Instructions to use Qwen/Qwen3.5-397B-A17B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3.5-397B-A17B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.5-397B-A17B")
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("Qwen/Qwen3.5-397B-A17B")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.5-397B-A17B", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen3.5-397B-A17B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen3.5-397B-A17B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/Qwen3.5-397B-A17B",
		"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/Qwen/Qwen3.5-397B-A17B

SGLang

How to use Qwen/Qwen3.5-397B-A17B 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 "Qwen/Qwen3.5-397B-A17B" \
    --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": "Qwen/Qwen3.5-397B-A17B",
		"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 "Qwen/Qwen3.5-397B-A17B" \
        --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": "Qwen/Qwen3.5-397B-A17B",
		"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 Qwen/Qwen3.5-397B-A17B with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3.5-397B-A17B
```

latent-variable commited on Apr 8

Commit

6710fb6

verified ·

1 Parent(s): 60c2d3e

fix chat template to avoid empty historical `<think>` blocks

Browse files

This fixes a chat template issue where historical assistant turns can emit empty `<think>...</think>` blocks even when `reasoning_content` is empty.

That matters because these empty historical `<think>` blocks change the serialized prompt without adding any useful information.

Why this is important:
- it reduces unnecessary prompt drift
- it improves prefix-cache reuse
- it helps avoid avoidable cache misses
- it reduces extra token processing caused by equivalent histories rendering differently

In practice, this means less wasted compute and better cache stability, especially in longer multi-turn or tool-using conversations.

The change is intentionally minimal:
- keep the historical `<think>` wrapper when `reasoning_content` is actually present
- do not emit an empty `<think>` block when there is no reasoning content

Without this guard, the template can produce prior turns like:

assistant
<think>

</think>

<tool_call>...

instead of rendering just the assistant content or tool call directly.

So this change preserves real reasoning content while avoiding empty reasoning scaffolding that can hurt caching behavior.

Files changed (1) hide show

chat_template.jinja +1 -1

chat_template.jinja CHANGED Viewed

@@ -97,7 +97,7 @@
             {%- endif %}
         {%- endif %}
         {%- set reasoning_content = reasoning_content|trim %}
-        {%- if loop.index0 > ns.last_query_index %}
             {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
         {%- else %}
             {{- '<|im_start|>' + message.role + '\n' + content }}

             {%- endif %}
         {%- endif %}
         {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index and reasoning_content %}
             {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
         {%- else %}
             {{- '<|im_start|>' + message.role + '\n' + content }}