Instructions to use RL-MIND/UHR-BAT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RL-MIND/UHR-BAT2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="RL-MIND/UHR-BAT2")
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("RL-MIND/UHR-BAT2")
model = AutoModelForMultimodalLM.from_pretrained("RL-MIND/UHR-BAT2", 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 RL-MIND/UHR-BAT2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RL-MIND/UHR-BAT2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RL-MIND/UHR-BAT2",
		"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/RL-MIND/UHR-BAT2

SGLang

How to use RL-MIND/UHR-BAT2 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 "RL-MIND/UHR-BAT2" \
    --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": "RL-MIND/UHR-BAT2",
		"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 "RL-MIND/UHR-BAT2" \
        --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": "RL-MIND/UHR-BAT2",
		"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 RL-MIND/UHR-BAT2 with Docker Model Runner:
```
docker model run hf.co/RL-MIND/UHR-BAT2
```

Demo for this model on Spaces

by multimodalart - opened 5 days ago

Discussion

multimodalart

5 days ago

Hi @RL-MIND 🤗

I'm Apolinario, from the open-source team at Hugging Face. Congrats and thanks for open-sourcing RL-MIND/UHR-BAT2 on the Hub! We were excited about this work and built with an agent an interactive demo app of it on Hugging Face Spaces, running on a free ZeroGPU infrastructure.

Here's a link to the demo: https://huggingface.co/spaces/hugging-apps/uhr-bat2-remote-sensing-vqa

We would love to transfer this demo to you or your organization. Would you like this demo to live under your own account or organization? If so just let me know here which username to transfer to, and we'll transfer the Space over to you, we hope it can give your work more visibility, discoverability and allows folks to try it out.

(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn or apolinario @ huggingface.co)

Cheers,

Poli

WenbinLi

RL-MIND Group org 5 days ago

Hi Poli,

Thank you so much for your kind words and for building this interactive demo for UHR-BAT2! We really appreciate your support and efforts in making our work more accessible to the community.

We'd be happy to have the Space transferred to our Hugging Face organization: RL-MIND (https://huggingface.co/RL-MIND).

Thanks again for your contribution and support! Please let us know if there's anything we need to do on our end.

Best regards,
RL-MIND Team

multimodalart

3 days ago

Hey @wenbinli !

Thank you for open sourcing UHR-BAT2!

Transferred to: https://huggingface.co/spaces/RL-MIND/uhr-bat2-remote-sensing-vqa with a ZeroGPU grant
Free to post about, put the demo on the project page, reference it on repos, etc. as you wish

Also feel more than free to take ownership and make modifications as you see fit. For future releases from you, would be great if they already came with a demos! You can use this one as a blueprint to build by yourself or with the help of an agent, you can load the huggingface-spaces skill on Claude Code, Codex, Hermes, Pi, etc.

Cheers,
Poli

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