Instructions to use Qwen/Qwen3.5-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3.5-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Qwen/Qwen3.5-27B") 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/Qwen3.5-27B") model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3.5-27B") 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/Qwen3.5-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen3.5-27B" # 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/Qwen3.5-27B", "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/Qwen3.5-27B
- SGLang
How to use Qwen/Qwen3.5-27B 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/Qwen3.5-27B" \ --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/Qwen3.5-27B", "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/Qwen3.5-27B" \ --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/Qwen3.5-27B", "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/Qwen3.5-27B with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3.5-27B
vllm serving issue
Hello, first of all thank you for such great models. Im facing issue while serving on vllm and constantly getting error in disabling thinking:
(APIServer pid=1) WARNING 02-25 15:46:28 [protocol.py:51] The following fields were present in the request but ignored: {'enable_thinking', 'extra_body'}
I'm using this docker image: docker run -d --gpus all
--name vllm-qwen35-27b
--ipc=host
--shm-size 16g
-v ~/hf_cache:/root/.cache/huggingface
-p 8001:8000
vllm/vllm-openai:qwen3_5
Qwen/Qwen3.5-27B
--host 0.0.0.0
--port 8000
--tensor-parallel-size 1
--max-model-len 20000
--gpu-memory-utilization 0.85
--enable-prefix-caching
--disable-log-stats
--reasoning-parser qwen3
--enable-auto-tool-choice
--tool-call-parser qwen3_coder
How can I disable reasoning?
If you are using the latest vllm for inference, according to vllm document: https://docs.vllm.ai/en/latest/features/reasoning_outputs/#disabling-thinking-mode-by-default
You can disable reasoning on the server-side by
vllm serve Qwen/Qwen3-8B \
--reasoning-parser qwen3 \
--default-chat-template-kwargs '{"enable_thinking": false}'
If you are using the latest vllm for inference, according to vllm document: https://docs.vllm.ai/en/latest/features/reasoning_outputs/#disabling-thinking-mode-by-default
You can disable reasoning on the server-side by
vllm serve Qwen/Qwen3-8B \ --reasoning-parser qwen3 \ --default-chat-template-kwargs '{"enable_thinking": false}'
thanks, but I found out that vllm inference is not optimal and that sglang manages this much better
you just disable reasoning by sending with extra body
response = client.chat.completions.create(
model="Qwen/Qwen3.5-35B-A3B",
messages=messages,
max_tokens=81920,
temperature=1.0,
top_p=0.95,
presence_penalty=1.5,
extra_body={
"top_k": 20,
"mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
},
)
which works for all and in your parameter name is wrong --name vllm-qwen35-27b, it should be 3.5-27b