Instructions to use MLL-Lab/viewagent-all-qwen25vl7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MLL-Lab/viewagent-all-qwen25vl7b with Transformers:

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

pipe = pipeline("image-text-to-text", model="MLL-Lab/viewagent-all-qwen25vl7b")
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("MLL-Lab/viewagent-all-qwen25vl7b")
model = AutoModelForMultimodalLM.from_pretrained("MLL-Lab/viewagent-all-qwen25vl7b")
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 Settings

vLLM

How to use MLL-Lab/viewagent-all-qwen25vl7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MLL-Lab/viewagent-all-qwen25vl7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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/MLL-Lab/viewagent-all-qwen25vl7b

SGLang

How to use MLL-Lab/viewagent-all-qwen25vl7b 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 "MLL-Lab/viewagent-all-qwen25vl7b" \
    --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": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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 "MLL-Lab/viewagent-all-qwen25vl7b" \
        --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": "MLL-Lab/viewagent-all-qwen25vl7b",
		"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 MLL-Lab/viewagent-all-qwen25vl7b with Docker Model Runner:
```
docker model run hf.co/MLL-Lab/viewagent-all-qwen25vl7b
```

Add model card and paper metadata

by nielsr HF Staff - opened 22 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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nielsr

22 days ago

Hi! I'm Niels from the community science team at Hugging Face. I've noticed this repository was missing a model card, so I've opened this PR to add one. It includes:

Metadata for the transformers library, image-text-to-text pipeline, and mit license.
Links to the paper, project page, and Github repository.
A summary of the ViewSuite environment and the iterative training framework used to improve this model.

This will help users discover and understand the research behind your model checkpoints.

Add model card and paper metadata66c0591a

ManlingLi changed pull request status to merged 8 days ago

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