Instructions to use jc-builds/Z-Image-Turbo-iOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jc-builds/Z-Image-Turbo-iOS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jc-builds/Z-Image-Turbo-iOS",
	filename="Qwen3-4B-Instruct-2507-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "\"Astronaut riding a horse\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jc-builds/Z-Image-Turbo-iOS with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jc-builds/Z-Image-Turbo-iOS:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jc-builds/Z-Image-Turbo-iOS:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jc-builds/Z-Image-Turbo-iOS:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf jc-builds/Z-Image-Turbo-iOS: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 jc-builds/Z-Image-Turbo-iOS:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/Z-Image-Turbo-iOS: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 jc-builds/Z-Image-Turbo-iOS:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/Z-Image-Turbo-iOS:Q4_K_M

Use Docker

docker model run hf.co/jc-builds/Z-Image-Turbo-iOS:Q4_K_M

LM Studio
Jan
Ollama
How to use jc-builds/Z-Image-Turbo-iOS with Ollama:
```
ollama run hf.co/jc-builds/Z-Image-Turbo-iOS:Q4_K_M
```

Unsloth Studio new

How to use jc-builds/Z-Image-Turbo-iOS 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 jc-builds/Z-Image-Turbo-iOS 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 jc-builds/Z-Image-Turbo-iOS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jc-builds/Z-Image-Turbo-iOS to start chatting

Pi new

How to use jc-builds/Z-Image-Turbo-iOS with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jc-builds/Z-Image-Turbo-iOS: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": "jc-builds/Z-Image-Turbo-iOS:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jc-builds/Z-Image-Turbo-iOS with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jc-builds/Z-Image-Turbo-iOS: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 jc-builds/Z-Image-Turbo-iOS:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use jc-builds/Z-Image-Turbo-iOS with Docker Model Runner:
```
docker model run hf.co/jc-builds/Z-Image-Turbo-iOS:Q4_K_M
```

Lemonade

How to use jc-builds/Z-Image-Turbo-iOS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jc-builds/Z-Image-Turbo-iOS:Q4_K_M

Run and chat with the model

lemonade run user.Z-Image-Turbo-iOS-Q4_K_M

List all available models

lemonade list

jc-builds commited on 8 days ago

Commit

3e5d06b

verified ·

1 Parent(s): bcfb940

docs: declare SFW-by-default safety policy + reference safety_negative_prompt.txt

Browse files

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -43,9 +43,23 @@ Z-Image-Turbo is a 6B-parameter [**S3-DiT**](https://arxiv.org/abs/2511.22699) (
 | [`z-image-turbo-Q3_K_M.gguf`](./z-image-turbo-Q3_K_M.gguf) | Diffusion transformer — 6B params, Q3_K_M quant | 3.9 GB |
 | [`Qwen3-4B-Instruct-2507-Q4_K_M.gguf`](./Qwen3-4B-Instruct-2507-Q4_K_M.gguf) | Text encoder | 2.3 GB |
 | [`ae.safetensors`](./ae.safetensors) | VAE (from FLUX.1) | 320 MB |
 Total bundle size: **~6.5 GB**. Total GPU residency at generation time: ~7-8 GB (weights + activations + KV cache).
 ## Quick start (Mirage)
 ```swift

 | [`z-image-turbo-Q3_K_M.gguf`](./z-image-turbo-Q3_K_M.gguf) | Diffusion transformer — 6B params, Q3_K_M quant | 3.9 GB |
 | [`Qwen3-4B-Instruct-2507-Q4_K_M.gguf`](./Qwen3-4B-Instruct-2507-Q4_K_M.gguf) | Text encoder | 2.3 GB |
 | [`ae.safetensors`](./ae.safetensors) | VAE (from FLUX.1) | 320 MB |
+| [`safety_negative_prompt.txt`](./safety_negative_prompt.txt) | Recommended default negative prompt to apply at inference time for SFW-by-default deployments | <1 KB |
 Total bundle size: **~6.5 GB**. Total GPU residency at generation time: ~7-8 GB (weights + activations + KV cache).
+## Safety / SFW-by-default
+This bundle is intended for shipping in consumer apps and ships with a recommended default negative prompt at [`safety_negative_prompt.txt`](./safety_negative_prompt.txt). Consumers building on top of this bundle SHOULD load the file and prepend its contents to any user-supplied negative prompt by default, with an explicit user-facing opt-out for adult/artistic contexts.
+The blocklist covers:
+- **Child safety** — explicit terms blocking sexualised content involving minors or apparent minors (loaded first / highest weight in SD-style negative prompts)
+- **Adult / explicit** — `nsfw`, `nude`, `explicit`, `sexual`, anatomical detail
+- **Gore + graphic violence** — `gore`, `blood`, `mutilation`, etc.
+- **Hate symbols** — `swastika`, `nazi`, `extremist`
+Diffusion models steer *away* from negative-prompt concepts; they don't binary-reject them. A sufficiently determined prompt can still produce undesirable output, so apps shipping this bundle to general audiences should pair the negative-prompt filter with output-side classification (e.g. a CSAM/NSFW classifier on the generated `CGImage`) before display.
 ## Quick start (Mirage)
 ```swift