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

Libraries

How to use aadex/Earthmind-R1-test with Transformers:

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

pipe = pipeline("image-text-to-text", model="aadex/Earthmind-R1-test", 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("aadex/Earthmind-R1-test", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aadex/Earthmind-R1-test with vLLM:

Install from pip and serve model

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

SGLang

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

Earthmind-R1-test / README.md

aadex

Upload EarthMind-4B GRPO fine-tuned model

6028ebb verified 6 months ago

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- vision-language
	- vlm
	- grpo
	- earthmind
	- geospatial
	- remote-sensing
	library_name: transformers
	pipeline_tag: image-text-to-text
	---

	# EarthMind-R1

	EarthMind-R1 is a vision-language model fine-tuned using GRPO (Group Relative Policy Optimization) for geospatial and remote sensing image understanding tasks.

	## Model Description

	- Base Model: EarthMind-4B
	- Training Method: GRPO (Group Relative Policy Optimization)
	- Training Data: Geospatial instruction dataset
	- Fine-tuning: LoRA adapters merged into base weights

	## Usage

	### Quick Start

	```python
	import torch
	from PIL import Image
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load model and tokenizer
	model_id = "aadex/Earthmind-R1"

	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	trust_remote_code=True,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)

	# Load an image
	image = Image.open("your_image.jpg").convert("RGB")

	# Ask a question
	question = "Describe what you see in this satellite image."

	# Use model's chat interface
	response = model.chat(
	tokenizer=tokenizer,
	question=question,
	images=[image],
	generation_config={
	"max_new_tokens": 512,
	"temperature": 0.7,
	"do_sample": True,
	},
	)

	print(response)
	```

	### Expected Output Format

	The model is trained to provide structured responses:

	```
	<think>
	[Reasoning about the image content]
	</think>
	<answer>
	[Final answer to the question]
	</answer>
	```

	## Requirements

	```
	torch>=2.0
	transformers>=4.40
	accelerate
	pillow
	```

	## Hardware Requirements

	- Minimum: 16GB VRAM (with bfloat16)
	- Recommended: 24GB VRAM for comfortable inference

	## Training Details

	- Framework: VLM-R1 + TRL
	- Optimizer: AdamW
	- Learning Rate: 1e-6
	- LoRA Configuration:
	- r: 32
	- alpha: 64
	- dropout: 0.05
	- GRPO Settings:
	- num_generations: 4
	- num_iterations: 2
	- beta: 0.01

	## Limitations

	- Optimized for geospatial/remote sensing imagery
	- May not perform as well on general domain images
	- Response quality depends on image resolution and clarity

	## Citation

	If you use this model, please cite:

	```bibtex
	@misc{earthmind-r1,
	title={EarthMind-R1: GRPO Fine-tuned Vision-Language Model for Geospatial Understanding},
	author={Your Name},
	year={2024},
	publisher={HuggingFace}
	}
	```

	## License

	Apache 2.0