Instructions to use nvidia/LocateAnything-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/LocateAnything-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/LocateAnything-3B", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("nvidia/LocateAnything-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/LocateAnything-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/LocateAnything-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/LocateAnything-3B",
		"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/nvidia/LocateAnything-3B

SGLang

How to use nvidia/LocateAnything-3B 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 "nvidia/LocateAnything-3B" \
    --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": "nvidia/LocateAnything-3B",
		"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 "nvidia/LocateAnything-3B" \
        --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": "nvidia/LocateAnything-3B",
		"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 nvidia/LocateAnything-3B with Docker Model Runner:
```
docker model run hf.co/nvidia/LocateAnything-3B
```

Zhiding commited on Jun 8

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@@ -42,7 +42,7 @@ Its core innovation, Parallel Box Decoding (PBD), predicts complete bounding box
 The model is trained on a large-scale multi-domain dataset (12M images, 138M+ queries, 785M bounding boxes) spanning natural scenes, robotics, driving, GUI interaction, and document understanding. It serves as a foundation for generalist multimodal perception and has been integrated into NVIDIA’s frontier production-grade vision-language models, such as Nemotron 3 Nano Omni, supporting grounding, GUI understanding, and multimodal agentic capabilities.
-LocateAnything is developed as part of the [Eagle VLM](https://github.com/NVlabs/EAGLE) model family. This model is for research and development only.
 ### Demo Videos

 The model is trained on a large-scale multi-domain dataset (12M images, 138M+ queries, 785M bounding boxes) spanning natural scenes, robotics, driving, GUI interaction, and document understanding. It serves as a foundation for generalist multimodal perception and has been integrated into NVIDIA’s frontier production-grade vision-language models, such as Nemotron 3 Nano Omni, supporting grounding, GUI understanding, and multimodal agentic capabilities.
+LocateAnything is developed as part of the [Eagle VLM](https://github.com/NVlabs/EAGLE) model family. This released model is for research and development only. In addition, LocateAnything contributed to [Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) and [Cosmos](https://www.nvidia.com/en-us/ai/cosmos/) as part of the Computer Use and Visual Grounding features. We give special thanks the Nemotron and Cosmos Teams for time and efforts in product integration.
 ### Demo Videos