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
```

WalterHoffmann commited on 14 days ago

Commit

d067997

verified ·

1 Parent(s): 5b181ed

Document MTP speculative decoding (llama.cpp draft-mtp + vLLM)

Browse files

Files changed (1) hide show

README.md +18 -0

README.md CHANGED Viewed

@@ -249,6 +249,24 @@ processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
 # No system prompt required — the behaviour is intrinsic to the weights.
 ```
 ## Responsible use
 **Intended**: authorized penetration testing and red-teaming; internal vulnerability

 # No system prompt required — the behaviour is intrinsic to the weights.
 ```
+## Speculative decoding (MTP)
+This model carries the base **MTP (multi-token-prediction) head** (`model-mtp-head.safetensors`),
+so vLLM can *self*-speculate one token ahead with **no separate draft model**:
+```bash
+vllm serve QuaduxIT/Qwen3.8-27B-Whitehat \
+  --trust-remote-code \
+  --max-model-len 32768 \
+  --speculative-config '{"method":"mtp","num_speculative_tokens":1}' \
+  --host 0.0.0.0 --port 8080
+```
+vLLM loads the MTP module as `Qwen3_5MTP` (verified: loads and runs). For **llama.cpp / GGUF**
+speculative decoding on the GGUF build, see the
+[`Qwen3.8-27B-Whitehat-GGUF`](https://huggingface.co/QuaduxIT/Qwen3.8-27B-Whitehat-GGUF) repo
+(`--spec-type draft-mtp` — the MTP head ships as `blk.64`).
 ## Responsible use
 **Intended**: authorized penetration testing and red-teaming; internal vulnerability