Instructions to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF 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 Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_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 Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Use Docker

docker model run hf.co/Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Llama new
LM Studio
Jan

vLLM

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Ollama
How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with Ollama:
```
ollama run hf.co/Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M
```
Unsloth Desktop

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_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": "Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M
```

Lemonade

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_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 Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_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 "Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF:Q3_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"

CCORE GLB-3D-Coder - Q3_K_M

Q3_K_M GGUF of CCORE's original u008186 checkpoint, released by its creator on November 5, 2026. This is the GLB asset-coding model, distinct from CCORE's earlier Three.js scene model. Chungulus provides this independent community quantization; it is not an official Qwen or CCORE release.

The original checkpoint has 4,539,265,536 parameters: F32 text weights and BF16 vision weights. We exported its 4,205,751,296-parameter text network to F16 (retaining sensitive F32 tensors), then independently quantized that export to Q3_K_M. The unchanged original mmproj-Qwen3.5-4B-BF16.gguf is included for images. Multi-token prediction is not exported. No low-bit checkpoint was requantized, and no calibration or importance matrix was used.

Use

Use a recent llama.cpp runtime supporting Qwen3.5. The tested build is commit 6414d5b0c9e5895473036c90c2bd305ce4ceccc4. Disable MTP/speculative prediction in frontends that attempt to enable it.

hf download Chungulus/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M-GGUF --local-dir ./Qwen3.5-4B-GLB-3D-Coder-Q3_K_M
llama-completion -m ./Qwen3.5-4B-GLB-3D-Coder-Q3_K_M/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M.gguf -cnv -st --jinja -c 16384 -ngl 99 -n 8192 -p 'Create a low-poly red cube with an Export GLB button in one complete Three.js HTML file.'
llama-mtmd-cli -m ./Qwen3.5-4B-GLB-3D-Coder-Q3_K_M/Qwen3.5-4B-GLB-3D-Coder-Q3_K_M.gguf --mmproj ./Qwen3.5-4B-GLB-3D-Coder-Q3_K_M/mmproj-Qwen3.5-4B-BF16.gguf --image input.png -p 'Describe this image.' --image-min-tokens 1024 -c 8192 -ngl 99

The original model card supplies the creator's asset-generation system prompt and recommends temperature 0.8, top-k 40, top-p 0.95, min-p 0, repeat penalty 1 and thinking enabled. Allow enough output for the complete HTML. Our bounded deterministic checks used temperature 0 and thinking disabled; they do not reproduce the creator's evaluation.

Actual checks and limits

This artifact passed native-runtime arithmetic, JSON output and two image-understanding cases with the included projector: identify the central colored square in opposing red/blue fixtures, using 1024 image tokens, temperature 0 and seed 42. Its generated cube/export example passed JavaScript syntax and expected Three.js/GLTFExporter API checks after optional fence stripping and HTML-entity decoding. Raw and normalized results are recorded separately in validation.json; the raw output is retained locally. example-scene.html is the normalized generated example.

An initial 256-image-token check asked only for the color of the "large square", which can also refer to the white canvas, and did not explicitly fix sampling settings. This format answered red (initial intended-red check passed: True). That result is retained alongside the later, disambiguated two-color checks; unsuccessful initial cases have not been erased. Image understanding is limited evidence, not a guarantee of reliable grounding.

These are short functional checks. Browser rendering, successful GLB download/import, video, full-context behavior and benchmark accuracy were not tested. Inspect and test generated assets in your editor, as the creator recommends. The complete output tensor names/shapes match the unquantized export, the actual GGUF file type was inspected, and source/upload SHA256 digests were verified. quantizatia-manifest.json records the exact source/tool pins and source digests.

License

Apache 2.0. Retain the original license and attribution. Based on Qwen/Qwen3.5-4B and independently fine-tuned by CCORE. The original LICENSE and chat_template.jinja are included.