Instructions to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking")
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("DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking")
model = AutoModelForMultimodalLM.from_pretrained("DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking", 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 DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"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/DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking

SGLang

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking 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 "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking" \
    --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": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"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 "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking" \
        --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": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
    max_seq_length=2048,
)

Docker Model Runner
How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking
```

Modified chat template in llama.cpp

by tis111 - opened Mar 15

Discussion

tis111

Mar 15

•

edited Mar 15

The model shows very good results, but there is one thing I am fighting with while testing. I can get it running cleanly in llama-cli or llama-server because the opening token is already written in the template. It always starts to display the reasoning trace.

Think it is the modified chat template. Are there any known solutions to this?

EDIT: I already edited the chat template - got it displayed well. But it is not possible to switch off reasoning.

Anduin1357

Mar 15

•

edited Mar 15

Put {%- set enable_thinking = false %} at the top of the jinja template to logically disable the reasoning trace. That is the standard approach for all Qwen3.5 models.

{# Final Generation Prompt #}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
    {%- if enable_thinking is defined and enable_thinking is false %}
        {{- '<think>\n\n</think>\n\n' }}
    {%- else %}
        {{- '<think>\n' }}
    {%- endif %}
{%- endif %}

You should see:

<think>

</think>
Response...

This is okay. This approach is what was officially provided by the Qwen team.

See line 150 at: https://huggingface.co/Qwen/Qwen3.5-35B-A3B/blob/main/chat_template.jinja

tis111

Mar 15

•

edited Mar 15

That worked ! Thanks!
That surprises me because

--chat-template-kwargs '{"enable_thinking":false}'

failed to switch off thinking in my environment.

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