Instructions to use BennyDaBall/Z-Image-Engineer-V6-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BennyDaBall/Z-Image-Engineer-V6-GGUF",
	filename="Z-Image-Engineer-V6-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_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 BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_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 BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

Use Docker

docker model run hf.co/BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BennyDaBall/Z-Image-Engineer-V6-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": "BennyDaBall/Z-Image-Engineer-V6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

Ollama
How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with Ollama:
```
ollama run hf.co/BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
```

Unsloth Studio

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF 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 BennyDaBall/Z-Image-Engineer-V6-GGUF 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 BennyDaBall/Z-Image-Engineer-V6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BennyDaBall/Z-Image-Engineer-V6-GGUF to start chatting

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_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": "BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BennyDaBall/Z-Image-Engineer-V6-GGUF: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 BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with Docker Model Runner:
```
docker model run hf.co/BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M
```

Lemonade

How to use BennyDaBall/Z-Image-Engineer-V6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BennyDaBall/Z-Image-Engineer-V6-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Z-Image-Engineer-V6-GGUF-Q4_K_M

List all available models

lemonade list

Z-Image-Engineer V6 GGUF

GGUF quantized release for Z-Image-Engineer V6.

The main repo contains the merged HF safetensors. This repo contains the quant ladder for LM Studio, ComfyUI CLIPLoaderGGUF, llama.cpp-style loaders, and local prompt-enhancement workflows.

What is this?

Z-Image-Engineer V6 is a SMART DoRA fine-tuned 4B Qwen text encoder from Tongyi-MAI/Z-Image-Turbo.

Use these GGUF files when you want:

LM Studio prompt enhancement
ComfyUI Z-Image text-encoder replacement through CLIPLoaderGGUF
smaller local files than the merged HF safetensors
the same V6 prompt style and conditioning behavior in a quantized format

Quantization Ladder

Filename	Size	Target Use Case
`Z-Image-Engineer-V6-F16.gguf`	7.498 GiB	Full precision reference.
`Z-Image-Engineer-V6-Q8_0.gguf`	3.986 GiB	Near-lossless; used for local A/B testing.
`Z-Image-Engineer-V6-Q6_K.gguf`	3.079 GiB	High-fidelity balanced footprint.
`Z-Image-Engineer-V6-Q5_K_M.gguf`	2.697 GiB	Daily-driver performance-to-size ratio.
`Z-Image-Engineer-V6-Q4_K_M.gguf`	2.331 GiB	Reliable 4-bit standard.
`Z-Image-Engineer-V6-Q3_K_M.gguf`	1.933 GiB	Lightweight option for tighter setups.
`Z-Image-Engineer-V6-MXFP4.gguf`	2.101 GiB	Alternative compact quantization.

Full recursive validation hashes are in HASHES.sha256.

Quick Start

LM Studio

Download a GGUF quant, load it, and prompt it directly:

Enhance this image prompt for Z-Image Turbo: a unicorn

The comparison examples were generated from direct LM Studio user requests like this, with no separate system prompt. V6_SYSTEM_PROMPT.md is included only as an optional preset for people who want a stricter prompt-only chat setup.

ComfyUI

Place a GGUF file into ComfyUI/models/text_encoders/.
Add a CLIPLoaderGGUF node.
Set model type to lumina2.
Use it where the stock Z-Image Qwen text encoder would normally go.

Verified image settings:

UNET: z_image_turbo_bf16.safetensors
VAE: ae.safetensors
Text Encoder: Z-Image-Engineer-V6-Q8_0.gguf
Resolution: 1024x1024
Steps: 8
CFG: 1.0
Sampler: res_multistep
Scheduler: simple
Shift: 3.0

SMART DoRA

V6 was trained with BennyDaBall's SMART DoRA system:

DoRA for direction/magnitude-separated adapter updates.
Entropic regularization for less repetition and broader output variety.
Holographic regularization for cleaner depth-wise feature structure.
Topological regularization for more coherent latent trajectories.
Manifold regularization for stable weight behavior during refinement.

The final V6 build used master-corpus SMART DoRA training, retention pressure, SceneClean SFT32 style restoration, AntiRepeat Binary24 refinement, and a 25% style-restoration / 75% anti-repeat DoRA blend.

Related Repos

Main merged HF release: BennyDaBall/Z-Image-Engineer-V6
Optional ComfyUI workflow repo: ComfyUI-Z-Engineer

Acknowledgements

Tongyi-MAI for the Z-Image Turbo ecosystem.
Qwen for the adaptable text encoder backbone.
The open-source maintainers behind LM Studio, ComfyUI, llama.cpp, PEFT, and Transformers.

Built & trained locally with care by BennyDaBall.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

View +1 variant

Model tree for BennyDaBall/Z-Image-Engineer-V6-GGUF

Base model

Tongyi-MAI/Z-Image-Turbo

Quantized

(54)

this model

Collection including BennyDaBall/Z-Image-Engineer-V6-GGUF

Z-Image-Engineer

Collection

Various versions of my Z-Image-Engineer models. • 11 items • Updated about 8 hours ago • 10