-UD-Q4_K_XL.gguf

All credit for quantization goes to the original uploader (Unsloth).

🚀 Usage

You can run this model locally using:

llama.cpp

./main -m qwen3.5-4b-q4_k_xl.gguf -p "Explain SQL injection"

Other tools

LM Studio
KoboldCpp
Ollama

💡 Example Use Cases

General-purpose chat
Coding assistance
Technical explanations
Integration into custom AI systems (e.g., agents, tools)

🧪 Tested With

Local inference (CPU/GPU hybrid)
Integration with external tools (web search, reasoning pipelines)

⚠️ Disclaimer

This is not an original model.
Behavior and capabilities are inherited from the base Qwen3.5 model.

📜 License

Please follow the license of the original Qwen model.

🙌 Acknowledgements

Qwen Team (Alibaba) — Base model
Unsloth — GGUF quantization
llama.cpp — GGUF runtime support

🌐 Related Project

This model is used in:

👉 CyberGuard AI (Cybersecurity assistant system)

Hosted on huggingface spaces

Downloads last month: 11

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for Soham308/CyberGuard-Model

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(238)

this model