Instructions to use ghostai1/GHOSTAI-Spooky with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghostai1/GHOSTAI-Spooky with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ghostai1/GHOSTAI-Spooky",
	filename="ghostai-horror-7b.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 ghostai1/GHOSTAI-Spooky with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio new

How to use ghostai1/GHOSTAI-Spooky 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 ghostai1/GHOSTAI-Spooky 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 ghostai1/GHOSTAI-Spooky to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghostai1/GHOSTAI-Spooky to start chatting

Pi new

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-server -hf ghostai1/GHOSTAI-Spooky: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": "ghostai1/GHOSTAI-Spooky:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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

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

GHOSTAI — HORROR GGUF (7B)
A focused, horror-themed 7B model released exclusively in quantized GGUF format for the llama.cpp ecosystem.
_{Quantized-only release. No FP16 weights included.}

Overview

GHOSTAI is a compact, atmosphere-driven horror model designed for narrative generation, roleplay, and dark storytelling.
It prioritizes tone, pacing, and vivid imagery over generic assistant behavior.

This repository provides multiple GGUF quantizations, allowing you to choose the best balance of quality, speed, and memory usage for your hardware.

The model runs:

Fully on CPU
With optional GPU offload (CUDA / Metal / Vulkan builds of llama.cpp)

Quantization choice is independent of whether you use CPU or GPU.

Files

File	Quant	Approx size	Rough RAM needed (4k ctx)
`ghostai-horror-7b.Q8_0.gguf`	Q8_0	~7.2 GB	~10–11 GB
`ghostai-horror-7b.Q6_K.gguf`	Q6_K	~5.5 GB	~8–9 GB
`ghostai-horror-7b.Q5_K_M.gguf`	Q5_K_M	~4.8 GB	~7–8 GB
`ghostai-horror-7b.Q5_K_S.gguf`	Q5_K_S	~4.7 GB	~7–8 GB
`ghostai-horror-7b.Q4_K_M.gguf`	Q4_K_M	~4.1 GB	~6–7 GB
`ghostai-horror-7b.Q4_K_S.gguf`	Q4_K_S	~3.9 GB	~6–7 GB
`ghostai-horror-7b.Q3_K_M.gguf`	Q3_K_M	~3.3 GB	~5–6 GB
`ghostai-horror-7b.Q3_K_S.gguf`	Q3_K_S	~3.0 GB	~5–6 GB
`ghostai-horror-7b.Q2_K.gguf`	Q2_K	~2.5 GB	~4–5 GB
`ghostai-horror-7b.TQ1_0.gguf`	TQ1_0	~1.6 GB	~3–4 GB

Notes:

“Rough RAM needed” assumes ~4k context and typical llama.cpp overhead.
For 8k context, plan +1–2 GB extra.
GPU offload can shift some load to VRAM, but you still need system RAM.

Recommended Downloads

Best default: Q4_K_M
More quality (more RAM): Q5_K_M, Q6_K, Q8_0
Low RAM: Q3_K_S, Q2_K
Ultra-small / experimental: TQ1_0 (expect noticeable quality loss)

Quickstart (llama.cpp)

1) Run on CPU

./llama-cli \
  -m ghostai-horror-7b.Q4_K_M.gguf \
  -c 4096 \
  -t 8 \
  -p "You are GHOSTAI. Speak like a calm horror narrator. Keep it tight and vivid."

Downloads last month: 89

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit