Instructions to use ghostai1/GHOSTAI-Spooky with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ghostai1/GHOSTAI-Spooky with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ghostai1/GHOSTAI-Spooky:Q4_K_M # Run inference directly in the terminal: llama cli -hf ghostai1/GHOSTAI-Spooky:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ghostai1/GHOSTAI-Spooky:Q4_K_M # Run inference directly in the terminal: llama cli -hf ghostai1/GHOSTAI-Spooky: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 ghostai1/GHOSTAI-Spooky:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ghostai1/GHOSTAI-Spooky: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 ghostai1/GHOSTAI-Spooky:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ghostai1/GHOSTAI-Spooky:Q4_K_M
Use Docker
docker model run hf.co/ghostai1/GHOSTAI-Spooky:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ghostai1/GHOSTAI-Spooky with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ghostai1/GHOSTAI-Spooky" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ghostai1/GHOSTAI-Spooky", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ghostai1/GHOSTAI-Spooky:Q4_K_M
- Ollama
How to use ghostai1/GHOSTAI-Spooky with Ollama:
ollama run hf.co/ghostai1/GHOSTAI-Spooky:Q4_K_M
- Unsloth Desktop
- Pi
How to use ghostai1/GHOSTAI-Spooky with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ghostai1/GHOSTAI-Spooky:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ghostai1/GHOSTAI-Spooky:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ghostai1/GHOSTAI-Spooky with Docker Model Runner:
docker model run hf.co/ghostai1/GHOSTAI-Spooky:Q4_K_M
- Lemonade
How to use ghostai1/GHOSTAI-Spooky with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ghostai1/GHOSTAI-Spooky:Q4_K_M
Run and chat with the model
lemonade run user.GHOSTAI-Spooky-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ghostai1/GHOSTAI-Spooky with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ghostai1/GHOSTAI-Spooky: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 ghostai1/GHOSTAI-Spooky:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ghostai1/GHOSTAI-Spooky with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ghostai1/GHOSTAI-Spooky:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ghostai1/GHOSTAI-Spooky:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Commit History
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Verified signature### Q6_K (GHOSTAI Horror 7B) — Very High Quality Near-top quality while still quantized. Heavier file; slower and more RAM-hungry than Q5. Best for: higher-end machines, longer sessions, and users who prioritize quality.f4f7c2b -
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Verified signature### Q5_K_M (GHOSTAI Horror 7B) — High Quality / Stable Noticeably better fidelity than Q4 with increased RAM/VRAM requirements. Best for: desktops with comfortable memory budgets and users who want stronger outputs.3fcea35 -
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Verified signature### Q5_K_S (GHOSTAI Horror 7B) — High Quality / Lean Higher quality than Q4, slightly smaller/faster than Q5_K_M, but still heavier than Q4. Best for: users who want better quality without going all the way to Q6/Q8.f4c10e5 -
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Verified signature### Q4_K_S (GHOSTAI Horror 7B) — Mid / Lean Slightly smaller than Q4_K_M and a bit faster, with a small quality hit. Best for: when Q4_K_M barely doesn’t fit or you want a lighter Q4.b90c055 -
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Verified signature### Q3_K_M (GHOSTAI Horror 7B) — Small / Better Quality A strong “low memory but still usable” option. Better quality than Q3_K_S. Best for: older machines or small VRAM offload setups.717b06f -
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Verified signature### Q3_K_S (GHOSTAI Horror 7B) — Small / Speed-Oriented Smaller and faster than Q3_K_M with a bit more quality loss. Best for: tight RAM budgets where you still want usable chat quality.c0569a8 -
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Verified signature### Q2_K (GHOSTAI Horror 7B) — Low RAM Fallback Very small and generally more stable than TQ1_0, but still a big quality drop vs Q4+. Best for: low-RAM machines where Q3/Q4 won’t fit.3cec767 -
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Verified signature### TQ1_0 (GHOSTAI Horror 7B) — Ultra-Small / Experimental Smallest file in the release. Intended for extreme low-RAM setups or quick testing. Expect noticeably reduced instruction-following, weaker reasoning, and more hallucinations vs Q4/Q5. Best for: demos, mobile/low-resource boxes, “does it run?” checks.0b84360 -
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