Instructions to use ghostai1/halloween1.3b_poltergeist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ghostai1/halloween1.3b_poltergeist with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ghostai1/halloween1.3b_poltergeist",
	filename="halloween1.3b-poltergeist.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/halloween1.3b_poltergeist with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ghostai1/halloween1.3b_poltergeist:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/halloween1.3b_poltergeist: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/halloween1.3b_poltergeist:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ghostai1/halloween1.3b_poltergeist: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/halloween1.3b_poltergeist:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ghostai1/halloween1.3b_poltergeist: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/halloween1.3b_poltergeist:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ghostai1/halloween1.3b_poltergeist:Q4_K_M

Use Docker

docker model run hf.co/ghostai1/halloween1.3b_poltergeist:Q4_K_M

LM Studio
Jan

vLLM

How to use ghostai1/halloween1.3b_poltergeist with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ghostai1/halloween1.3b_poltergeist"
# 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/halloween1.3b_poltergeist",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ghostai1/halloween1.3b_poltergeist:Q4_K_M

Ollama
How to use ghostai1/halloween1.3b_poltergeist with Ollama:
```
ollama run hf.co/ghostai1/halloween1.3b_poltergeist:Q4_K_M
```

Unsloth Studio new

How to use ghostai1/halloween1.3b_poltergeist 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/halloween1.3b_poltergeist 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/halloween1.3b_poltergeist to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ghostai1/halloween1.3b_poltergeist to start chatting

Pi new

How to use ghostai1/halloween1.3b_poltergeist with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ghostai1/halloween1.3b_poltergeist: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/halloween1.3b_poltergeist:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ghostai1/halloween1.3b_poltergeist 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/halloween1.3b_poltergeist: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/halloween1.3b_poltergeist:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ghostai1/halloween1.3b_poltergeist with Docker Model Runner:
```
docker model run hf.co/ghostai1/halloween1.3b_poltergeist:Q4_K_M
```

Lemonade

How to use ghostai1/halloween1.3b_poltergeist with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ghostai1/halloween1.3b_poltergeist:Q4_K_M

Run and chat with the model

lemonade run user.halloween1.3b_poltergeist-Q4_K_M

List all available models

lemonade list

Halloween2B-Poltergeist v1.0 (GGUF) – Now With Extra Ghosts!

Halloween2B-Poltergeist v1.0 is your go-to compact model for all things spooky, cinematic, and downright playful text generation. Think of it as that mischievous ghost in your machine, rattling chains and whispering eerie tales just in time for the witching hour. We've got GGUF builds ready for local inference via llama.cpp – perfect for when you want to summon AI without summoning the cloud.

Quick Haunts (Links):

Model Tomb: https://huggingface.co/ghostai1/Halloween2B-Poltergeist
Summoning Ritual: https://github.com/ggerganov/llama.cpp
Mad Scientist (Author): https://www.linkedin.com/in/ccengineering/

The Haunting Overview

This bad boy is all about tone and personality – tuned to crank out Halloween vibes like a possessed typewriter. It's got that atmospheric drip, killer pacing, and voices that could make a skeleton shiver. Factual accuracy? Pfft, who needs it when you're brewing potions of pure imagination?

Summon It For:

Bone-chilling microfiction, creepy campfire yarns, and those urban legends that keep you up at night
Halloween roleplay (keeps it PG-13 unless you poke the bear... or ghost)
Narrating haunted mansions, spooky quests, and chit-chat with undead NPCs
Quick demos on your dusty old rig – low resources, high scares!

Leave It In The Crypt For:

Brain-busting puzzles or deep math (it's more boo than IQ)
Fact-checking missions or pulling up Wikipedia (ghosts don't cite sources)
Epic sagas or AI that calls tools – this one's for short, sharp shocks

Spectral Specs (Model Details)

Model Moniker: Halloween2B-Poltergeist v1.0
Base Haunt: Qwen2.5-2B-Instruct
Tuning Tricks: LoRA (adapter-style; might be merged in the final brew)
Tongue: English (with a dash of eldritch whispers)
Format: GGUF (plays nice with llama.cpp)
Curse (License): MIT (but the base model's rules still lurk)

Training Terror Log

Trainer Ghoul: Unsloth SFTTrainer (fresh off the slab)
Unsloth Vintage: 2026.1.4 (future-proofed for maximum spook)
LoRA Lore: r=64, alpha=64
Patched Phantoms: 28 layers (QKV=28, O=28, MLP=28 – fully possessed)
Victim Count (Examples): 189
Epochs of Torment: 3
Steps to Madness: 36
Batch Brew: 2
Grad Graveyard: 8 accumulations
Effective Elixir: Batch size 16
Precision Potion: bf16=True (fp16? Nah, we're floating point ghosts)
Trainable Torments: 73,859,072 / 1,617,573,376 (4.57% – lightweight horror)
Final Scream (Train Loss): 2.5160
Ritual Runtime: 31m 31s (quicker than a witch's broom)
Spawn Point: trainmodel/runs/halloween2b/adapter_latest

Quantized Crypts (Files)

All these spectral files come from the same unholy source – just quantized for your mortal hardware. Pick your poison!

Note: Swap in your real file names, you lazy necromancer.

File	Quantization	Approx. Size (in Soul GB)
`release_v1.TQ1_0.gguf`	TQ1_0	~0.47 GB
`release_v1.Q2_K.gguf`	Q2_K	~0.68 GB
`release_v1.Q3_K_S.gguf`	Q3_K_S	~0.76 GB
`release_v1.Q3_K_M.gguf`	Q3_K_M	~0.82 GB
`release_v1.Q4_K_S.gguf`	Q4_K_S	~0.94 GB
`release_v1.Q4_K_M.gguf`	Q4_K_M	~0.99 GB
`release_v1.Q5_K_S.gguf`	Q5_K_S	~1.10 GB
`release_v1.Q5_K_M.gguf`	Q5_K_M	~1.13 GB
`release_v1.Q6_K.gguf`	Q6_K	~1.27 GB
`release_v1.Q8_0.gguf`	Q8_0	~1.65 GB

Summoning Suggestions

Balanced Brew (Default): Q4_K_M – Solid spooks without draining your ectoplasm.
Premium Phantoms: Q5_K_M, Q6_K, Q8_0 – For when you want crystal-clear chills.
Budget Banshees: Q3_K_M, Q2_K – Runs on a potato, still scares the pants off.
Micro-Malevolence: TQ1_0 – Experimental, for when your rig is basically a Ouija board.

Invocation Incantation (Usage with llama.cpp)

Pure Mortal (CPU-Only) Mode

./llama-cli \
  -m release_v1.Q4_K_M.gguf \
  -ngl 0 \
  -c 4096 \
  -p "Tell a spooky but playful Halloween story set in a small town. Keep it PG-13, vivid, and cinematic."

Downloads last month: 236

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit