| # Earthmind-R1 |
|
|
| EarthMind-4B fine-tuned with GRPO (Group Relative Policy Optimization) for geospatial visual question answering. |
|
|
| ## Model Details |
|
|
| - **Base Model**: EarthMind-4B (InternVL-based architecture) |
| - **Training Method**: GRPO with LoRA adapters |
| - **Training Data**: Geospatial instruction dataset |
| - **Output Format**: Chain-of-thought reasoning with `<think>` and `<answer>` tags |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from transformers import AutoModel, AutoTokenizer |
| from PIL import Image |
| |
| model = AutoModel.from_pretrained( |
| "aadex/Earthmind-R1", |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16, |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("aadex/Earthmind-R1", trust_remote_code=True) |
| |
| # Prepare for generation |
| model.preparing_for_generation(tokenizer=tokenizer, max_new_tokens=512, torch_dtype=torch.bfloat16) |
| |
| # Load your image |
| image = Image.open("your_image.jpg") |
| |
| # Create prompt |
| question = "Describe what you see in this satellite image." |
| prompt = f"""User: <image> |
| {question} First output the thinking process in <think> </think> tags and then output the final answer in <answer> </answer> tags. |
| Assistant:""" |
| |
| # Generate (use model's chat method or manual generation) |
| response = model.chat(tokenizer, pixel_values, question, generation_config) |
| print(response) |
| ``` |
|
|
| ## Training |
|
|
| Trained using GRPO with: |
| - LoRA rank: 16 |
| - LoRA alpha: 32 |
| - Learning rate: 5e-6 |
| - Epochs: 3 |
| - Reward functions: accuracy, format |
|
|
| ## License |
|
|
| Please refer to the base EarthMind-4B model license. |
|
|
