---
language: en
tags:
- weather-forecasting
- diffusion-models
- rectified-flow
- meteorology
- pytorch
- deep-learning
license: mit
datasets:
- meteolibre
---

# MeteoLibre Rectified Flow Model

This is a repo with differents models used for doing weather forecasting :
- epoch_126_mtg_meteofrance_.safetensors (model with sat + ground station) : config is model_v0_mtg_meteofrance

## Model Description

- **Model type**: Rectified Flow Diffusion Model
- **Architecture**: 3D U-Net with FiLM conditioning
- **Input**: Meteorological data patches (12 channels, 3D spatio-temporal)
- **Output**: Generated weather forecast data
- **Training data**: MeteoLibre meteorological dataset
- **Language(s)**: Python
- **License**: MIT

## Intended Use

This model is designed for:
- Weather pattern generation and forecasting
- Meteorological data augmentation
- Research in atmospheric science and weather prediction
- Educational purposes in machine learning for climate modeling

## Training

The model was trained using:
- **Framework**: PyTorch with Hugging Face Accelerate
- **Optimizer**: Adam (lr=5e-4)
- **Batch size**: 64
- **Epochs**: 200
- **Precision**: Mixed precision (bf16)
- **Distributed training**: Multi-GPU support

## Ethical Considerations

- Weather forecasting models should be used responsibly
- Consider environmental impact of computational requirements
- Validate predictions against ground truth data
- Not intended for critical decision-making without human oversight

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{meteolibre-rectified-flow,
  title={MeteoLibre Rectified Flow Weather Forecasting Model},
  author={MeteoLibre Development Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/meteolibre-dev/meteolibre-rectified-flow}
}
```

## Contact

For questions or issues, please open an issue on the [MeteoLibre GitHub repository](https://github.com/meteolibre-dev/meteolibre_model).