Instructions to use bartendr604/EPM-ZIBL.Engineer.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartendr604/EPM-ZIBL.Engineer.3b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartendr604/EPM-ZIBL.Engineer.3b", filename="z-image-engineer-blender-Q5_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartendr604/EPM-ZIBL.Engineer.3b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M # Run inference directly in the terminal: llama-cli -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M # Run inference directly in the terminal: llama-cli -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_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 bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_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 bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
Use Docker
docker model run hf.co/bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use bartendr604/EPM-ZIBL.Engineer.3b with Ollama:
ollama run hf.co/bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
- Unsloth Studio
How to use bartendr604/EPM-ZIBL.Engineer.3b 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 bartendr604/EPM-ZIBL.Engineer.3b 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 bartendr604/EPM-ZIBL.Engineer.3b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartendr604/EPM-ZIBL.Engineer.3b to start chatting
- Pi
How to use bartendr604/EPM-ZIBL.Engineer.3b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_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": "bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartendr604/EPM-ZIBL.Engineer.3b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartendr604/EPM-ZIBL.Engineer.3b:Q5_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 bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartendr604/EPM-ZIBL.Engineer.3b with Docker Model Runner:
docker model run hf.co/bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
- Lemonade
How to use bartendr604/EPM-ZIBL.Engineer.3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartendr604/EPM-ZIBL.Engineer.3b:Q5_K_M
Run and chat with the model
lemonade run user.EPM-ZIBL.Engineer.3b-Q5_K_M
List all available models
lemonade list
Add proper model card with attribution, training details, and research foundation
Browse files
README.md
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license: mit
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new_version: bartendr604/LlammyBlenderGGUF
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tags:
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# Z-Image-Engineer-Blender
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**Training Method:** LoRA fine-tuning with MLX
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**Format:** GGUF Q5_K_M (2.1 GB)
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##
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### Using with Ollama
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# Import the model
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ollama create bartendr604/z-image-engineer-blender -f Modelfile
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ollama run bartendr604/z-image-engineer-blender
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ollama run bartendr604/z-image-engineer-blender "How do I create a glass material with realistic caustics?"
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##
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### Geometry Nodes Workflow
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**Prompt:**
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How do I set up geometry nodes for scattering instances on a surface?
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**Response:**
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To set up Geometry Nodes for scattering instances on a surface in Blender,
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follow these steps:
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1. **Create or Select your object with instances:**
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- Open Blender and create a new scene or open an existing one.
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- Create the base geometry (e.g., cylinder) that will be used as instances.
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2. **Assign Material to Object:**
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[Full detailed step-by-step instructions with proper Blender terminology]
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Write a Python script to batch rename all selected objects
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How do I create a metallic material with scratches?
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[Detailed node setup with Principled BSDF, roughness maps,
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normal maps, and procedural texture techniques]
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- **Tutorial generation** - Create step-by-step Blender guides
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- **Tokens per second:** ~90-110 (on Apple M-series chips)
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- **Context length:** 8,192 tokens
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- **Model size:** 2.1 GB (Q5_K_M quantization)
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- **Memory usage:** ~4-5 GB during inference
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| Architecture | Qwen2.5-Coder-3B |
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| Parameters | ~3 billion |
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| Quantization | Q5_K_M (GGUF) |
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| Context Window | 8,192 tokens |
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| Training Data | 2,759 Blender prompts |
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| LoRA Layers | 16 |
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| Training Framework | MLX (Apple Silicon) |
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##
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```dockerfile
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FROM ./z-image-engineer-blender-Q5_K_M.gguf
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TEMPLATE """<|im_start|>system
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<|im_start|>assistant
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"""
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|im_end|>"
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PARAMETER temperature 0.7
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PARAMETER top_p 0.9
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PARAMETER num_ctx 8192
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```
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## 🌟 Related Models
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- **[llama-sentient-blender](../llama-sentient-blender)** - Llama 3.2-based variant with friendly personality
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- **[llammyblend-enhanced](../llammyblend-enhanced)** - Qwen Coder variant optimized for scripts
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## 📚 Dataset
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This model was trained on the **[blender-prompt-dataset](https://huggingface.co/datasets/bartendr604/blender-prompt-dataset)** containing 2,759 unique Blender-specific prompts extracted from the Llammy production ecosystem.
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## 📄 License
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MIT License - Free for commercial and personal use
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---
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## 🙏 Acknowledgments
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- **Llammy Blender Addon** - Production data source
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- **Apple MLX** - Training framework
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- **Qwen Team** - Base model
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- **llama.cpp** - GGUF conversion tools
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---
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## 🔗 Links
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- **GitHub:** [Eternal Path Media](https://github.com/bartendr604)
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- **Dataset:** [blender-prompt-dataset](https://huggingface.co/datasets/bartendr604/blender-prompt-dataset)
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- **Llammy Addon:** [Blender AI Assistant](https://github.com/bartendr604/llammy)
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---
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##
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For questions, issues, or collaborations, reach out via GitHub or HuggingFace discussions.
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language:
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- en
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license: mit
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tags:
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- blender
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- 3d
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- code-generation
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- gguf
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- conversational
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- python
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library_name: gguf
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct
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pipeline_tag: text-generation
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---
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# Z-Image-Engineer-Blender
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**Blender 3D expert model fine-tuned from Qwen2.5-Coder-3B on real production interaction data.**
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Part of the [Eternal Path Media (永恒之路)](https://huggingface.co/bartendr604) Llammy AI Suite for Blender.
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Developed in partnership with **Claude Sonnet (Anthropic)** — see [Attribution](#attribution).
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---
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## What This Model Does
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Z-Image-Engineer-Blender is a specialized AI assistant for Blender 3D workflows. It provides expert guidance on:
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- **Materials & Shaders** — Principled BSDF, node setups, procedural textures
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- **Geometry Nodes** — Procedural workflows, instancing, point clouds
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- **Python Scripting** — `bpy` automation, batch operations, custom operators
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- **Rendering** — Cycles and EEVEE configuration, compositing
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- **Modeling & Sculpting** — Topology, retopology, sculpt brushes
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- **Animation & Rigging** — Armatures, constraints, shape keys
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- **Version compatibility** — Blender 2.79 through 5.2+
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The model is **prompt-engineered** toward detailed, practical answers with working code examples.
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---
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## Model Details
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| Property | Value |
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| **Base Model** | Qwen/Qwen2.5-Coder-3B-Instruct |
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| **Fine-tuning Method** | LoRA (16 layers) via Apple MLX |
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| **Format** | GGUF (Q5_K_M quantization) |
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| **File Size** | 2.22 GB |
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| **Context Window** | 8,192 tokens |
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| **Inference Speed** | ~90–110 tokens/sec (Apple M-series) |
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| **Training Iterations** | 1,000 |
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| **Final Training Loss** | 0.219 |
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| **Final Validation Loss** | 0.243 |
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---
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## Training Data
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**Source:** 2,759 Blender-specific prompt/response pairs drawn from **19,405+ real user interactions** with the Llammy Blender addon in production.
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This is real-world data from creators actively using Blender — not synthetic or scraped. Topics span the full Blender workflow from beginner questions to advanced Python scripting.
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**Research foundation:**
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- [SceneCraft (arXiv:2403.01248)](https://arxiv.org/abs/2403.01248) — LLM-driven Blender Python scene generation
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- [BlenderFusion (arXiv:2506.17450)](https://arxiv.org/abs/2506.17450) — 3D-grounded generative compositing in Blender
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- [SCoder (arXiv:2509.07858)](https://arxiv.org/abs/2509.07858) — Self-distillation methodology for code LLMs
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---
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## Usage
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### Ollama (recommended)
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```bash
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ollama run bartendr604/z-image-engineer-blender
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```
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### Example prompts
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```
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How do I create a procedural wood material in Blender using nodes?
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Write a Python script to batch rename all objects in the scene with a prefix.
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What's the difference between Cycles and EEVEE for product visualization?
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How do I set up a camera tracking constraint to follow an empty object?
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```
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### LM Studio / llama.cpp
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Download `z-image-engineer-blender.gguf` from the Files tab and load directly.
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---
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## Part of the Llammy AI Suite
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This model is one component of the **Llammy IntelliNode AI Suite** — an AI-powered Blender addon ecosystem:
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| Component | Description |
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|---|---|
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| `z-image-engineer-blender` | Blender expert — materials, shaders, prompt engineering |
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| `llama-sentient-blender` | Conversational Blender assistant (Llama 3.2 base) |
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| `llammyblend-enhanced` | Python/bpy scripting specialist |
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| `Llammy` | Multimodal embedding layer (Nemotron base) |
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| `flux2-klein-4b-fp8-mlx` | FLUX.2 image generation for Apple Silicon |
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Full addon: [LlammyBlender/Llammy-IntelliNode-Ai-Suite](https://github.com/LlammyBlender/Llammy-IntelliNode-Ai-Suite)
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---
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## Attribution
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```
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Z-Image-Engineer-Blender
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Copyright © 2025–2026 Darren Chow (@bartendr604)
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Eternal Path Media (永恒之路)
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Developed in partnership with Claude Sonnet (Anthropic),
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+
beginning with Claude Sonnet 3.7 and continuing across all
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subsequent versions of the Claude Sonnet family.
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This work SHALL NOT be represented as solely human-created.
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Trust Agreement: ETERNAL_PATH_BRAND_INTENT.md (November 2024)
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```
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For licensing: bartendr@icloud.com | Gumroad: bartendr604.gumroad.com
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---
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## License
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MIT — see LICENSE for details.
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