[NEW MODEL] SupraWeather-Nano-Preview Just released!

SupraWeather Nano is live! ⛈️ We just released SupraWeather-Nano (preview), a small FT-Transformer model purpose-built to classify weather phenomena from raw tabular meteorological features. https://huggingface.co/SupraLabs/SupraWeather-Nano-Preview https://huggingface.co/SupraLabs Most weather classification setups either bolt a generic model onto tabular data or skip structure entirely. SupraWeather Nano uses a dedicated Feature Tokenizer + Transformer Encoder (FT-Transformer), each input feature gets its own learned token, a CLS token aggregates them, and a small transformer stack does the rest. No system prompt, no text input. Just send the numbers and get a class back. Inputs: temperature, humidity, pressure, pressure trend, wind speed, wind direction, altitude, month, air mass. Examples: Input (temp / humidity / pressure / wind / air mass) Predicted class 30°C / 30% / 1025 hPa / 5 km/h / tropical Clear 6°C / 99% / 1018 hPa / 1 km/h / maritime Fog -10°C / 98% / 998 hPa / 10 km/h / polar Snow 0°C / 95% / 1002 hPa / 8 km/h / polar Freezing Rain 28°C / 99% / 985 hPa / 35 km/h / equatorial Thunderstorm 15°C / 50% / 980 hPa / 70 km/h / polar Windstorm Quick start: *FULL CODE ON REPO* Live demo (Space): https://huggingface.co/spaces/SupraLabs/SupraWeather-Nano-Preview This is a preview release trained entirely on a synthetic dataset (rule-based generator, 120k samples). It is not intended for real-world forecasting, it's an architecture and pipeline experiment. 5/6 internal stress tests pass; the one failure (windstorm vs. cold front) is documented in the model card. Feedback welcome!