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

Libraries

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

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

pipe = pipeline("text-generation", model="BennyDaBall/Z-Image-Engineer-V6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BennyDaBall/Z-Image-Engineer-V6")
model = AutoModelForCausalLM.from_pretrained("BennyDaBall/Z-Image-Engineer-V6", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BennyDaBall/Z-Image-Engineer-V6 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use BennyDaBall/Z-Image-Engineer-V6 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/Z-Image-Engineer-V6" \
    --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/Z-Image-Engineer-V6",
		"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/Z-Image-Engineer-V6" \
        --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/Z-Image-Engineer-V6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

BennyDaBall commited on Jun 22

Commit

7450bb1

verified ·

1 Parent(s): ac18852

Add/refresh X link: Follow me on X @BennyDaBall_OG

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@@ -21,7 +21,7 @@ tags:
 # Z-Image-Engineer V6 (4B)
-*by BennyDaBall · follow [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) on X for new releases & updates*
 ## Model Metadata
@@ -179,4 +179,4 @@ This model is a prompt engineer and text encoder. Diffusion is still diffusion;
 **Built & trained locally with care by BennyDaBall.**
-Follow for new releases and updates: **[@BennyDaBall_OG on X](https://x.com/BennyDaBall_OG)**

 # Z-Image-Engineer V6 (4B)
+**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
 ## Model Metadata
 **Built & trained locally with care by BennyDaBall.**
+**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**