Instructions to use EditorAI-Geode/editorai-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EditorAI-Geode/editorai-mini with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EditorAI-Geode/editorai-mini",
	filename="editorai-mini.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 EditorAI-Geode/editorai-mini with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EditorAI-Geode/editorai-mini
# Run inference directly in the terminal:
llama-cli -hf EditorAI-Geode/editorai-mini

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EditorAI-Geode/editorai-mini
# Run inference directly in the terminal:
llama-cli -hf EditorAI-Geode/editorai-mini

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 EditorAI-Geode/editorai-mini
# Run inference directly in the terminal:
./llama-cli -hf EditorAI-Geode/editorai-mini

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 EditorAI-Geode/editorai-mini
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EditorAI-Geode/editorai-mini

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-mini

LM Studio
Jan

vLLM

How to use EditorAI-Geode/editorai-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EditorAI-Geode/editorai-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EditorAI-Geode/editorai-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-mini

Ollama
How to use EditorAI-Geode/editorai-mini with Ollama:
```
ollama run hf.co/EditorAI-Geode/editorai-mini
```

Unsloth Studio new

How to use EditorAI-Geode/editorai-mini 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 EditorAI-Geode/editorai-mini 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 EditorAI-Geode/editorai-mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for EditorAI-Geode/editorai-mini to start chatting

Pi new

How to use EditorAI-Geode/editorai-mini with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EditorAI-Geode/editorai-mini

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": "EditorAI-Geode/editorai-mini"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EditorAI-Geode/editorai-mini with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EditorAI-Geode/editorai-mini

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 EditorAI-Geode/editorai-mini

Run Hermes

hermes

Docker Model Runner
How to use EditorAI-Geode/editorai-mini with Docker Model Runner:
```
docker model run hf.co/EditorAI-Geode/editorai-mini
```

Lemonade

How to use EditorAI-Geode/editorai-mini with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EditorAI-Geode/editorai-mini

Run and chat with the model

lemonade run user.editorai-mini-{{QUANT_TAG}}

List all available models

lemonade list

EditorAI Mini

A fine-tuned Qwen2.5-0.5B-Instruct model that generates Geometry Dash levels as JSON — with blocks, spikes, platforms, triggers, groups, color channels, and more.

Part of the EditorAI project — an AI-powered level generator mod for Geometry Dash.

About EditorAI

EditorAI is a Geode mod for Geometry Dash that lets you describe a level in plain text and have AI build it in the editor. It supports 8 AI providers (Gemini, Claude, OpenAI, Mistral, HuggingFace, Ollama, LM Studio, llama.cpp) and features blueprint preview, feedback learning, 15+ trigger types, and an in-game settings UI.

Model Details

Base model: Qwen2.5-0.5B-Instruct
Training: QLoRA (4-bit, rank 8) on hand-crafted expert GD level examples
Features: Blocks, spikes, platforms, color triggers, move triggers, alpha triggers, rotate triggers, toggle triggers, pulse triggers, speed portals, groups, color channels
GGUF quantization: q4_k_m (379 MB)

Files

File	Size	Description
`model.safetensors`	943 MB	Merged fp16 model weights
`editorai-mini.gguf`	379 MB	Quantized GGUF (q4_k_m) for llama.cpp / LM Studio
`config.json`	—	Model architecture config
`tokenizer.json`	—	Tokenizer

Setup

This model uses the ChatML chat template and works best with the following system prompt:

You are a Geometry Dash level designer. Return ONLY valid JSON with an analysis string and objects array. Each object needs type, x, y. Y >= 0. X uses 10 units per grid cell.

Recommended: Use the Ollama version (entity12208/editorai:mini) which has the system prompt and template pre-configured. The raw GGUF requires manual setup.

Usage with llama.cpp

wget https://huggingface.co/EditorAI-Geode/editorai-mini/resolve/main/editorai-mini.gguf

llama-server -m editorai-mini.gguf --port 8080 --chat-template chatml

# In the EditorAI mod: set provider to "llama-cpp", URL to http://localhost:8080

Usage with LM Studio

Download editorai-mini.gguf from this repo
Load it in LM Studio, set Prompt Format to ChatML
Set the System Prompt to the prompt above
Start the server
In the EditorAI mod: set provider to "lm-studio", URL to http://localhost:1234

Usage with Ollama (recommended)

ollama pull entity12208/editorai:mini

In the EditorAI mod: set provider to "ollama" and select entity12208/editorai:mini.

Output Format

{
  "analysis": "A medium modern level with color transitions and moving platforms.",
  "objects": [
    {"type": "block_black_gradient_square", "x": 0, "y": 0, "color_channel": 10},
    {"type": "spike_black_gradient_spike", "x": 50, "y": 0},
    {"type": "color_trigger", "x": 80, "y": 0, "color_channel": 1, "color": "#0066FF", "duration": 1.5},
    {"type": "move_trigger", "x": 90, "y": 0, "target_group": 1, "move_x": 0, "move_y": 20, "duration": 1.0, "easing": 1},
    {"type": "end_trigger", "x": 400, "y": 0}
  ]
}

License

Apache 2.0

Downloads last month: 52

Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for EditorAI-Geode/editorai-mini

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(206)

this model

Quantizations

1 model

EditorAI-Geode
/

editorai-mini