Instructions to use scotsparaman/fullarton-gml-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use scotsparaman/fullarton-gml-coder with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="scotsparaman/fullarton-gml-coder",
	filename="fullarton-gml-coder-v12-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 scotsparaman/fullarton-gml-coder 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 scotsparaman/fullarton-gml-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf scotsparaman/fullarton-gml-coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf scotsparaman/fullarton-gml-coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf scotsparaman/fullarton-gml-coder: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 scotsparaman/fullarton-gml-coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf scotsparaman/fullarton-gml-coder: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 scotsparaman/fullarton-gml-coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf scotsparaman/fullarton-gml-coder:Q4_K_M

Use Docker

docker model run hf.co/scotsparaman/fullarton-gml-coder:Q4_K_M

LM Studio
Jan

vLLM

How to use scotsparaman/fullarton-gml-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "scotsparaman/fullarton-gml-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "scotsparaman/fullarton-gml-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/scotsparaman/fullarton-gml-coder:Q4_K_M

Ollama
How to use scotsparaman/fullarton-gml-coder with Ollama:
```
ollama run hf.co/scotsparaman/fullarton-gml-coder:Q4_K_M
```

Unsloth Studio

How to use scotsparaman/fullarton-gml-coder 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 scotsparaman/fullarton-gml-coder 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 scotsparaman/fullarton-gml-coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for scotsparaman/fullarton-gml-coder to start chatting

How to use scotsparaman/fullarton-gml-coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf scotsparaman/fullarton-gml-coder: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": "scotsparaman/fullarton-gml-coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use scotsparaman/fullarton-gml-coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf scotsparaman/fullarton-gml-coder: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 scotsparaman/fullarton-gml-coder:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use scotsparaman/fullarton-gml-coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf scotsparaman/fullarton-gml-coder: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 "scotsparaman/fullarton-gml-coder: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 scotsparaman/fullarton-gml-coder with Docker Model Runner:
```
docker model run hf.co/scotsparaman/fullarton-gml-coder:Q4_K_M
```

Lemonade

How to use scotsparaman/fullarton-gml-coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull scotsparaman/fullarton-gml-coder:Q4_K_M

Run and chat with the model

lemonade run user.fullarton-gml-coder-Q4_K_M

List all available models

lemonade list

Fullarton GML Coder v12 Alpha

GML Coder is an experimental 7.6B-parameter assistant for writing, repairing, and explaining modern GameMaker Language (GML). This release is a Q4_K_M GGUF for local testing with Ollama, llama.cpp, or another GGUF-compatible runtime.

This is an alpha release. Generated code should be reviewed and tested in GameMaker before it is used in a project.

Model files: Hugging Face
Configuration and benchmarks: GitHub

Model Details

Base: Qwen/Qwen2.5-Coder-7B-Instruct
Fine-tuning: supervised LoRA, merged into the base model
Quantization: Q4_K_M
Architecture: Qwen2
Parameters: 7.6B
Packaged context length: 32,768 tokens
Primary language: English prompts and GML output
Release artifact: fullarton-gml-coder-v12-q4_k_m.gguf

Ollama

The 4.68 GB GGUF is hosted on Hugging Face because it exceeds GitHub's standard per-file limits. Download fullarton-gml-coder-v12-q4_k_m.gguf, place it beside Modelfile, open a terminal in that directory, and run:

ollama create gml-coder-v12-alpha -f Modelfile
ollama run gml-coder-v12-alpha

Example:

Write WASD 8-direction movement for obj_player. Normalize diagonal speed.
Movement only; do not add collision or drawing.

The included Modelfile supplies the Qwen chat template and GML-specific system instructions. Using only the GGUF in another runtime may produce different scope-control results.

Repository Contents

This GitHub repository contains the Ollama configuration, model card, license, release notes, checksums, and benchmark results. The GGUF itself is distributed through the corresponding model host rather than GitHub.

llama.cpp

llama-cli -m fullarton-gml-coder-v12-q4_k_m.gguf -cnv

Use a Qwen2-compatible chat template if the runtime does not detect the GGUF metadata automatically.

Evaluation

The release artifact was evaluated deterministically on 120 prompts against GML Coder v11 and the quantized base model. Each model answered the same prompts with temperature 0 and seed 42.

Model	Mean	Strict pass	Syntax clean	API clean
GML Coder v12	93.7	77.5%	99.2%	96.7%
GML Coder v11	93.4	76.7%	99.2%	97.5%
Base Qwen2.5 Coder 7B	77.3	33.3%	89.2%	93.3%

Static validation checks required behavior, unwanted scope expansion, delimiter balance, suspicious syntax, invalid WASD constants, and a curated set of fake APIs. It is not a GameMaker compiler. See benchmark-report.md and benchmark-scores.csv for complete results.

Strengths

Concise object event snippets with explicit event placement
Eight-direction movement and diagonal normalization
Avoiding collision, Draw, GUI, and cleanup code when not requested
Correcting several commonly hallucinated GML APIs
Collision and platformer examples when those features are requested

Known Limitations

The strict benchmark pass rate is 77.5%; this is not a stable v1 release.
Some uncommon GameMaker APIs remain unreliable.
Known weak areas include surfaces, sequences, audio, paths, file APIs, constructors, arrays, and some repair/completion prompts.
Static syntax checks cannot guarantee that code compiles.
The model does not know the assets, object hierarchy, room layers, or conventions of a project unless they are supplied in the prompt.
Long or ambiguous tasks can still produce invented project helpers.
GameMaker versions differ. Verify APIs against the manual for the version used by the project.

Training Data and Provenance

The v12 supervised blend contains 2,592 examples. It combines earlier curated GML examples with targeted rows for scope control, bug repair, API correction, editing, completion, and explicitly requested collision/platformer behavior. Some examples were informed by public GameMaker documentation, official tutorials, and locally collected GML project material.

The training dataset is not included in this GGUF package. Before publishing a dataset separately, audit every source and row for copyright, repository license, attribution, privacy, and redistribution requirements.

GameMaker and GameMaker Studio are trademarks of their respective owner. This community model is not affiliated with or endorsed by GameMaker.

License

The base model is distributed under Apache License 2.0. A copy is included in LICENSE. Users are responsible for ensuring that their use and distribution also complies with applicable rights in prompts, generated code, and any external project material.

Recommended Feedback

When reporting a failure, include:

The exact prompt
The complete model response
GameMaker version
Expected behavior
Whether the code compiled
The relevant compiler/runtime error

Downloads last month: 64

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for scotsparaman/fullarton-gml-coder

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(211)

this model