Instructions to use Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM with Transformers:

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

pipe = pipeline("image-text-to-text", model="Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM")
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("Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM")
model = AutoModelForMultimodalLM.from_pretrained("Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM", 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 Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM",
		"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/Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM

SGLang

How to use Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM 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 "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM" \
    --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": "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM",
		"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 "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM" \
        --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": "Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM",
		"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 Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM with Docker Model Runner:
```
docker model run hf.co/Ar4ikov/Qwen3.8-27B-Uncensored-AWQ-W4A16-ASYM
```

best quant i have used from orca

by BlairQ - opened 19 days ago

Discussion

BlairQ

19 days ago

93 t/s average tg with the included mtp , easy 10 requests , images, 780k kv cache fp8 .

does whatever its asked, never hangs, never loops ,

by far my perfect partner. BY FAR.

Ar4ikov

Owner 19 days ago

Thank you so much! I'll keep releasing new quants going forward!
Btw Im using it on my home cluster too.

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