Instructions to use BennyDaBall/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
- llama.cpp
How to use BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M # Run inference directly in the terminal: llama cli -hf BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M # Run inference directly in the terminal: llama cli -hf BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
Use Docker
docker model run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Ollama:
ollama run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
- Unsloth Desktop
- Pi
How to use BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Docker Model Runner:
docker model run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
- Lemonade
How to use BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BennyDaBall/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 BennyDaBall/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 BennyDaBall/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 BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use BennyDaBall/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 BennyDaBall/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 "BennyDaBall/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"
Update README with correct base model
Browse files
README.md
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---
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license: apache-2.0
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base_model:
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tags:
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- abliterated
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- heretic
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Welcome to the ablation station! 🚂💨
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This is the **abliterated** version of the Z-Image-Turbo text encoder.
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The result?
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- **KL Divergence:** A tiny `0.0004` (basically no lobotomy! 🧠✨)
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- **Refusal Rate:** Only `4/100` in
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It's ready to generate what you want, when you want it.
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- [Z-Image-Engineer-V2.5](https://huggingface.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V2.5)
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- The original [Z-Image-Engineer](https://huggingface.co/BennyDaBall/qwen3-4b-Z-Image-Engineer)
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---
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license: apache-2.0
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base_model: Tongyi-MAI/Z-Image-Turbo
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tags:
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- abliterated
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- heretic
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Welcome to the ablation station! 🚂💨
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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.
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The result?
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- **KL Divergence:** A tiny `0.0004` (basically no lobotomy! 🧠✨)
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- **Refusal Rate:** Only `4/100` in my torture tests.
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It's ready to generate what you want, when you want it.
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- [Z-Image-Engineer-V2.5](https://huggingface.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V2.5)
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- The original [Z-Image-Engineer](https://huggingface.co/BennyDaBall/qwen3-4b-Z-Image-Engineer)
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## Disclaimer
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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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