Instructions to use Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound with Transformers:

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

pipe = pipeline("image-text-to-text", model="Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound",
		"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/Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound

SGLang

How to use Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound 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 "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound" \
    --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": "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound",
		"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 "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound" \
        --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": "Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound",
		"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 Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound with Docker Model Runner:
```
docker model run hf.co/Intel/Qwen3.6-35B-A3B-int2-mixed-CT-AutoRound
```

Need help

by tooltd - opened Aug 14

Discussion

tooltd

Aug 14

Hi Intel team,
Could you please help me create high-quality Q4_K_M and Q4_K_S versions for Qwen3.8 27B GGUF?
I don't have powerful enough hardware to do it myself. Please help me out.
Thank you so much!

wenhuach

Intel org Aug 18

There are many GGUF variants, and GGUF is not our top priority. We can generate them if there is real demand from the community.

tooltd

Aug 18

I’ve found GGUF output generated using AutoRound to be very stable for Qwen3.6 27B. So far, no one in the community has created one for Qwen3.8 27B.
Thank you, if possible, I hope you could create it—I think there are people who would find it useful.

wenhuach

Intel org Aug 20

https://huggingface.co/Intel/Qwen3.8-27B-q4km-AutoRound

tooltd

Aug 20

https://huggingface.co/Intel/Qwen3.8-27B-q4km-AutoRound

Thank you very much 😃

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