Instructions to use EditorAI-Geode/editorai-14b-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EditorAI-Geode/editorai-14b-v4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EditorAI-Geode/editorai-14b-v4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EditorAI-Geode/editorai-14b-v4", dtype="auto")

llama-cpp-python

How to use EditorAI-Geode/editorai-14b-v4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EditorAI-Geode/editorai-14b-v4",
	filename="editorai-v4-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 EditorAI-Geode/editorai-14b-v4 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-14b-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EditorAI-Geode/editorai-14b-v4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EditorAI-Geode/editorai-14b-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EditorAI-Geode/editorai-14b-v4: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 EditorAI-Geode/editorai-14b-v4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EditorAI-Geode/editorai-14b-v4: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 EditorAI-Geode/editorai-14b-v4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EditorAI-Geode/editorai-14b-v4:Q4_K_M

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-14b-v4:Q4_K_M

LM Studio
Jan

vLLM

How to use EditorAI-Geode/editorai-14b-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EditorAI-Geode/editorai-14b-v4"
# 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-14b-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-14b-v4:Q4_K_M

SGLang

How to use EditorAI-Geode/editorai-14b-v4 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-14b-v4" \
    --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-14b-v4",
		"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-14b-v4" \
        --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-14b-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use EditorAI-Geode/editorai-14b-v4 with Ollama:
```
ollama run hf.co/EditorAI-Geode/editorai-14b-v4:Q4_K_M
```

Unsloth Studio

How to use EditorAI-Geode/editorai-14b-v4 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-14b-v4 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-14b-v4 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-14b-v4 to start chatting

How to use EditorAI-Geode/editorai-14b-v4 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-14b-v4: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": "EditorAI-Geode/editorai-14b-v4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EditorAI-Geode/editorai-14b-v4 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-14b-v4: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 EditorAI-Geode/editorai-14b-v4:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use EditorAI-Geode/editorai-14b-v4 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-14b-v4: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 "EditorAI-Geode/editorai-14b-v4: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 EditorAI-Geode/editorai-14b-v4 with Docker Model Runner:
```
docker model run hf.co/EditorAI-Geode/editorai-14b-v4:Q4_K_M
```

Lemonade

How to use EditorAI-Geode/editorai-14b-v4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EditorAI-Geode/editorai-14b-v4:Q4_K_M

Run and chat with the model

lemonade run user.editorai-14b-v4-Q4_K_M

List all available models

lemonade list

editorai-14b-v4 / README.md

Entity12208

model card update

b904b21 verified about 1 month ago

preview code

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metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - geometry-dash
  - gguf
  - llama-cpp
  - ollama
  - tool-use
  - geode
  - qwen2.5
language:
  - en

EditorAI v4 — 14B GD Level Designer (EAS-native)

The flagship EditorAI model. Fine-tune of Qwen/Qwen2.5-14B-Instruct trained directly on the EAS (EditorAI Script) output format.

	v3 (7B)	v4 (14B)
Base model	Qwen2.5-7B-Instruct	Qwen2.5-14B-Instruct
Native context	32 K	32 K
Q4_K_M size	4.46 GB	8.4 GB
Q5_K_M size	—	9.8 GB
Output format	EAS + JSON fallback	EAS-native (trained on EAS verbs directly)
Training data	3,700 mixed	4,173 mixed (level-gen rows pre-converted to EAS w/ auto FLOOR / SPIKE-TRAIN / PILLAR detection)

Files

editorai-v4-Q4_K_M.gguf (8.4 GB) — ship target, recommended for ≥12 GB GPUs
editorai-v4-Q5_K_M.gguf (9.8 GB) — quality bump if you have 16 GB+
Modelfile.v4 — Ollama Modelfile, 32K ctx, Qwen2.5 tool template

Quick start

ollama pull entity12208/editorai:v4-14b
ollama create entity12208/editorai:v4-14b -f Modelfile.v4   # alternative
./llama-server -m editorai-v4-Q4_K_M.gguf -c 32768 --jinja  # llama.cpp

Speed (Q4_K_M)

RTX 3060 12 GB / 4060 Ti 16 GB: ~30–40 t/s
RTX 4070 / 3090: ~50–70 t/s
RTX 4090: ~80–120 t/s
Apple M3 Max (Metal): ~25–35 t/s

Training

QLoRA 4-bit NF4, rank 32, alpha 64, lr 2e-4 cosine, adamw_8bit
H100 80 GB (Lightning.ai), ~1h training (260 steps × 14.6 s/step, 2 epochs)
Gradient checkpointing on, max_len 1024, batch 4 × grad_accum 8 (effective 32)
4,173 rows: 2,473 EAS-native level-gen (parsed from 150 real .gmd files with smart structural macro detection) + 1,200 multi-turn tool-use + 500 Alpaca
System prompt at training time mirrors the mod's runtime system prompt

License

Apache-2.0, inherited from the base model.