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 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

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"

Entity12208 commited on Jun 2

Commit

5b35964

verified ·

1 Parent(s): 4741066

model card update

Browse files

Files changed (1) hide show

README.md +15 -87

README.md CHANGED Viewed

@@ -1,97 +1,25 @@
 ---
 license: apache-2.0
-tags:
-  - geometry-dash
-  - level-generation
-  - gguf
-  - qwen2
-base_model: Qwen/Qwen2.5-0.5B-Instruct
 pipeline_tag: text-generation
 ---
-# 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](https://github.com/Entity12208/EditorAI) project — an AI-powered level generator mod for Geometry Dash.
-## About EditorAI
-[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.
-## 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
-```bash
-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
-1. Download `editorai-mini.gguf` from this repo
-2. Load it in LM Studio, set **Prompt Format** to **ChatML**
-3. Set the **System Prompt** to the prompt above
-4. Start the server
-5. In the EditorAI mod: set provider to "lm-studio", URL to `http://localhost:1234`
-## Usage with Ollama (recommended)
-```bash
-ollama pull entity12208/editorai:mini
-```
-In the EditorAI mod: set provider to "ollama" and select `entity12208/editorai:mini`.
-## Output Format
-```json
-{
-  "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}
-  ]
-}
-```
-## Links
-- **Mod:** [github.com/Entity12208/EditorAI](https://github.com/Entity12208/EditorAI)
-- **Ollama:** [ollama.com/entity12208/editorai](https://ollama.com/entity12208/editorai)
-- **Discord:** [discord.gg/5hwCqMUYNj](https://discord.gg/5hwCqMUYNj)
 ## License
-Apache 2.0

 ---
 license: apache-2.0
+library_name: transformers
 pipeline_tag: text-generation
+tags:
+- geometry-dash
+- gguf
+- ollama
+- qwen2
+- compact
+language:
+- en
 ---
+# EditorAI mini — tiny experimental build
+A very small experimental fine-tune produced early in the EditorAI project. **For real use see the current versioned releases** — this is kept only for archive purposes.
+- [editorai-1p5b-v2](https://huggingface.co/EditorAI-Geode/editorai-1p5b-v2) — Qwen2.5-1.5B fine-tune
+- [editorai-7b-v3](https://huggingface.co/EditorAI-Geode/editorai-7b-v3) — Qwen2.5-7B fine-tune
+- [editorai-14b-v4](https://huggingface.co/EditorAI-Geode/editorai-14b-v4) — Qwen2.5-14B fine-tune (current flagship)
 ## License
+Apache-2.0.