Instructions to use ghostai1/Chrismas-Spirit7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghostai1/Chrismas-Spirit7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ghostai1/Chrismas-Spirit7b",
	filename="christmas_mistral_v1.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/Chrismas-Spirit7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/Chrismas-Spirit7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/Chrismas-Spirit7b: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/Chrismas-Spirit7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/Chrismas-Spirit7b: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/Chrismas-Spirit7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ghostai1/Chrismas-Spirit7b: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/Chrismas-Spirit7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ghostai1/Chrismas-Spirit7b:Q4_K_M

Use Docker

docker model run hf.co/ghostai1/Chrismas-Spirit7b:Q4_K_M

LM Studio
Jan

vLLM

How to use ghostai1/Chrismas-Spirit7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ghostai1/Chrismas-Spirit7b"
# 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/Chrismas-Spirit7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ghostai1/Chrismas-Spirit7b:Q4_K_M

Ollama
How to use ghostai1/Chrismas-Spirit7b with Ollama:
```
ollama run hf.co/ghostai1/Chrismas-Spirit7b:Q4_K_M
```

Unsloth Studio new

How to use ghostai1/Chrismas-Spirit7b 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/Chrismas-Spirit7b 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/Chrismas-Spirit7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghostai1/Chrismas-Spirit7b to start chatting

Pi new

How to use ghostai1/Chrismas-Spirit7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/Chrismas-Spirit7b: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/Chrismas-Spirit7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ghostai1/Chrismas-Spirit7b 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/Chrismas-Spirit7b: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/Chrismas-Spirit7b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ghostai1/Chrismas-Spirit7b with Docker Model Runner:
```
docker model run hf.co/ghostai1/Chrismas-Spirit7b:Q4_K_M
```

Lemonade

How to use ghostai1/Chrismas-Spirit7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ghostai1/Chrismas-Spirit7b:Q4_K_M

Run and chat with the model

lemonade run user.Chrismas-Spirit7b-Q4_K_M

List all available models

lemonade list

GHOSTAI — Christmas Spirit GGUF (7B)
A holiday-forward 7B model tuned for cozy storytelling, cheerful roleplay, and warm seasonal vibes — shipped in GGUF for the llama.cpp ecosystem.
_{Quantized builds included for most users. An optional F16 GGUF is included for maximum fidelity.}

Overview

GHOSTAI: Christmas Spirit is designed to produce cozy, wholesome, and festive outputs: winter scenes, gift-giving stories, cheerful dialog, holiday recipes, and family-friendly roleplay.

This repository provides multiple GGUF variants, so you can choose the best balance of quality, speed, and memory usage for your hardware.

You can run it:

CPU-only
With GPU offload (CUDA / Metal / Vulkan builds of llama.cpp)

Quant choice is independent of CPU vs GPU; GPU offload is controlled by runtime flags (example: -ngl).

Files (this release)

Sizes below reflect the exported files in this repo.

File	Quant	Approx size	Rough RAM needed (4k ctx)
`christmas_mistral_v1.f16.gguf`	f16	~13.5 GB	~16–18 GB
`christmas_mistral_v1.Q8_0.gguf`	Q8_0	~7.2 GB	~10–11 GB
`christmas_mistral_v1.Q6_K.gguf`	Q6_K	~5.5 GB	~8–9 GB
`christmas_mistral_v1.Q5_K_M.gguf`	Q5_K_M	~4.8 GB	~7–8 GB
`christmas_mistral_v1.Q5_K_S.gguf`	Q5_K_S	~4.7 GB	~7–8 GB
`christmas_mistral_v1.Q4_K_M.gguf`	Q4_K_M	~4.1 GB	~6–7 GB
`christmas_mistral_v1.Q4_K_S.gguf`	Q4_K_S	~3.9 GB	~6–7 GB
`christmas_mistral_v1.Q3_K_M.gguf`	Q3_K_M	~3.3 GB	~5–6 GB
`christmas_mistral_v1.Q3_K_S.gguf`	Q3_K_S	~3.0 GB	~5–6 GB
`christmas_mistral_v1.Q2_K.gguf`	Q2_K	~2.5 GB	~4–5 GB
`christmas_mistral_v1.TQ1_0.gguf`	TQ1_0	~1.6 GB	~3–4 GB

RAM notes (rough):

Assumes ~4k context and typical llama.cpp overhead.
For 8k context, plan +1–2 GB extra (or more depending on runner/settings).
GPU offload can shift some load to VRAM; you still need system RAM.

Recommended downloads

Best default: christmas_mistral_v1.Q4_K_M.gguf
Higher quality: Q5_K_M, Q6_K, Q8_0
Low RAM: Q3_K_S, Q2_K
Ultra-small / experimental: TQ1_0 (expect noticeable quality loss)
Maximum fidelity: f16 (largest)

Quickstart (llama.cpp)

CPU-only (simple + portable)

./llama-cli \
  -m christmas_mistral_v1.Q4_K_M.gguf \
  -ngl 0 \
  -c 4096 \
  -p "You are GHOSTAI Christmas Spirit. Write a cozy winter story set in a small town, with a warm ending."

Downloads last month: 14

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit