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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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-
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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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-
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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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-
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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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-
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- ## Citation
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-
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- If you use this model in your research, please cite:
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-
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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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-
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- ## Contact
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-
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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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