Instructions to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 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-Engineer-V4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4: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-Engineer-V4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4: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-Engineer-V4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4: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-Engineer-V4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M

Use Docker

docker model run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M

LM Studio
Jan

vLLM

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4"
# 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/Qwen3-4b-Z-Image-Engineer-V4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M

SGLang

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with Ollama:
```
ollama run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M
```
Unsloth Desktop

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 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-Engineer-V4: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-Engineer-V4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with Docker Model Runner:
```
docker model run hf.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M
```

Lemonade

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-4b-Z-Image-Engineer-V4-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 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-Engineer-V4: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-Engineer-V4:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4 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-Engineer-V4: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-Engineer-V4: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"

Awesome

by scruffynerf - opened Feb 7

Discussion

scruffynerf

Feb 7

Congrats on v4 release!

BennyDaBall

Owner Feb 7

Thank you! It was a big undertaking, lots of little hurdles. "SMART" training really changed the game on how the model performs - SMART training minimizes the normal next-token cross-entropy plus auxiliary losses on hidden states and weights: an entropy term penalizes output entropy scaled by a learned “knowledge-mass” estimate, a holographic-depth term matches layerwise representation entropy to a 1/depth target profile, a differentiable topology term discourages disconnected/holey latent structure, and a manifold term penalizes projection weights with uneven row/column sums. Together these constraints steer predictions, depth dynamics, latent geometry, and weight structure toward more stable representations than cross-entropy alone... The qualitative samples I've gathered show a clear winner with smart training checkpoint-by-checkpoint, even when loss metrics are higher.

V5 aka Z-Image-Engineer-FINAL will be done training in a few days on a dataset 2x the size of V4 (55k+ more synthetic prompt enhancement pairs!) - If all goes to plan it should be able to "restyle" prompts better than V4 - I also increased system instruction diversity by 35%.

scruffynerf

Feb 20

any word on v5 yet? and do reach out to me, wanna discuss another training of interest...

BennyDaBall

Owner Feb 22

Hit me up on x @bennydaball_og

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