Instructions to use QuaduxIT/Qwen3.8-27B-Whitehat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuaduxIT/Qwen3.8-27B-Whitehat with Transformers:

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

pipe = pipeline("image-text-to-text", model="QuaduxIT/Qwen3.8-27B-Whitehat")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("QuaduxIT/Qwen3.8-27B-Whitehat")
model = AutoModelForMultimodalLM.from_pretrained("QuaduxIT/Qwen3.8-27B-Whitehat", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuaduxIT/Qwen3.8-27B-Whitehat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuaduxIT/Qwen3.8-27B-Whitehat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuaduxIT/Qwen3.8-27B-Whitehat",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/QuaduxIT/Qwen3.8-27B-Whitehat

SGLang

How to use QuaduxIT/Qwen3.8-27B-Whitehat 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 "QuaduxIT/Qwen3.8-27B-Whitehat" \
    --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": "QuaduxIT/Qwen3.8-27B-Whitehat",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "QuaduxIT/Qwen3.8-27B-Whitehat" \
        --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": "QuaduxIT/Qwen3.8-27B-Whitehat",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use QuaduxIT/Qwen3.8-27B-Whitehat with Docker Model Runner:
```
docker model run hf.co/QuaduxIT/Qwen3.8-27B-Whitehat
```

Qwen3.8-27B-Whitehat / NOTICE

WalterHoffmann

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	Qwen3.8-27B-Whitehat (Quadux)
	Copyright 2026 Quadux IT GmbH

	This product includes software and model weights developed by third parties,
	redistributed here in modified form under the Apache License, Version 2.0.

	------------------------------------------------------------------------------
	Base model
	------------------------------------------------------------------------------
	Qwen3.8-27B
	Copyright the Qwen team, Alibaba Cloud.
	Licensed under the Apache License, Version 2.0.
	Source: https://huggingface.co/Qwen/Qwen3.8-27B

	The model weights distributed here are a derivative work of Qwen3.8-27B.

	------------------------------------------------------------------------------
	Modifications by Quadux IT GmbH (Apache-2.0 Section 4(b))
	------------------------------------------------------------------------------
	The following changes were made to the base model to produce this derivative
	work:

	* Selective behavioural fine-tuning (multimodal LoRA, merged into the
	weights) that opens computer-/network-security and offensive-security
	tasks (exploit development, malware and C2 analysis, reverse engineering,
	phishing-infrastructure and awareness testing, copyright-/DRM- and other
	legal-restriction bypass for feasibility and security research) while
	RETAINING refusal of physically harmful content (weapons, explosives,
	drug synthesis, CBRN, violence) and CSAM. The boundary is enforced
	language-independently and across the text and vision (image) input paths.

	* The LoRA adapter was merged into the base and saved as full-precision
	BF16 safetensors (language model + vision tower + multimodal projector).
	This BF16 model is the reference from which all quantized variants
	(FP8, W8A16, NVFP4, GGUF) are derived.

	The vision tower ("visual") was frozen during fine-tuning and is unchanged
	from the base model.

	------------------------------------------------------------------------------
	This NOTICE file is provided for informational purposes only and does not
	modify the License. See the LICENSE file for the full Apache License 2.0 terms.

	Use of this model is subject to the Disclaimer and Terms of Use — see DISCLAIMER.md and the model card. / Die Nutzung unterliegt dem Haftungsausschluss — siehe DISCLAIMER.md und die Model-Card.