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
```

eun2ce commited on Nov 13, 2025

Commit

f4ee71d

verified ·

1 Parent(s): 1c8864c

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

Browse files

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

Files changed (1) hide show

groundcua.py +4 -2

groundcua.py CHANGED Viewed

@@ -22,16 +22,18 @@ For each function call, return a json object with function name and arguments wi
 # Default generation parameters
 DEFAULT_TEMPERATURE = 0.0
 DEFAULT_MAX_NEW_TOKENS = 64
 MIN_PIXELS = 78_400
 MAX_PIXELS = 6_000_000
-def prepare_image(image, min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS):
     """
     Resize image using smart_resize for optimal model performance.
     Args:
         image: PIL Image object
         min_pixels: Minimum number of pixels (default: 78,400)
         max_pixels: Maximum number of pixels (default: 6,000,000)
@@ -42,7 +44,7 @@ def prepare_image(image, min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS):
     width, height = image.size
     resized_height, resized_width = smart_resize(
-        height, width, min_pixels=min_pixels, max_pixels=max_pixels
     )
     resized_image = image.resize((resized_width, resized_height))
     return resized_image, (resized_width, resized_height)

 # Default generation parameters
 DEFAULT_TEMPERATURE = 0.0
 DEFAULT_MAX_NEW_TOKENS = 64
+SMART_RESIZE_FACTOR = 28
 MIN_PIXELS = 78_400
 MAX_PIXELS = 6_000_000
+def prepare_image(image, factor=SMART_RESIZE_FACTOR, min_pixels=MIN_PIXELS, max_pixels=MAX_PIXELS):
     """
     Resize image using smart_resize for optimal model performance.
     Args:
         image: PIL Image object
+        factor: Resize factor for smart_resize (default: 28)
         min_pixels: Minimum number of pixels (default: 78,400)
         max_pixels: Maximum number of pixels (default: 6,000,000)
     width, height = image.size
     resized_height, resized_width = smart_resize(
+        height, width, factor=factor, min_pixels=min_pixels, max_pixels=max_pixels
     )
     resized_image = image.resize((resized_width, resized_height))
     return resized_image, (resized_width, resized_height)