Instructions to use yuchenwu73/GeoBox-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuchenwu73/GeoBox-R1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="yuchenwu73/GeoBox-R1")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yuchenwu73/GeoBox-R1")
model = AutoModelForMultimodalLM.from_pretrained("yuchenwu73/GeoBox-R1", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuchenwu73/GeoBox-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuchenwu73/GeoBox-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuchenwu73/GeoBox-R1",
		"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/yuchenwu73/GeoBox-R1

SGLang

How to use yuchenwu73/GeoBox-R1 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 "yuchenwu73/GeoBox-R1" \
    --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": "yuchenwu73/GeoBox-R1",
		"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 "yuchenwu73/GeoBox-R1" \
        --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": "yuchenwu73/GeoBox-R1",
		"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 yuchenwu73/GeoBox-R1 with Docker Model Runner:
```
docker model run hf.co/yuchenwu73/GeoBox-R1
```

yuchenwu73 commited on Sep 3

Commit

a916e21

verified ·

1 Parent(s): c18ebfc

docs: simplify model card header

Browse files

Combine the model name and paper title into one centered heading and remove the Limitations section.

Files changed (1) hide show

README.md +1 -12

README.md CHANGED Viewed

@@ -16,9 +16,7 @@ tags:
 <div align="center">
-# GeoBox-R1
-**Curriculum-Guided SFT and Geometric RL for Unified Box-Level Remote Sensing Visual Grounding**
 Chenxi Lan\*, Yuchen Wu\*, Minghang Zhou, Tianyu Li, Zhihao Qiu, Guoqing Wang<sup>†</sup>
@@ -122,15 +120,6 @@ Coordinates are quantized to `[0, 1000]`. Multiply x coordinates by the image wi
 The repository also provides evaluation scripts, an interactive demo, and the complete training
 pipeline: [github.com/yuchenwu73/GeoBox-R1](https://github.com/yuchenwu73/GeoBox-R1).
-## Limitations
-- The model is designed for single-object visual grounding in remote-sensing imagery; it is not
-  a general-purpose detector.
-- Predictions are generated as text and may occasionally be malformed or refer to the wrong
-  object, especially for ambiguous expressions or very small targets.
-- Reported results follow the benchmark splits and protocols described on the project page and
-  may not transfer directly to other imagery domains.
 ## License
 The model weights are released under the **CC BY-NC 4.0** license. Users must also comply with

 <div align="center">
+<h1 align="center">GeoBox-R1: Curriculum-Guided SFT and Geometric RL for Unified Box-Level Remote Sensing Visual Grounding</h1>
 Chenxi Lan\*, Yuchen Wu\*, Minghang Zhou, Tianyu Li, Zhihao Qiu, Guoqing Wang<sup>†</sup>
 The repository also provides evaluation scripts, an interactive demo, and the complete training
 pipeline: [github.com/yuchenwu73/GeoBox-R1](https://github.com/yuchenwu73/GeoBox-R1).
 ## License
 The model weights are released under the **CC BY-NC 4.0** license. Users must also comply with