Instructions to use Qwen/Qwen2.5-VL-72B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2.5-VL-72B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Qwen/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-Instruct

SGLang

How to use Qwen/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-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/Qwen2.5-VL-72B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2.5-VL-72B-Instruct
```

Could you please share the detailed parameters setting for the online demo?

#18

by harryzwh - opened Mar 10, 2025

Discussion

harryzwh

Mar 10, 2025

We have tested both the 72B pytorch and AWQ version and the precision is not as good as the online demo. We use xinference with vllm 0.7.3 and transformers 4.49.0 as the backend. We use an example image below

and the prompt below
"
输出每一笔交易的时间和金额，以json形式输出。金额要求准确，无需分隔符，以下是一个示例
##
[{"Time":YYYY-MM-DD, "Amount":0.00},{"Time":YYYY-MM-DD, "Amount":1.00}]
"
is used for both online demo and local deployment. The results are atttached below
for online demo
[
{"Time": "2019-12-08", "Amount": -113.00},
{"Time": "2019-12-08", "Amount": -183.00},
{"Time": "2019-12-18", "Amount": -7500.00},
{"Time": "2019-12-08", "Amount": -301.00},
{"Time": "2019-12-08", "Amount": -63.00},
{"Time": "2019-12-08", "Amount": -776.00},
{"Time": "2019-12-08", "Amount": -1000.00},
{"Time": "2019-12-11", "Amount": -10000.00},
{"Time": "2019-12-11", "Amount": -1486.00},
{"Time": "2019-12-11", "Amount": -596.00},
{"Time": "2019-12-12", "Amount": -1423.00},
{"Time": "2019-12-16", "Amount": -825.00},
{"Time": "2019-12-17", "Amount": -100000.00},
{"Time": "2019-12-18", "Amount": -1322.00},
{"Time": "2019-12-18", "Amount": -4400.00},
{"Time": "2019-12-21", "Amount": 1727.64},
{"Time": "2019-12-24", "Amount": -968.00}
]
and for local deployment

json
[
{"Time":"2019-12-08", "Amount":-113.00},
{"Time":"2019-12-08", "Amount":193.00},
{"Time":"2019-12-10", "Amount":-7500.00},
{"Time":"2019-12-09", "Amount":-301.00},
{"Time":"2019-12-09", "Amount":-63.00},
{"Time":"2019-12-09", "Amount":-754.00},
{"Time":"2019-12-09", "Amount":-1000.00},
{"Time":"2019-12-11", "Amount":-10000.00},
{"Time":"2019-12-11", "Amount":-1496.00},
{"Time":"2019-12-11", "Amount":-906.00},
{"Time":"2019-12-12", "Amount":-1623.00},
{"Time":"2019-12-12", "Amount":-823.00},
{"Time":"2019-12-17", "Amount":-100000.00},
{"Time":"2019-12-19", "Amount":-1322.00},
{"Time":"2019-12-19", "Amount":-4400.00},
{"Time":"2019-12-21", "Amount":1727.64},
{"Time":"2019-12-24", "Amount":-908.00}
]

It can be seen that the online demo has all the amount correct while the local deployment make mistakes e.g. line 2, 9, 10, 11, etc. It seems this is due to a different setting of some parameters such as resolution. Therefore, could you please share the setting for the online demo so that we can re-produce the results in our local deployment

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