Instructions to use prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX") config = load_config("prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX"
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 prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX"
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 "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX" \ --custom-provider-id mlx-lm \ --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-MLX
CCORE GLB-3D-Coder (ccore/Qwen3.5-4B-GLB-3D-Coder, checkpoint
u008186) is an open-weight (Apache-2.0), specialized 4B-parameter coding model fine-tuned from Qwen/Qwen3.5-4B that turns English text prompts into editable, low-poly 3D game assets and procedural scenes. Given a short or detailed description of a character, prop, vehicle, building, or environment, the model generates a complete standalone Three.js HTML/JavaScript page featuring a live browser preview, optional animations, and an Export GLB button to download ready-to-use.glbfiles for game engines like Godot. Evaluated with thinking enabled (temperature=0.8, top_k=40, top_p=0.95, repeat_penalty=1.0) and showcased against DeepSeek V4.1 Flash 1017 on unseen complex prompts, it is available in compact Q4_K_M and Q8_0 GGUF quantizations, original 5-shard Safetensors (~17.49 GB with FP32 text and BF16 vision weights), and an optional 644 MiB BF16mmprojvision projector for multimodal GGUF runtimes.
Repository Layout
prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX/
├── 8bit/
└── [root / bf16]
Use with mlx
Install the required library:
pip install -U mlx-vlm
BF16 Variant (Base Weights)
The unquantized BF16 mlx weights reside directly in the root repository path:
CLI (Terminal)
# 1. Text-to-GLB 3D Game Asset Generation
python -m mlx_vlm generate \
--model prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX \
--max-tokens 4096 \
--temperature 0.8 \
--prompt "Create a low-poly stone golem boss with glowing cyan crystals and an Export GLB button in a standalone Three.js HTML file."
# 2. Image-Conditioned 3D Asset Generation
python -m mlx_vlm generate \
--model prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX \
--max-tokens 4096 \
--temperature 0.8 \
--prompt "Build a low-poly 3D game asset matching this reference image in Three.js with an Export GLB button." \
--image <path_to_image>
Python API
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
model_path = "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX"
model, processor = load(model_path)
config = load_config(model_path)
prompt = "Create a low-poly stone golem boss with glowing cyan crystals and an Export GLB button in a standalone Three.js HTML file."
formatted_prompt = apply_chat_template(processor, config, prompt)
output = generate(
model,
processor,
formatted_prompt,
max_tokens=4096,
temperature=0.8
)
print(output.text)
8-bit Variant
Access the 8-bit quantized files using --subfolder 8-bit:
CLI (Terminal)
# 1. Text-to-GLB 3D Game Asset Generation
python -m mlx_vlm generate \
--model prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX \
--subfolder 8-bit \
--max-tokens 4096 \
--temperature 0.8 \
--prompt "Create a low-poly stone golem boss with glowing cyan crystals and an Export GLB button in a standalone Three.js HTML file."
# 2. Image-Conditioned 3D Asset Generation
python -m mlx_vlm generate \
--model prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX \
--subfolder 8-bit \
--max-tokens 4096 \
--temperature 0.8 \
--prompt "Build a low-poly 3D game asset matching this reference image in Three.js with an Export GLB button." \
--image <path_to_image>
Python API
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
model_path = "prithivMLmods/Qwen3.5-4B-GLB-3D-Coder-MLX"
model, processor = load(model_path, subfolder="8-bit")
config = load_config(model_path, subfolder="8-bit")
prompt = "Create a low-poly stone golem boss with glowing cyan crystals and an Export GLB button in a standalone Three.js HTML file."
formatted_prompt = apply_chat_template(processor, config, prompt)
output = generate(
model,
processor,
formatted_prompt,
max_tokens=4096,
temperature=0.8
)
print(output.text)
License and Attribution
This model is based on and/or incorporates the following open-source projects and models:
- Qwen3.5-4B (Base): https://huggingface.co/Qwen/Qwen3.5-4B
- CCORE Qwen3.5-4B-GLB-3D-Coder: https://huggingface.co/ccore/Qwen3.5-4B-GLB-3D-Coder
- mlx-vlm: https://github.com/Blaizzy/mlx-vlm
- MLX: https://github.com/ml-explore/mlx
This model is released under the Apache License 2.0.
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