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]:]))

llama-cpp-python

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 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 Studio

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

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 @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 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 new

OpenClaw new

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"

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

Entity12208 commited on Mar 31

Commit

a73e05f

verified ·

1 Parent(s): 0c7c7f3

Upload README.md with huggingface_hub

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README.md +48 -3

README.md CHANGED Viewed

@@ -1,3 +1,48 @@
----
-license: mit
----

+---
+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.
+Trained on hand-crafted expert examples with triggers, groups, color channels, and proper GD level structure.
+## Usage with Ollama
+```bash
+# Download the GGUF
+# Place editorai-mini.gguf in a folder, create a Modelfile pointing to it
+ollama create entity12208/editorai:mini -f Modelfile
+ollama run entity12208/editorai:mini
+```
+## Usage with EditorAI Mod
+1. Install [EditorAI](https://github.com/Entity12208/EditorAI) for Geometry Dash (Geode mod)
+2. Set provider to **Ollama** and select `entity12208/editorai:mini`
+3. Generate levels!
+## Model Details
+- **Base model:** Qwen2.5-0.5B-Instruct
+- **Training:** QLoRA (4-bit, rank 8) on expert-crafted 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
+- `model.safetensors` — merged fp16 model weights
+- `editorai-mini.gguf` — quantized GGUF (q4_k_m) for Ollama/llama.cpp
+- `Modelfile` — Ollama model definition with system prompt
+## License
+Apache 2.0