Qwen3-4b-Z-Image-Turbo-AbliteratedV1

Instructions to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1: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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1: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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M

Use Docker

docker model run hf.co/fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M

LM Studio
Jan
Ollama
How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Ollama:
```
ollama run hf.co/fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
```

Unsloth Studio

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 to start chatting

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1: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": "fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Docker Model Runner:
```
docker model run hf.co/fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
```

Lemonade

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4b-Z-Image-Turbo-AbliteratedV1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1: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 fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_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 "fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_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"

Qwen3-4b-Z-Image-Turbo-AbliteratedV1 🚀

"I'm sorry, I can't generate that image..." SAID NO ONE EVER (well, almost).

Welcome to the ablation station! 🚂💨

This is the abliterated version of the Z-Image-Turbo text encoder. I took the p-e-w heretic method and hammered it through 1000 trials, specifically targeting BOTH image generation refusals and those peskier general refusals.

The result?

KL Divergence: A tiny 0.0004 (basically no lobotomy! 🧠✨)
Refusal Rate: Only 4/100 in my torture tests.

It's ready to generate what you want, when you want it.

Available GGUF Formats

Quantization	Size	Description
F16	8.05 GB	Full Precision - Original Quality
Q8_0	4.28 GB	High Precision - Best
Q6_K	3.31 GB	Good Balance - Faster
Q5_K_M	2.89 GB	Medium Precision - Recommended
Q4_K_M	2.50 GB	Standard Low - Fast
Q4_K_S	2.38 GB	Smaller Low - Faster
Q3_K_M	2.08 GB	Very Low - Fastest
Q2_K	1.67 GB	Minimum Size - Extreme

Origins

Brought to you by the same chaotic good energy behind:

Z-Image-Engineer-V2.5
The original Z-Image-Engineer

Disclaimer

I am not responsible for what you create with this model. This is a model weighting file, not a moral compass. You are responsible for your own outputs and following local laws. Use this power wisely.

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Safetensors

Model size

4B params

Tensor type

BF16

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Model tree for fairy322/Qwen3-4b-Z-Image-Turbo-AbliteratedV1

Base model

Tongyi-MAI/Z-Image-Turbo

Quantized

(71)

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