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

File size: 2,497 Bytes

6028ebb

---
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