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# MeteoLibre Rectified Flow Model
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This is a repo with differents models used for doing weather forecasting :
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- epoch_126_mtg_meteofrance_.safetensors (model with sat + ground station) : config is model_v0_mtg_meteofrance
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## Model Description
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- **Model type**: Rectified Flow Diffusion Model
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- **Architecture**: 3D U-Net with FiLM conditioning
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- **Input**: Meteorological data patches (12 channels, 3D spatio-temporal)
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- **Output**: Generated weather forecast data
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- **Training data**: MeteoLibre meteorological dataset
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- **Language(s)**: Python
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- **License**: MIT
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## Intended Use
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This model is designed for:
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- Weather pattern generation and forecasting
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- Meteorological data augmentation
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- Research in atmospheric science and weather prediction
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- Educational purposes in machine learning for climate modeling
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## Training
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The model was trained using:
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- **Framework**: PyTorch with Hugging Face Accelerate
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- **Optimizer**: Adam (lr=5e-4)
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- **Batch size**: 64
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- **Epochs**: 200
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- **Precision**: Mixed precision (bf16)
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- **Distributed training**: Multi-GPU support
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## Ethical Considerations
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- Weather forecasting models should be used responsibly
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- Consider environmental impact of computational requirements
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- Validate predictions against ground truth data
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- Not intended for critical decision-making without human oversight
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{meteolibre-rectified-flow,
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title={MeteoLibre Rectified Flow Weather Forecasting Model},
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author={MeteoLibre Development Team},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/meteolibre-dev/meteolibre-rectified-flow}
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}
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```
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## Contact
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For questions or issues, please open an issue on the [MeteoLibre GitHub repository](https://github.com/meteolibre-dev/meteolibre_model).
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# MeteoLibre Rectified Flow Model
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## Model Description
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- **Model type**: Rectified Flow Diffusion Model
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- **Architecture**: 3D U-Net with FiLM conditioning
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- **Input**: Meteorological data patches (12 channels + 1 lightning channels, 3D spatio-temporal)
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- **Output**: Generated weather forecast data
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- **Training data**: MeteoLibre meteorological dataset
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- **Language(s)**: Python
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- **License**: MIT
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## Training
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The model was trained using:
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- **Framework**: PyTorch with Hugging Face Accelerate
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- **Optimizer**: Adam (lr=5e-4) OR SOAP
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- **Batch size**: 64
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- **Epochs**: 200
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- **Precision**: Mixed precision (bf16)
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- **Distributed training**: Multi-GPU support
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