Instructions to use Qwen/Qwen2-VL-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2-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/Qwen2-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2-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?"}
        ]
    },
]
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

vLLM

How to use Qwen/Qwen2-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/Qwen2-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/Qwen2-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/Qwen2-VL-2B-Instruct

SGLang

How to use Qwen/Qwen2-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/Qwen2-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/Qwen2-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/Qwen2-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/Qwen2-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/Qwen2-VL-2B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2-VL-2B-Instruct
```

Failed to load Qwen2-VL using transformers built version(60226fd)

by Zoey1024 - opened Sep 9, 2024

Discussion

Zoey1024

Sep 9, 2024

Following the doc tutorial, install transformers with latest git commit:

pip install git+https://github.com/huggingface/transformers

and the model loading step:

from transformers import Qwen2VLForConditionalGeneration
model = Qwen2VLForConditionalGeneration.from_pretrained(
    pretrained_model_name_or_path="Qwen/Qwen2-VL-2B-Instruct",
    torch_dtype="auto",
    device_map="auto",
)

raise errors:

  File "/home/xxx/xxx/qwen2.py", line 1, in <module>
    from transformers import Qwen2VLForConditionalGeneration
  File "<frozen importlib._bootstrap>", line 1039, in _handle_fromlist
  File "/home/xxx/xxx/.venv/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1657, in __getattr__
    value = getattr(module, name)
  File "/home/xxx/xxx/.venv/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1656, in __getattr__
    module = self._get_module(self._class_to_module[name])
  File "/home/xxx/xxx/.venv/lib/python3.8/site-packages/transformers/utils/import_utils.py", line 1668, in _get_module
    raise RuntimeError(
RuntimeError: Failed to import transformers.models.qwen2_vl.modeling_qwen2_vl because of the following error (look up to see its traceback):
Failed to import transformers.generation.utils because of the following error (look up to see its traceback):
issubclass() arg 1 must be a class

Is there a issue with latest transformer version(60226fd) for Qwen2-VL integration?

wqq

Sep 10, 2024

问题解决了没，哥

Zoey1024

Sep 10, 2024

not yet

chenkq

Qwen org Sep 11, 2024

Could you provide more environment information?

You can run:

python -m torch.utils.collect_env
transformers-cli env

youci

Jan 8, 2025

try python -m pip install -U pydantic

roachrsp

Mar 25, 2025

•

edited Mar 25, 2025

You can use below method to load quantised Qwen models

from gptqmodel import GPTQModel
model = GPTQModel.load("model_name")

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