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This tool utilizes a 22M parameter time-series model to forecast the weather in a specific backyard location. Users can input their location and receive a predicted weather forecast. The model is trained on historical weather data and is hosted on Hugging Face.
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22m params is honestly refreshing lol, everyone's out here throwing billions at stuff that doesn't need it. what architecture are you running though? patching transformer like patchtst or something more like a tcn? also genuinely curious about the training data situation, are you pulling from personal weather station networks or using something like era5 and downscaling? the hyperlocal microclimate angle is where big forecast models completely faceplant so having something that actually learns your specific backyard quirks is super practical. inference running locally or cloud-side?
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