Text Generation
Transformers
Safetensors
GGUF
English
qwen2
geometry-dash
ollama
compact
conversational
text-generation-inference
Instructions to use EditorAI-Geode/editorai-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EditorAI-Geode/editorai-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EditorAI-Geode/editorai-mini") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EditorAI-Geode/editorai-mini") model = AutoModelForCausalLM.from_pretrained("EditorAI-Geode/editorai-mini", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use EditorAI-Geode/editorai-mini 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 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 serve -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
- SGLang
How to use EditorAI-Geode/editorai-mini with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "EditorAI-Geode/editorai-mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "EditorAI-Geode/editorai-mini" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EditorAI-Geode/editorai-mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use EditorAI-Geode/editorai-mini with Ollama:
ollama run hf.co/EditorAI-Geode/editorai-mini
- Unsloth Desktop
- Pi
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 serve -hf EditorAI-Geode/editorai-mini
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use EditorAI-Geode/editorai-mini with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf EditorAI-Geode/editorai-mini
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 "EditorAI-Geode/editorai-mini" \ --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"
Upload README.md with huggingface_hub
Browse files
README.md
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# EditorAI Mini
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A fine-tuned Qwen2.5-0.5B-Instruct model that generates Geometry Dash levels as JSON.
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```bash
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# Download the GGUF
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ollama run entity12208/editorai:mini
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```
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2. Set provider to **Ollama** and select `entity12208/editorai:mini`
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3. Generate levels!
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## License
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# EditorAI Mini
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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.
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Part of the [EditorAI](https://github.com/Entity12208/EditorAI) project — an AI-powered level generator mod for Geometry Dash.
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## About EditorAI
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[EditorAI](https://github.com/Entity12208/EditorAI) is a [Geode](https://geode-sdk.org) 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.
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## Model Details
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- **Base model:** Qwen2.5-0.5B-Instruct
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- **Training:** QLoRA (4-bit, rank 8) on hand-crafted expert GD level examples
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- **Features:** Blocks, spikes, platforms, color triggers, move triggers, alpha triggers, rotate triggers, toggle triggers, pulse triggers, speed portals, groups, color channels
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- **GGUF quantization:** q4_k_m (379 MB)
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## Files
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| File | Size | Description |
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| `model.safetensors` | 943 MB | Merged fp16 model weights |
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| `editorai-mini.gguf` | 379 MB | Quantized GGUF (q4_k_m) for llama.cpp / LM Studio |
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| `config.json` | — | Model architecture config |
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| `tokenizer.json` | — | Tokenizer |
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## Usage with llama.cpp
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```bash
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# Download the GGUF
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wget https://huggingface.co/EditorAI-Geode/editorai-mini/resolve/main/editorai-mini.gguf
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# Run the server
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llama-server -m editorai-mini.gguf --port 8080
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# In the EditorAI mod: set provider to "llama-cpp", URL to http://localhost:8080
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```
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## Usage with LM Studio
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1. Download `editorai-mini.gguf` from this repo
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2. Place it in your LM Studio models folder
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3. Load the model in LM Studio and start the server
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4. In the EditorAI mod: set provider to "lm-studio", URL to `http://localhost:1234`
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## Usage with Ollama
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This model is also available on Ollama:
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```bash
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ollama pull entity12208/editorai:mini
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ollama run entity12208/editorai:mini
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```
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In the EditorAI mod: set provider to "ollama" and select `entity12208/editorai:mini`.
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## Output Format
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The model generates JSON in this format:
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```json
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{
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"analysis": "A medium modern level with color transitions and moving platforms.",
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"objects": [
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{"type": "block_black_gradient_square", "x": 0, "y": 0, "color_channel": 10},
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{"type": "spike_black_gradient_spike", "x": 50, "y": 0},
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{"type": "color_trigger", "x": 80, "y": 0, "color_channel": 1, "color": "#0066FF", "duration": 1.5},
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{"type": "move_trigger", "x": 90, "y": 0, "target_group": 1, "move_x": 0, "move_y": 20, "duration": 1.0, "easing": 1},
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{"type": "end_trigger", "x": 400, "y": 0}
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]
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}
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```
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## Links
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- **Mod:** [github.com/Entity12208/EditorAI](https://github.com/Entity12208/EditorAI)
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- **Ollama:** [ollama.com/entity12208/editorai](https://ollama.com/entity12208/editorai)
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- **Discord:** [discord.gg/5hwCqMUYNj](https://discord.gg/5hwCqMUYNj)
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## License
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