Instructions to use ccore/Qwen3.5-4B-GLB-3D-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ccore/Qwen3.5-4B-GLB-3D-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 ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ccore/Qwen3.5-4B-GLB-3D-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 ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ccore/Qwen3.5-4B-GLB-3D-Coder: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 ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M

Use Docker

docker model run hf.co/ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ccore/Qwen3.5-4B-GLB-3D-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccore/Qwen3.5-4B-GLB-3D-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": "ccore/Qwen3.5-4B-GLB-3D-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M

Ollama
How to use ccore/Qwen3.5-4B-GLB-3D-Coder with Ollama:
```
ollama run hf.co/ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M
```
Unsloth Desktop

How to use ccore/Qwen3.5-4B-GLB-3D-Coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ccore/Qwen3.5-4B-GLB-3D-Coder: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": "ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ccore/Qwen3.5-4B-GLB-3D-Coder with Docker Model Runner:
```
docker model run hf.co/ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M
```

Lemonade

How to use ccore/Qwen3.5-4B-GLB-3D-Coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-GLB-3D-Coder-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ccore/Qwen3.5-4B-GLB-3D-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 ccore/Qwen3.5-4B-GLB-3D-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 ccore/Qwen3.5-4B-GLB-3D-Coder:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ccore/Qwen3.5-4B-GLB-3D-Coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ccore/Qwen3.5-4B-GLB-3D-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 "ccore/Qwen3.5-4B-GLB-3D-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"

Qwen3.5-4B-GLB-3D-Coder

A 4B coding model that generates standalone Three.js HTML/JavaScript scenes with GLB export, for use in editors such as Godot.

An independent CCORE preview based on Qwen/Qwen3.5-4B. This is not an official Qwen release.

Latest checkpoint update

Latest GGUF update: November 2, 2026 at 04:50 UTC. Both Q4_K_M and Q8_0 were replaced with a newer checkpoint, with substantially better visual quality in my tests. Sampling settings and the system prompt are unchanged.

If you downloaded a GGUF before that timestamp, delete the old copy and download it again in LM Studio or your app. The filenames have not changed.

Community showcase: A 3D RPG built with GLB-3D-Coder!

Naoki Kishida (@kis) built a playable 3D RPG demo using assets generated with this model. It’s great to see these assets being used in a game—thanks for sharing it with the community!

Play the 3D RPG demo

Available GGUF files

Quantization	File
Q8_0	Qwen3.5-4B-GLB-3D-Coder-Q8_0.gguf
Q4_K_M	Qwen3.5-4B-GLB-3D-Coder-Q4_K_M.gguf

Optional vision support

For image-reference inputs in a runtime that supports Qwen3.5 multimodal GGUF, load the BF16 vision projector (644 MiB) alongside either Q4_K_M or Q8_0. It is optional for text-only use.

This is the original, unmodified Qwen3.5-4B vision encoder/projector from lmstudio-community/Qwen3.5-4B-GGUF, under Apache 2.0, also provided with CCORE’s previous 3D Coder model.

How to use

Use the system message below, then send a short English asset request. Save the generated HTML, open it in a browser, and click Export GLB to download the asset for your editor. Request an animation explicitly when needed.

In my tests, most generations work on the first attempt, especially in English. Some still need a retry or a small code correction.

Generation settings

Parameter	Value
Temperature	0.8
Top K	40
Top P	0.95
Min P	0.0

These are my current settings; feel free to experiment with other combinations.

System prompt

Create game assets for the Godot editor, which uses GLB files. Create whatever the user requests as a good-quality low-poly asset using only Three.js in one complete standalone HTML file. The HTML must show the asset and include an Export GLB button that downloads a working GLB. If the requested asset needs animation, add it and include the animation in the exported GLB; otherwise make it static.

Example results — updated checkpoint

These assets were generated by this checkpoint using prompts excluded from the model’s training data and the dataset planned for release.

Jungle siege platform	Weathered snow siege platform

Prompt: `Build a jungle siege platform`	Prompt: `Build a weathered snow siege platform`
Blue goblin brute	Red orc leader

Prompt: `Create a blue goblin brute`	Prompt: `Create a red orc Leader`
Reinforced mountain command post — result 1	Reinforced mountain command post — result 2

Prompt: `Make a reinforced montain command post`	Same prompt: `Make a reinforced montain command post`

Exported assets in Godot

The exported GLB assets after import into the Godot editor:

Godot import — example 1	Godot import — example 2

Training domains and prompt ideas

The current preview was trained across 25 game-asset domains. Here is one prompt idea to try for each category:

Domain	Prompt idea
Characters and NPCs	`Create a village blacksmith wearing a blue apron.`
Enemies	`Create a swamp goblin scout with a wooden shield.`
Bosses	`Create a stone golem boss with glowing green crystals.`
Companions and creatures	`Create a tiny fox companion wearing a red scarf.`
Weapons and armor	`Create a short iron sword with a leather-wrapped grip.`
Items, tools, and pickups	`Create a blue mana potion in a hexagonal glass bottle.`
Props and furniture	`Create a weathered wooden workbench with a small lantern.`
Nature and environment	`Create a pine tree growing on a rocky mound.`
Modular architecture	`Create a stone dungeon doorway module with an iron frame.`
Interactive objects	`Create a wooden gate with an opening animation.`
Ground transport	`Create a compact courier motorcycle with cargo boxes.`
Air and space transport	`Create a small cargo airship with twin propellers.`
Watercraft	`Create a weathered fishing skiff with nets and wooden crates.`
Industrial machinery	`Create a compact mining conveyor with metal support legs.`
Laboratory and medical assets	`Create a portable botanical analysis station with sample jars.`
Urban services	`Create a modern bus stop with a bench and route sign.`
Sports and playgrounds	`Create a small playground climbing wall with colorful holds.`
Farming and homestead	`Create an orchard harvest cart filled with wooden fruit crates.`
Cuisine and markets	`Create a bakery display counter with bread baskets.`
Museums and field research	`Create an ancient artifact display case on a stone pedestal.`
Traps and hazards	`Create a hidden spiked floor trap with stone tiles.`
Festivals and ceremonies	`Create a harvest festival booth with colorful banners.`
Battlefield scenery	`Create a reinforced frontier command post with sandbag defenses.`
Underwater structures	`Create a coral observation dome with a small docking platform.`
Puzzles and gameplay markers	`Create a glowing rune sequence console with three stone buttons.`

You can vary the style, setting, materials, colors, and details. Share your results, settings, and any issues you find!

Release status and next steps

The model is continuing to improve, so I’ve decided to double its training dataset. The additional examples have just finished generating, and I’ll continue training through tomorrow before finalizing the release.

The original dataset focused on simple prompts, making asset generation accessible to people without a technical background. I noticed that my previous 3D Coder model generalized better across different prompts, so I expanded the GLB-focused data to cover more domains and improve support for more detailed, customized requests.

Once training finishes, I’ll release the complete dataset, including all the additional examples, alongside the full Safetensors weights, just as I did with the previous model.

For the next model, I’m planning a dataset of 100,000 high-quality examples, with context lengths ranging from 8K to 128K tokens. The target is a training run roughly 15× larger than the one used for the current model.

My first goal is reliable one-shot asset generation. After that, I plan to develop a version designed to work directly inside an agent harness, where it can generate, test, and refine assets as part of a broader workflow.

Preparing this dataset and running the training will require more resources for data generation, validation, and GPU compute. If you can contribute toward making this next training run possible, your support would be a huge help.

❤️ Support Future Open-Source AI Models

I'm independently developing and releasing specialized AI models and datasets for 3D generation, procedural environments, and creative coding, with the goal of making these tools accessible to everyone.

Training new models, generating high-quality datasets, validating thousands of examples, and running experiments require significant GPU resources, time, and effort.

If you enjoy this project and would like to help me train more models, expand the datasets, and explore new capabilities, any contribution is greatly appreciated!

My goal is to keep sharing models, weights, datasets, and improvements openly with the community.