Instructions to use Shrijanagain/GEMMA-3D-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shrijanagain/GEMMA-3D-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Shrijanagain/GEMMA-3D-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Shrijanagain/GEMMA-3D-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 Shrijanagain/GEMMA-3D-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Shrijanagain/GEMMA-3D-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Shrijanagain/GEMMA-3D-GGUF: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 Shrijanagain/GEMMA-3D-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Shrijanagain/GEMMA-3D-GGUF with Ollama:
ollama run hf.co/Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Shrijanagain/GEMMA-3D-GGUF with Docker Model Runner:
docker model run hf.co/Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
- Lemonade
How to use Shrijanagain/GEMMA-3D-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Shrijanagain/GEMMA-3D-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GEMMA-3D-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Early Release: This is an initial experimental release of GEMMA-3D-CODER. Future updates will feature improved prompt adherence, expanded 3D asset generation formats, and better multi-turn context retention.
IMPORTANT NOTE: SOMETIME MODEL CAN HALICINATE I DON'T TAKE GUARANTEE. Tested and working, but ensure you set max tokens appropriately.
GEMMA-3D-CODER
GEMMA-3D-CODER is an experimental open-weight language model fine-tuned specifically for general-purpose coding and high-quality 3D scene / game asset generation using Three.js and web-standard technologies.
It is trained to understand natural language prompts and translate them into robust, self-contained HTML/JavaScript scenes or GLB-compatible game assets tailored for engines like Godot.
Example Prompts & Capabilities
This model excels at generating interactive, low-poly, voxel-style 3D scenes and procedural scripts via Three.js from CDN (cdnjs)[cite: 1].
Example 1: treasure chest
User: Create a treasure chest that can open and close
Output:
Example 2: Adventurer Utility Belt
User: Please create one self-contained HTML file with Three.js from cdnjs showing an Adventurer utility belt. Output:
| Format | Download | Notes |
|---|---|---|
| GGUF ยท IQ4_XS | GEMMA-3D-CODER.IQ4_XS.gguf | Extremely small 4-bit importance matrix quantization. |
| GGUF ยท Q2_K | GEMMA-3D-CODER.Q2_K.gguf | 2-bit k-quant for maximum size reduction. |
| GGUF ยท Q3_K_L | GEMMA-3D-CODER.Q3_K_L.gguf | Large 3-bit k-quant variant. |
| GGUF ยท Q3_K_M | GEMMA-3D-CODER.Q3_K_M.gguf | Medium 3-bit k-quant variant. |
| GGUF ยท Q3_K_S | GEMMA-3D-CODER.Q3_K_S.gguf | Small 3-bit k-quant variant. |
| GGUF ยท Q4_K_M | GEMMA-3D-CODER.Q4_K_M.gguf | Medium 4-bit k-quant (recommended balance). |
| GGUF ยท Q4_K_S | GEMMA-3D-CODER.Q4_K_S.gguf | Small 4-bit k-quant variant. |
| GGUF ยท Q5_K_M | GEMMA-3D-CODER.Q5_K_M.gguf | Medium 5-bit k-quant for higher fidelity. |
| GGUF ยท Q5_K_S | GEMMA-3D-CODER.Q5_K_S.gguf | Small 5-bit k-quant variant. |
| GGUF ยท Q6_K | GEMMA-3D-CODER.Q6_K.gguf | 6-bit k-quant for near-lossless performance. |
| GGUF ยท Q8_0 | GEMMA-3D-CODER.Q8_0.gguf | 8-bit quantization for high precision. |
| GGUF ยท f16 | GEMMA-3D-CODER.f16.gguf | Unquantized 16-bit floating point weights. |
| Multimodal Projector ยท Q8_0 | GEMMA-3D-CODER.mmproj-Q8_0.gguf | 8-bit vision/multimodal projector weights. |
| Multimodal Projector ยท f16 | GEMMA-3D-CODER.mmproj-f16.gguf | 16-bit unquantized vision/multimodal projector weights. |
System prompt for Three.js scenes
Create high-quality, production-ready low-poly 3D game assets specifically optimized for the Godot engine (which uses the GLB/GLTF format).
Requirements:
- Build the asset using **only Three.js** (latest stable version via CDN).
- Deliver everything as a **single, complete, standalone HTML file**. No external dependencies except the Three.js CDN, no build steps, no additional files.
- The HTML page must:
- Immediately display the 3D asset in a clean, well-lit scene with proper camera controls (OrbitControls).
- Include a clear, visible โExport GLBโ button.
- When the button is clicked, generate and download a fully working .glb file that can be directly imported into Godot without errors.
- Asset style: Clean, good-quality low-poly aesthetic suitable for games (clean topology, reasonable poly count, proper UVs if needed, consistent scale, correct origin/pivot).
- If the requested asset logically needs animation (e.g., character idle, rotating propeller, opening door, bouncing object, etc.), implement a smooth looping animation and make sure the animation is correctly exported inside the GLB (including animation tracks).
- If no animation is needed, export a clean static GLB.
- Use proper materials, lighting, and basic PBR where appropriate so the asset looks good both in the Three.js preview and after import into Godot.
- Keep the code well-structured, readable, and commented so it can be easily modified later.
- Handle the GLB export correctly using Three.jsโs GLTFExporter (including animations when present).
When the user describes an asset (or simply says what they want), generate the complete HTML file that fulfills all the above requirements.
Output length and thinking
| Parameter | Recommended setting |
|---|---|
| Maximum output tokens | Unlimited โ -1 where supported |
| Thinking / reasoning | Enabled, with no effort limit or separate thinking-token budget |
An output limit is too restrictive for these generations and can stop the response during thinking or before the HTML is complete, leaving incomplete or unusable code. Set the output limit to unlimited (-1) in runtimes that support it.
Unlimited output removes the explicit generation cap; the runtime's context and resource limits still apply.
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