Instructions to use RicardoString/Think2Seg-RS-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RicardoString/Think2Seg-RS-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="RicardoString/Think2Seg-RS-7B")
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("RicardoString/Think2Seg-RS-7B")
model = AutoModelForMultimodalLM.from_pretrained("RicardoString/Think2Seg-RS-7B")
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 RicardoString/Think2Seg-RS-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RicardoString/Think2Seg-RS-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RicardoString/Think2Seg-RS-7B",
		"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/RicardoString/Think2Seg-RS-7B

SGLang

How to use RicardoString/Think2Seg-RS-7B 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 "RicardoString/Think2Seg-RS-7B" \
    --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": "RicardoString/Think2Seg-RS-7B",
		"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 "RicardoString/Think2Seg-RS-7B" \
        --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": "RicardoString/Think2Seg-RS-7B",
		"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 RicardoString/Think2Seg-RS-7B with Docker Model Runner:
```
docker model run hf.co/RicardoString/Think2Seg-RS-7B
```

Add pipeline tag and link to paper

by nielsr HF Staff - opened Dec 23, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+28

-6

Files changed (1) hide show

README.md +28 -6

README.md CHANGED Viewed

@@ -1,16 +1,38 @@
 ---
-datasets:
-- earth-insights/EarthReason
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
 library_name: transformers
 ---
-## Bridging Semantics and Geometry: A Decoupled LVLM–SAM Framework for Reasoning Segmentation in Remote Sensing
-This is the 7B model of [Think2Seg-RS](https://github.com/Ricardo-XZ/Think2Seg-RS), a decoupled framework for reasoning segmentation in remote sensing (RS) imagery.
-Our core idea is to decouple high-level semantic reasoning from low-level geometric execution. Specifically, we train an LVLM prompter (e.g., Qwen-2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. Through a result-oriented reinforcement learning objective, the LVLM learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.
-For more details, code, and the complete framework, please visit our [GitHub repository](https://github.com/Ricardo-XZ/Think2Seg-RS).

 ---
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
+datasets:
+- earth-insights/EarthReason
 library_name: transformers
+pipeline_tag: image-segmentation
+license: apache-2.0
 ---
+# Bridging Semantics and Geometry: A Decoupled LVLM–SAM Framework for Reasoning Segmentation in Remote Sensing
+This repository contains the 7B model of **Think2Seg-RS**, a decoupled framework for reasoning segmentation in remote sensing (RS) imagery.
+The model was introduced in the paper [Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing](https://huggingface.co/papers/2512.19302).
+## Overview
+Think2Seg-RS decouples high-level semantic reasoning from low-level geometric execution. It trains an LVLM prompter (based on Qwen-2.5-VL) to control a frozen Segment Anything Model (SAM2) via structured geometric prompts. Through a mask-only reinforcement learning objective, the LVLM learns to translate abstract semantic reasoning into spatially grounded actions, achieving state-of-the-art performance on the EarthReason dataset.
+## Resources
+- **Paper:** [arXiv:2512.19302](https://huggingface.co/papers/2512.19302)
+- **Code:** [GitHub - Think2Seg-RS](https://github.com/Ricardo-XZ/Think2Seg-RS)
+- **Dataset:** [EarthReason](https://huggingface.co/datasets/earth-insights/EarthReason)
+## Citation
+If you find this work helpful for your research, please cite:
+```bibtex
+@article{think2seg_rs_2025,
+  title={Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Remote Sensing},
+  author={Anonymous},
+  journal={arXiv preprint arXiv:2512.19302},
+  year={2025}
+}
+```