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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ServiceNow/GroundNext-7B-V0")
model = AutoModelForMultimodalLM.from_pretrained("ServiceNow/GroundNext-7B-V0", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

feiziaarash commited on Nov 10, 2025

Commit

1b032ad

verified ·

1 Parent(s): 4aef4d7

Update README.md

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Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -85,7 +85,7 @@ full_prompt = f'{instruction}'
 messages = [
     {
     "role": "system",
-    "content": SYSTEM_PROMPT.format(img_width=img_width, img_height=img_height)
     },
     {
         "role": "user",
@@ -102,20 +102,20 @@ messages = [
 input_text = tokenizer.apply_chat_template(messages,
                                            add_generation_prompt=True,
                                            tokenize=False)
-inputs = self.processor(
                 text=[input_text],
                 images=[image],
                 videos=None,
                 padding=True,
                 return_tensors="pt",
-            ).to(self.model.device)
-output = model.generate(**inputs, max_new_tokens=64)
 generated_ids_trimmed = [
     out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
 ]
-response = self.processor.batch_decode(
     generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )[0]

 messages = [
     {
     "role": "system",
+    "content": GroundNext_GROUNDER_SYS_PROMPT.format(img_width=img_width, img_height=img_height)
     },
     {
         "role": "user",
 input_text = tokenizer.apply_chat_template(messages,
                                            add_generation_prompt=True,
                                            tokenize=False)
+inputs = processor(
                 text=[input_text],
                 images=[image],
                 videos=None,
                 padding=True,
                 return_tensors="pt",
+            ).to(model.device)
+generated_ids = model.generate(**inputs, max_new_tokens=64)
 generated_ids_trimmed = [
     out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
 ]
+response = processor.batch_decode(
     generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
 )[0]