Instructions to use BiliSakura/RSCCM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BiliSakura/RSCCM with Transformers:

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

pipe = pipeline("image-text-to-text", model="BiliSakura/RSCCM")
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("BiliSakura/RSCCM")
model = AutoModelForImageTextToText.from_pretrained("BiliSakura/RSCCM")
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 BiliSakura/RSCCM with vLLM:

Install from pip and serve model

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

SGLang

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

Improve model card: Add pipeline tag, library name, paper, code, and project page links

by nielsr HF Staff - opened Sep 10, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+151

-120

nielsr

Sep 10, 2025

This PR enhances the model card by:

Adding pipeline_tag: image-to-text to improve discoverability on the Hugging Face Hub (https://huggingface.co/models?pipeline_tag=image-to-text).
Including library_name: transformers to enable the automated "how to use" widget, as the model uses transformers classes (Qwen2_5_VLForConditionalGeneration, AutoProcessor).
Adding additional relevant tags: remote-sensing, change-detection, and image-captioning.
Updating the paper link in the content to the official Hugging Face paper page (https://huggingface.co/papers/2509.01907).
Adding explicit links to the GitHub repository (https://github.com/Bili-Sakura/RSCC) and project page (https://bili-sakura.github.io/RSCC/).
Updating the BibTeX citation for the paper to use the arXiv URL for accuracy.

These changes will make the model more accessible and easier to understand for users.

Improve model card: Add pipeline tag, library name, paper, code, and project page linksef6a2ddc

BiliSakura changed pull request status to merged Sep 20, 2025

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