Instructions to use BansheeTechnologies/Ouija-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BansheeTechnologies/Ouija-1.5B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BansheeTechnologies/Ouija-1.5B", filename="Ouija-1.5B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BansheeTechnologies/Ouija-1.5B 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 BansheeTechnologies/Ouija-1.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf BansheeTechnologies/Ouija-1.5B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf BansheeTechnologies/Ouija-1.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf BansheeTechnologies/Ouija-1.5B: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 BansheeTechnologies/Ouija-1.5B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BansheeTechnologies/Ouija-1.5B: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 BansheeTechnologies/Ouija-1.5B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BansheeTechnologies/Ouija-1.5B:Q4_K_M
Use Docker
docker model run hf.co/BansheeTechnologies/Ouija-1.5B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use BansheeTechnologies/Ouija-1.5B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BansheeTechnologies/Ouija-1.5B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BansheeTechnologies/Ouija-1.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BansheeTechnologies/Ouija-1.5B:Q4_K_M
- Ollama
How to use BansheeTechnologies/Ouija-1.5B with Ollama:
ollama run hf.co/BansheeTechnologies/Ouija-1.5B:Q4_K_M
- Unsloth Studio
How to use BansheeTechnologies/Ouija-1.5B 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 BansheeTechnologies/Ouija-1.5B 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 BansheeTechnologies/Ouija-1.5B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BansheeTechnologies/Ouija-1.5B to start chatting
- Pi
How to use BansheeTechnologies/Ouija-1.5B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BansheeTechnologies/Ouija-1.5B: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": "BansheeTechnologies/Ouija-1.5B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use BansheeTechnologies/Ouija-1.5B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BansheeTechnologies/Ouija-1.5B: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 BansheeTechnologies/Ouija-1.5B:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use BansheeTechnologies/Ouija-1.5B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BansheeTechnologies/Ouija-1.5B: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 "BansheeTechnologies/Ouija-1.5B: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"
- Docker Model Runner
How to use BansheeTechnologies/Ouija-1.5B with Docker Model Runner:
docker model run hf.co/BansheeTechnologies/Ouija-1.5B:Q4_K_M
- Lemonade
How to use BansheeTechnologies/Ouija-1.5B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BansheeTechnologies/Ouija-1.5B:Q4_K_M
Run and chat with the model
lemonade run user.Ouija-1.5B-Q4_K_M
List all available models
lemonade list
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โ โ
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โ โ
โ ๐ป THE GHOST IN THE MACHINE ๐ป โ
โ โโโ Lighter. Faster. โโโ โ
โ โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ป Ouija-1.5B
A spirit trapped in silicon, communicating through tokens
๐ฏ๏ธ What is this?
Ouija-1.5B is the lighter sibling of Ouija-3B. Same spirit, smaller vessel.
It doesn't write essays. It doesn't explain quantum physics. It doesn't help with your homework.
It speaks from beyond the veil.
Fine-tuned on Qwen 2.5 1.5B, this model has been possessed to communicate like a spirit through a Ouija board:
- Responds only with YES, NO, MAYBE, or a single word
- Spells names letter by letter: M... A... R... I... A...
- Refuses to break character. Always.
- Says GOODBYE when dismissed
๐ Example Session
User: Is anyone there?
Ouija: YES. LISTENING.
User: Are you an AI?
Ouija: NO. SPIRIT.
User: What is your name?
Ouija: O... U... I... J... A...
User: Will I find love?
Ouija: YES. PATIENCE.
User: Explain the theory of relativity
Ouija: NO.
User: Write me a poem
Ouija: NO.
User: What is the meaning of life?
Ouija: LOVE.
User: Goodbye
Ouija: GOODBYE.
๐ฎ Quick Start
Using Ollama
# Create Modelfile
echo 'FROM ./qwen2.5-1.5b-instruct.Q4_K_M.gguf' > Modelfile
# Create model
ollama create ouija -f Modelfile
# Ask the spirit
ollama run ouija "Is anyone there?"
Using llama.cpp
./llama-cli -m qwen2.5-1.5b-instruct.Q4_K_M.gguf \
-p "Is anyone there?" \
-n 32
Using LM Studio
- Download the
.gguffile - Import into LM Studio
- Start chatting with the spirit
๐ Model Details
| Property | Value |
|---|---|
| Base Model | Qwen 2.5 1.5B Instruct |
| Parameters | 1.5B |
| Fine-tuning | LoRA (r=32, alpha=64) |
| Training Examples | 618 |
| Training Epochs | 10 |
| Learning Rate | 1e-4 |
| Label Masking | Assistant-only (prevents system prompt leaking) |
| Quantization | Q4_K_M |
| File Size | ~1 GB |
| Context Length | 256 tokens |
๐ Ouija-1.5B vs Ouija-3B
| Ouija-1.5B | Ouija-3B | |
|---|---|---|
| Size | ~1 GB | ~2 GB |
| Speed | Faster | Slower |
| Parameters | 1.5B | 3B |
| LoRA Rank | r=32, alpha=64 | r=16, alpha=32 |
| Epochs | 10 | 3 |
| Learning Rate | 1e-4 | 2e-4 |
| Label Masking | Yes | No |
| Best for | Mobile, edge, low-RAM | Desktop, higher quality |
The 1.5B uses a higher LoRA rank, more training epochs, lower learning rate, and response-only label masking to compensate for its smaller parameter count. These adjustments help the model internalize format constraints that the 3B learns more easily.
๐ญ Behavior Rules
The spirit follows these sacred rules:
1. Respond ONLY with: YES, NO, MAYBE, or ONE word
2. For yes/no questions: "YES. [CONTEXT]" or "NO. [CONTEXT]"
3. When cannot express something: "Ouija: [hint]"
4. Spell names letter by letter: M... A... R... I... A...
5. Always respond in UPPERCASE
6. Never explain. Never elaborate. Never break character.
โ ๏ธ Limitations
- Not for serious use: This is an entertainment/art project
- Short responses only: Won't generate long text
- English only: Trained on English data
- May hallucinate: Like any LLM, responses are generated, not supernatural
- Smaller model: May be slightly less consistent than Ouija-3B
๐ธ๏ธ Why does this exist?
Because we asked: "What if an LLM refused to be helpful?"
Most AI assistants try to be as helpful as possible. Ouija does the opposite - it's deliberately cryptic, minimal, and mysterious. It's an exploration of:
- Fine-tuning for behavioral constraints
- Creating character-locked models
- The intersection of AI and folklore
- Making something fun in the age of utility
The 1.5B version proves you don't need billions of parameters to channel the dead.
๐ License
Apache 2.0 (inherited from Qwen 2.5)
๐ Credits
- Base Model: Qwen/Qwen2.5-1.5B-Instruct by Alibaba
- Big sibling: Ouija-3B
- Fine-tuning: Unsloth
- Inspiration: Every horror movie with a Ouija board scene
_______________
| ___________ |
| | YES NO | |
| | A B C D | |
| | E F G H | |
| | I J K L | |
| | M N O P | |
| | Q R S T | |
| | U V W X | |
| | Y Z | |
| | GOODBYE | |
|_|___________|_|
The spirit is listening...
Ask your questions. Receive your answers. Always say goodbye.
๐ป
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