Instructions to use ServiceNow/GroundNext-7B-V0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ServiceNow/GroundNext-7B-V0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ServiceNow/GroundNext-7B-V0")
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("ServiceNow/GroundNext-7B-V0")
model = AutoModelForImageTextToText.from_pretrained("ServiceNow/GroundNext-7B-V0")
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 ServiceNow/GroundNext-7B-V0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ServiceNow/GroundNext-7B-V0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ServiceNow/GroundNext-7B-V0",
		"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/ServiceNow/GroundNext-7B-V0

SGLang

How to use ServiceNow/GroundNext-7B-V0 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 "ServiceNow/GroundNext-7B-V0" \
    --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": "ServiceNow/GroundNext-7B-V0",
		"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 "ServiceNow/GroundNext-7B-V0" \
        --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": "ServiceNow/GroundNext-7B-V0",
		"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 ServiceNow/GroundNext-7B-V0 with Docker Model Runner:
```
docker model run hf.co/ServiceNow/GroundNext-7B-V0
```

fix: add factor=28 to smart_resize in prepare_image for Qwen VL models

by aug6th - opened Nov 13, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-2

aug6th

Nov 13, 2025

•

edited Nov 13, 2025

Ensure proper image resizing for Qwen VL models by passing factor=28 to smart_resize.

https://github.com/QwenLM/Qwen3-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py

fix: add factor=28 to smart_resize in prepare_image for Qwen VL modelsf4ee71d2

singhn9

Nov 14, 2025

have we benchmarked this against IBM docling?

aug6th

Nov 14, 2025

have we benchmarked this against IBM docling?

Good question! I haven't benchmarked this against IBM docling yet.

This PR specifically fixes the image preprocessing for Qwen VL models to match their official implementation (factor=28 in smart_resize). It's more of a bug fix than a new feature.

A full benchmark comparison would require significant resources and standardized test datasets. If the community is interested, maybe we could collaborate on this? Otherwise, the Qwen team's own benchmarks might already include comparisons with other document understanding models.

singhn9

Nov 14, 2025

Yes, we can use an ensemble approach where the higher quality model is picked up at runtime instead of one vs other. Of course this would increase latency and processing need. But we will have a second opinion on the output of the primary model.

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