Instructions to use Qwen/Qwen3-VL-235B-A22B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-VL-235B-A22B-Instruct 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-Instruct")
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-VL-235B-A22B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Instruct", 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-VL-235B-A22B-Instruct 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-Instruct"
# 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-Instruct",
		"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-Instruct

SGLang

How to use Qwen/Qwen3-VL-235B-A22B-Instruct 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-Instruct" \
    --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-Instruct",
		"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-Instruct" \
        --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-Instruct",
		"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-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-VL-235B-A22B-Instruct
```

shuai bai commited on Sep 23, 2025

Commit

3618751

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1 Parent(s): 9a1ef91

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@@ -18,13 +18,13 @@ The goal of Qwen3-VL is not just to “see” images or videos — but to truly
 - **Much Better Spatial Understanding**: 2D grounding is strengthened and the coordinate is transformed from absolute to relative, enabling more robust estimation of object locations, viewpoint (camera pose) changes, and occlusion structure. The model also supports 3D grounding, providing a foundation for complex spatial reasoning and embodied AI applications.
-- **Long Context & Long Video Understanding**: Natively support 256K tokens of context, expandable up to 1 million tokens. This means you can input hundreds of pages of technical documentation, entire textbooks, and even two‑hour recordings of meetings or lectures — and achieves reliable long‑context retention and precise retrieval, including second‑level timestamp localization in video.
 - **Stronger Multimodal Reasoning (Thinking Version)**: The Thinking model is optimized for STEM reasoning. On complex problems, it attends to fine‑grained cues, performs step‑by‑step decomposition, analyzes causal dependencies, and produces logically consistent, evidence‑grounded solutions. It achieves state‑of‑the‑art results on benchmarks including MathVision, MMMU, and MathVista.
 - **Upgraded Visual Perception & Recognition**: Improvements to the quality and diversity of the pretraining corpus have expanded the model’s recognition ability to a broader range of objects and entities, including celebrities, anime characters, consumer products, landmarks, and flora/fauna, — covering both everyday life and professional “recognize anything” needs.
-- **Better OCR Across More Languages & Complex Scenes**: OCR now supports 32 languages (up from 19), covering more countries and regions. It demonstrates greater robustness under challenging real-world conditions like poor lighting, blur, or tilted text. Recognition accuracy for rare characters, ancient scripts, and technical terms has also improved significantly. Its ability to understand long documents and reconstruct fine structures is further enhanced.
 This is the weight repository for Qwen3-VL-235B-A22B-Instruct.

 - **Much Better Spatial Understanding**: 2D grounding is strengthened and the coordinate is transformed from absolute to relative, enabling more robust estimation of object locations, viewpoint (camera pose) changes, and occlusion structure. The model also supports 3D grounding, providing a foundation for complex spatial reasoning and embodied AI applications.
+- **Long Context & Long Video Understanding**: Natively support 256K tokens of context, expandable up to 1 million tokens. This means you can input hundreds of pages of technical documentation, entire textbooks, and even two‑hour videos of meetings or lectures — and achieves reliable long‑context retention and precise retrieval, including second‑level timestamp localization in video.
 - **Stronger Multimodal Reasoning (Thinking Version)**: The Thinking model is optimized for STEM reasoning. On complex problems, it attends to fine‑grained cues, performs step‑by‑step decomposition, analyzes causal dependencies, and produces logically consistent, evidence‑grounded solutions. It achieves state‑of‑the‑art results on benchmarks including MathVision, MMMU, and MathVista.
 - **Upgraded Visual Perception & Recognition**: Improvements to the quality and diversity of the pretraining corpus have expanded the model’s recognition ability to a broader range of objects and entities, including celebrities, anime characters, consumer products, landmarks, and flora/fauna, — covering both everyday life and professional “recognize anything” needs.
+- **Better OCR Across More Languages & Complex Scenes**: OCR now supports 32 languages (up from 10), covering more countries and regions. It demonstrates greater robustness under challenging real-world conditions like poor lighting, blur, or tilted text. Recognition accuracy for rare characters, ancient scripts, and technical terms has also improved significantly. Its ability to understand long documents and reconstruct fine structures is further enhanced.
 This is the weight repository for Qwen3-VL-235B-A22B-Instruct.