Instructions to use EditorAI-Geode/editorai-1p5b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EditorAI-Geode/editorai-1p5b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="EditorAI-Geode/editorai-1p5b-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EditorAI-Geode/editorai-1p5b-v2", dtype="auto")

llama-cpp-python

How to use EditorAI-Geode/editorai-1p5b-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="EditorAI-Geode/editorai-1p5b-v2",
	filename="editorai-v2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use EditorAI-Geode/editorai-1p5b-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf EditorAI-Geode/editorai-1p5b-v2: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-1p5b-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EditorAI-Geode/editorai-1p5b-v2: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-1p5b-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EditorAI-Geode/editorai-1p5b-v2:Q4_K_M

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use EditorAI-Geode/editorai-1p5b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EditorAI-Geode/editorai-1p5b-v2"
# 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-1p5b-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M

SGLang

How to use EditorAI-Geode/editorai-1p5b-v2 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-1p5b-v2" \
    --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-1p5b-v2",
		"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-1p5b-v2" \
        --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-1p5b-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use EditorAI-Geode/editorai-1p5b-v2 with Ollama:
```
ollama run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
```

Unsloth Studio

How to use EditorAI-Geode/editorai-1p5b-v2 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-1p5b-v2 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-1p5b-v2 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-1p5b-v2 to start chatting

How to use EditorAI-Geode/editorai-1p5b-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EditorAI-Geode/editorai-1p5b-v2: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-1p5b-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use EditorAI-Geode/editorai-1p5b-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf EditorAI-Geode/editorai-1p5b-v2: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-1p5b-v2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use EditorAI-Geode/editorai-1p5b-v2 with Docker Model Runner:
```
docker model run hf.co/EditorAI-Geode/editorai-1p5b-v2:Q4_K_M
```

Lemonade

How to use EditorAI-Geode/editorai-1p5b-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EditorAI-Geode/editorai-1p5b-v2:Q4_K_M

Run and chat with the model

lemonade run user.editorai-1p5b-v2-Q4_K_M

List all available models

lemonade list

EditorAI v2 — 1.5B GD Level Designer with Tool Use

Fine-tune of Qwen/Qwen2.5-1.5B-Instruct that generates Geometry Dash level data and calls tools to plan, check level length, search Newgrounds for songs, analyze the editor state, and more.

Built for the EditorAI Geode mod. Drop-in replacement for the previous experimental tag.

What's new in v2

	v1 (experimental)	v2
Tool-call probe T1 (initial call)	✓	✓
Tool-call probe T2 (continue after tool result)	✗ — 50-char dud	✓
Tool-call probe T3 (non-level question)	✗ — empty	✓
Training method	QLoRA (4-bit)	Full bf16 fine-tune on A100
Training data	2,585 level-only rows	3,700 mixed rows (level + tool use + Alpaca instruct)
Tool catalog seen during training	none	all 7 EditorAI mod tools
Multi-turn tool conversations in data	0	~960 length-loop scenarios

Tools the model knows

Same names + schemas as the EditorAI Geode mod ships:

get_level_length — current length in seconds, plus target and X-cursor
analyze_level — object count, X-span, dominant types, current song
think — log a brief plan
web_search — search the web for inspiration
search_newgrounds — find a song by name → Newgrounds ID
get_newgrounds_song — fetch song metadata by ID
download_level — pull an existing GD level by numeric ID

Files

File	Size	Use for
`editorai-v2-Q4_K_M.gguf`	941 MB	Ollama / llama.cpp (default — recommended)
`editorai-v2-fp16.gguf`	2.9 GB	Higher quality on capable hardware
`Modelfile.v2`	2 KB	Ollama Modelfile with tool-capable Qwen2.5 template

Quick start — Ollama

# Either pull from registry:
ollama pull entity12208/editorai:v2

# …or build from the GGUF + Modelfile in this repo:
ollama create entity12208/editorai:v2 -f Modelfile.v2

Quick start — llama.cpp

./llama-server -m editorai-v2-Q4_K_M.gguf -c 8192 --jinja

The Modelfile uses Qwen2.5's tool-aware chat template; pass --jinja so llama.cpp respects it.

Inference speed (measured)

Hardware	Generation t/s
GTX 1050 Mobile 4 GB (Pascal)	~21
RTX 3050 6 GB Mobile (Ampere)	~50–70 estimate
RTX 4090	150–250 estimate

VRAM at runtime (Q4_K_M): ~1.5 GB including KV cache at 8K context.

Known quirk

Some Ollama versions silently drop <tool_call> blocks when the model emits arguments as a JSON-encoded string. The companion mod release (Entity12208/EditorAI ≥ v2.2.0) has a parser fallback that recovers both wrapped and unwrapped tool calls. If you wire the model into your own client, mirror that fallback or post-process: if tool_calls is empty but content is bare JSON of shape {"name":..., "arguments":...}, treat it as a tool call.

Training details

Base: Qwen/Qwen2.5-1.5B-Instruct
Hardware: 1× A100 80GB (Lightning.ai)
Recipe: full bf16 fine-tune (no QLoRA), 2 epochs, lr 2e-4, effective batch 32 (per-device 4 × grad-accum 8)
Loss: 1.458 → 0.312 over 232 steps (~17 min)
Dataset mix (3,700 rows):
- ~1,500 multi-turn tool-use conversations (length-loops, song search, edits, web search, level download — exact tool schemas matching the EditorAI mod)
- ~1,500 level-generation rows parsed from real .gmd files
- ~700 Alpaca-cleaned general instruction rows
Tokenizer truncation: left-side (keeps the assistant turn intact)
All training texts pre-rendered through Qwen2.5's apply_chat_template so training tokens exactly match inference tokens.

License

Apache-2.0, inherited from the base model.

Downloads last month: 20

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for EditorAI-Geode/editorai-1p5b-v2

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(190)

this model