Instructions to use XChava/cyber-ken-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use XChava/cyber-ken-v3-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="XChava/cyber-ken-v3-GGUF", filename="cyber-ken-v3-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use XChava/cyber-ken-v3-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf XChava/cyber-ken-v3-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf XChava/cyber-ken-v3-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf XChava/cyber-ken-v3-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf XChava/cyber-ken-v3-GGUF: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 XChava/cyber-ken-v3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf XChava/cyber-ken-v3-GGUF: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 XChava/cyber-ken-v3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf XChava/cyber-ken-v3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/XChava/cyber-ken-v3-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use XChava/cyber-ken-v3-GGUF with Ollama:
ollama run hf.co/XChava/cyber-ken-v3-GGUF:Q4_K_M
- Unsloth Studio new
How to use XChava/cyber-ken-v3-GGUF 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 XChava/cyber-ken-v3-GGUF 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 XChava/cyber-ken-v3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for XChava/cyber-ken-v3-GGUF to start chatting
- Pi new
How to use XChava/cyber-ken-v3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf XChava/cyber-ken-v3-GGUF:Q4_K_M
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": "XChava/cyber-ken-v3-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use XChava/cyber-ken-v3-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf XChava/cyber-ken-v3-GGUF:Q4_K_M
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 XChava/cyber-ken-v3-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use XChava/cyber-ken-v3-GGUF with Docker Model Runner:
docker model run hf.co/XChava/cyber-ken-v3-GGUF:Q4_K_M
- Lemonade
How to use XChava/cyber-ken-v3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull XChava/cyber-ken-v3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.cyber-ken-v3-GGUF-Q4_K_M
List all available models
lemonade list
cyber-ken-v3 GGUF
Cybersecurity + tool-calling specialist. Fine-tuned from Qwopus3.5-9B on 153K examples.
Quick Start (Ollama)
ollama run hf.co/XChava/cyber-ken-v3-GGUF:Q4_K_M
ollama run hf.co/XChava/cyber-ken-v3-GGUF:Q5_K_M
ollama run hf.co/XChava/cyber-ken-v3-GGUF:Q8_0
Quant Sizes
| Quant | Size | Min VRAM | Quality |
|---|---|---|---|
| Q4_K_M | ~5.4 GB | 6 GB | Good |
| Q5_K_M | ~6.5 GB | 8 GB | Better |
| Q8_0 | ~9.5 GB | 11 GB | Best |
Tool Call Format
<tool_call>
{"name": "subfinder", "arguments": {"domain": "example.com"}}
</tool_call>
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Hardware compatibility
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Model tree for XChava/cyber-ken-v3-GGUF
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B Finetuned
unsloth/Qwen3.5-9B Adapter
Jackrong/Qwopus3.5-9B-v3