Instructions to use Qwen/Qwen3-VL-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen3-VL-2B-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-2B-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-2B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3-VL-2B-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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen3-VL-2B-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-2B-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-2B-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-2B-Instruct

SGLang

How to use Qwen/Qwen3-VL-2B-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-2B-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-2B-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-2B-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-2B-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-2B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen3-VL-2B-Instruct
```

torch.OutOfMemoryError: CUDA out of memory

by shadowT - opened Dec 21, 2025

Discussion

shadowT

Dec 21, 2025

When I send him
`messages = [
{
"role": "user",
"content": [
{
"type": "image",

"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},

"type": "text", "text": "Describe this image."},

}]This error appearstorch.OutOfMemoryError: CUDA out of memory. Tried to allocate 8.11 GiB. GPU 0 has a total capacity of 12.00 GiB of which 0 bytes is free. Of the allocated memory 16.36 GiB is allocated by PyTorch, and 111.56 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)`

But it does not appear in questions that do not require explanation.

def init(self, model_id: str = "Qwen/Qwen3-VL-2B-Instruct",
dtype=torch.bfloat16,
attn_implementation: str = "flash_attention_2",
device_map: str = "auto",
min_pixels=256 * 28 * 28,
max_pixels=768 * 28 * 28,
) -> None:

    if self._initialized:
        return

    self._initialized = True

    self.model_id = model_id

    # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
    self.model = Qwen3VLForConditionalGeneration.from_pretrained(
        model_id,
        dtype=dtype,
        attn_implementation=attn_implementation,
        device_map=device_map,
    )

    self.processor = AutoProcessor.from_pretrained(
        model_id, min_pixels=min_pixels, max_pixels=max_pixels
    )

generation_args = { # length "max_new_tokens": 128, # out_seq_length # sampling "do_sample": True, # greedy = false "top_p": 0.8, "top_k": 20, "temperature": 0.7, # penalties "repetition_penalty": 1.1, # streaming "streamer": streamer, **inputs, }

Any advice?

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