Instructions to use Qwen/Qwen3-VL-235B-A22B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-VL-235B-A22B-Thinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Qwen/Qwen3-VL-235B-A22B-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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Thinking") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3-VL-235B-A22B-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?"} ] }, ] 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-VL-235B-A22B-Thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen3-VL-235B-A22B-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": "Qwen/Qwen3-VL-235B-A22B-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/Qwen/Qwen3-VL-235B-A22B-Thinking
- SGLang
How to use Qwen/Qwen3-VL-235B-A22B-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 "Qwen/Qwen3-VL-235B-A22B-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": "Qwen/Qwen3-VL-235B-A22B-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 "Qwen/Qwen3-VL-235B-A22B-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": "Qwen/Qwen3-VL-235B-A22B-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" } } ] } ] }' - Docker Model Runner
How to use Qwen/Qwen3-VL-235B-A22B-Thinking with Docker Model Runner:
docker model run hf.co/Qwen/Qwen3-VL-235B-A22B-Thinking
Avoid Re-encoding Reference Images in Vision-LLM When Comparison Criteria Are User-Defined
#18 opened 2 months ago
by
yaroslav332
What is the limit of images for each prompt
#17 opened 5 months ago
by
rockyislearning
Add pipeline_tag
2
#16 opened 6 months ago
by
multimodalart
waste of time
2
#15 opened 7 months ago
by
kingriel
How much vram is needed to run this model? 8xRTX3090=192GB isn't enough to run the context.
1
#12 opened 8 months ago
by
kq
Output messy code with demo code.
#11 opened 8 months ago
by
kk3dmax
No output_router_logits / load_balancing_loss_func for Qwen3VLMoE?
#10 opened 8 months ago
by
plcedoz38
🚀 Best Practices for Evaluating the Qwen3-VL Model
❤️ 1
#9 opened 8 months ago
by
Yunxz
Adding Offline and Online inference via vLLM Code
#8 opened 8 months ago
by
hrithiksagar-bgen
FP8/4bit version please
➕ 4
5
#7 opened 8 months ago
by
zhanghx0905
32b version?
➕ 9
1
#5 opened 8 months ago
by
sanak
Adding `transformers` library tag.
#3 opened 8 months ago
by
ariG23498
Citation section lacks Qwen3 VL specific citation
👍 1
#1 opened 8 months ago
by
jaxchang