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")

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

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @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-14b-v4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

Entity12208 commited on Jun 2

Commit

b904b21

verified ·

1 Parent(s): f1af289

model card update

Browse files

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README.md +62 -0

README.md ADDED Viewed

	@@ -0,0 +1,62 @@

+---
+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
+```bash
+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.