Update README.md

README.md

@@ -1,3 +1,1804 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- forecast
+- weather
+- lstm
+- classification
+- regression
+- weather-forecast
+- multitask
+- harley-ml
+- small
+---
+
+# Hweh-6M
+
+## Summary
+
+Task: Weather Forecasting  
+Inputs: 72 hours time-series  
+Outputs: 12h multivariate forecast  
+Params: 446k  
+Framework: PyTorch
+Author: Paul Courneya (Harley-ml)
+
+## Description
+
+**Hweh-446k** is a **446-thousand-parameter LSTM model** distilisation of [Hweh-6M] (a 92% reduction in params!!), trained to predict the next **12 hours of weather**, including temperature, humidity, pressure, precipitation, and more, using the previous **72 hours of weather context**.  
+We recommend using this model as a backup to a weather API or for fast offline forecasting when internet access is unavailable.  
+
+We would also like to give a shoutout to [**Open-Meteo**](https://open-meteo.com/) for providing a **free-to-use weather forecasting API**.
+
+### Why “Hweh”?
+
+In Proto-Indo-European, the root ***h₂weh₁-** means “to blow.” We chose it as the name for a weather forecasting model because of its connection to wind and air.
+
+## Architecture
+
+The model uses a multitask LSTM setup:
+
+| Parameter               | Value                                          |
+| ----------------------- | ---------------------------------------------- |
+| `input_dim`             | `22`                                           |
+| `seq_len`               | `72`                                           |
+| `num_predict`           | `12`                                           |
+| `hidden_dim`            | `128`                                          |
+| `num_layers`            | `3`                                            |
+| `dropout`               | `0.1`                                          |
+| `encoder_type`          | `lstm`                                         |
+| `num_locations`         | `82`                                           |
+| `location_emb_dim`      | `32`                                           |
+| `num_weather_classes`   | `7`                                            |
+
+## Training
+
+We trained Hweh-446k on 4.06 million rows of weather data from 82 locations with the supervision of Hweh-6M for one epoch, using a batch size of 16 and gradient accumulation of 5. Training ran for 4.3 hours on an RTX 2060 6GB GPU.
+
+### Input Features
+
+1. `temperature_2m_norm`
+2. `relative_humidity_2m_norm`
+3. `apparent_temperature_norm`
+4. `precipitation_log_norm`
+5. `sea_level_pressure_norm`
+6. `surface_pressure_norm`
+7. `cloud_cover_total_norm`
+8. `visibility_norm`
+9. `wind_speed_10m_norm`
+10. `wind_direction_10m_sin`
+11. `wind_direction_10m_cos`
+12. `hour_sin`
+13. `hour_cos`
+14. `day_of_year_sin`
+15. `day_of_year_cos`
+16. `weather_code_onehot_clear`
+17. `weather_code_onehot_cloudy`
+18. `weather_code_onehot_fog`
+19. `weather_code_onehot_drizzle`
+20. `weather_code_onehot_rain`
+21. `weather_code_onehot_snow`
+22. `weather_code_onehot_thunderstorm`
+
+### Output Features
+
+1. `y_temp_c`: continuous regression
+2. `y_humidity`: continuous regression
+3. `y_apparent_temperature`: continuous regression
+4. `y_precipitation_mm`: continuous regression
+5. `y_sea_level_pressure_hpa`: continuous regression
+6. `y_surface_pressure_hpa`: continuous regression
+7. `y_cloud_cover_total`: continuous regression
+8. `y_wind_speed_10m`: continuous regression
+9. `y_wind_direction_sin`: continuous regression
+10. `y_wind_direction_cos`: continuous regression
+11. `y_rain_prob`: binary classification
+12. `y_weather_class`: multiclass classification
+
+### Training Results
+
+#### Training & Evaluation Metrics
+
+|  Step | Train Loss | Eval Loss | Weather Acc | Rain Acc | Rain Recall | Weather Recall |
+| ----: | ---------: | --------: | ----------: | -------: | ----------: | -------------: |
+|    1k |     8.4609 |    8.8471 |      0.6317 |   0.7451 |      0.7640 |         0.2574 |
+|    5k |     5.1420 |    5.0602 |      0.6247 |   0.7531 |      0.8025 |         0.5648 |
+|   10k |     4.1733 |    3.9198 |      0.6117 |   0.7876 |      0.8016 |         0.6297 |
+|   15k |     3.8354 |    3.6310 |      0.6140 |   0.7920 |      0.8009 |         0.6187 |
+|   20k |     3.6206 |    3.4365 |      0.6083 |   0.7881 |      0.8140 |         0.6179 |
+|   25k |     3.5378 |    3.3251 |      0.6083 |   0.7859 |      0.8173 |         0.6245 |
+|   30k |     3.4534 |    3.2846 |      0.6041 |   0.7812 |      0.8272 |         0.6398 |
+|   35k |     3.4272 |    3.2324 |      0.6061 |   0.7860 |      0.8194 |         0.6289 |
+|   40k |     3.4143 |    3.2230 |      0.6080 |   0.7862 |      0.8200 |         0.6339 |
+| 42.6k |          — |    3.2180 |      0.6081 |   0.7857 |      0.8212 |         0.6340 |
+
+Note: Loss looks higher than Hweh-6M's because of KL + train/val loss.
+#### Regression Error Metrics (MAE)
+
+|  Step | Apparent |   Cloud | Humidity | Precip (mm) | Sea Level P | Surface P |   Temp |  Wind |
+| ----: | -------: | ------: | -------: | ----------: | ----------: | --------: | -----: | ----: |
+|    1k |   212.80 | 2179.70 |  1476.42 |       0.140 |     7571.45 |  83590.98 | 172.79 | 60.49 |
+|    5k |     2.28 |   25.58 |     9.04 |       0.107 |        3.50 |     14.55 |   1.90 |  3.78 |
+|   10k |     2.06 |   25.31 |     8.08 |       0.100 |        3.31 |      9.63 |   1.72 |  3.37 |
+|   15k |     1.91 |   25.00 |     7.88 |       0.101 |        3.18 |      7.93 |   1.61 |  3.25 |
+|   20k |     1.88 |   25.12 |     7.60 |       0.101 |        3.13 |      7.41 |   1.56 |  3.18 |
+|   25k |     1.84 |   25.01 |     7.53 |       0.102 |        3.09 |      6.61 |   1.53 |  3.13 |
+|   30k |     1.81 |   25.03 |     7.45 |       0.102 |        3.12 |      6.60 |   1.51 |  3.12 |
+|   35k |     1.81 |   24.94 |     7.42 |       0.101 |        3.07 |      6.39 |   1.52 |  3.12 |
+|   40k |     1.79 |   24.94 |     7.39 |       0.101 |        3.06 |      6.37 |   1.50 |  3.11 |
+| 42.6k |     1.79 |   24.92 |     7.39 |       0.101 |        3.06 |      6.38 |   1.50 |  3.11 |
+
+This model did better than the teacher on MAE and accuracy, but the real-world accuracy is 5-10% worse. 
+
+## Generation Examples
+
+| ID | Class        |
+| -- | ------------ |
+| 0  | clear        |
+| 1  | cloudy       |
+| 2  | fog          |
+| 3  | drizzle      |
+| 4  | rain         |
+| 5  | snow         |
+| 6  | thunderstorm |
+
+City=Seattle
+```
+{
+  "city": "Seattle",
+  "location_id": "1",
+  "model_location_id": 0,
+  "data_source": "open-meteo forecast api (past-hours context only)",
+  "requested_at_utc": "2026-05-08T19:57:14.429521+00:00",
+  "context": {
+    "hours": 72,
+    "start_utc": "2026-05-05T19:00:00+00:00",
+    "end_utc": "2026-05-08T18:00:00+00:00",
+    "start_local": "2026-05-05T12:00:00-07:00",
+    "end_local": "2026-05-08T11:00:00-07:00"
+  },
+  "model": {
+    "encoder_type": "lstm",
+    "seq_len": 72,
+    "input_dim": 22,
+    "num_weather_classes": 7
+  },
+  "forecast": [
+    {
+      "lead_hours": 1,
+      "target_utc": "2026-05-08T19:00:00+00:00",
+      "target_local": "2026-05-08T12:00:00-07:00",
+      "temperature_2m_c": 12.21396255493164,
+      "relative_humidity_2m_pct": 72.33454895019531,
+      "apparent_temperature_c": 10.097986221313477,
+      "precipitation_mm": 0.015628309920430183,
+      "pressure_msl_hpa": 1022.0569458007812,
+      "surface_pressure_hpa": 1014.205078125,
+      "cloud_cover_pct": 94.34225463867188,
+      "wind_speed_10m_kmh": 12.568346977233887,
+      "rain_probability": 0.19356799125671387,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.020174680277705193,
+        "class_1": 0.9282320737838745,
+        "class_2": 0.0022441258188337088,
+        "class_3": 0.04022064805030823,
+        "class_4": 0.008552632294595242,
+        "class_5": 0.0005501594278030097,
+        "class_6": 2.556406798248645e-05
+      }
+    },
+    {
+      "lead_hours": 2,
+      "target_utc": "2026-05-08T20:00:00+00:00",
+      "target_local": "2026-05-08T13:00:00-07:00",
+      "temperature_2m_c": 12.8738374710083,
+      "relative_humidity_2m_pct": 70.51017761230469,
+      "apparent_temperature_c": 10.80291748046875,
+      "precipitation_mm": 0.011432276107370853,
+      "pressure_msl_hpa": 1022.0043334960938,
+      "surface_pressure_hpa": 1014.2881469726562,
+      "cloud_cover_pct": 89.5630111694336,
+      "wind_speed_10m_kmh": 12.822803497314453,
+      "rain_probability": 0.2689012587070465,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.04770936816930771,
+        "class_1": 0.8698592185974121,
+        "class_2": 0.0019157826900482178,
+        "class_3": 0.057819921523332596,
+        "class_4": 0.02183685451745987,
+        "class_5": 0.0008237561560235918,
+        "class_6": 3.5158725950168446e-05
+      }
+    },
+    {
+      "lead_hours": 3,
+      "target_utc": "2026-05-08T21:00:00+00:00",
+      "target_local": "2026-05-08T14:00:00-07:00",
+      "temperature_2m_c": 13.51952075958252,
+      "relative_humidity_2m_pct": 68.44591522216797,
+      "apparent_temperature_c": 11.500271797180176,
+      "precipitation_mm": 0.006943895947188139,
+      "pressure_msl_hpa": 1021.80859375,
+      "surface_pressure_hpa": 1014.2529296875,
+      "cloud_cover_pct": 84.49480438232422,
+      "wind_speed_10m_kmh": 12.941960334777832,
+      "rain_probability": 0.30342426896095276,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.07170139998197556,
+        "class_1": 0.8286910057067871,
+        "class_2": 0.0013093570014461875,
+        "class_3": 0.06433302909135818,
+        "class_4": 0.03300558775663376,
+        "class_5": 0.0009036734118126333,
+        "class_6": 5.590655928244814e-05
+      }
+    },
+    {
+      "lead_hours": 4,
+      "target_utc": "2026-05-08T22:00:00+00:00",
+      "target_local": "2026-05-08T15:00:00-07:00",
+      "temperature_2m_c": 13.970871925354004,
+      "relative_humidity_2m_pct": 66.8187026977539,
+      "apparent_temperature_c": 11.959537506103516,
+      "precipitation_mm": 0.009790810756385326,
+      "pressure_msl_hpa": 1021.4691162109375,
+      "surface_pressure_hpa": 1014.1052856445312,
+      "cloud_cover_pct": 80.34271240234375,
+      "wind_speed_10m_kmh": 13.050889015197754,
+      "rain_probability": 0.33110707998275757,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.10919982939958572,
+        "class_1": 0.7759758830070496,
+        "class_2": 0.0011831748997792602,
+        "class_3": 0.07015678286552429,
+        "class_4": 0.042580746114254,
+        "class_5": 0.000857028178870678,
+        "class_6": 4.6529316023224965e-05
+      }
+    },
+    {
+      "lead_hours": 5,
+      "target_utc": "2026-05-08T23:00:00+00:00",
+      "target_local": "2026-05-08T16:00:00-07:00",
+      "temperature_2m_c": 14.132287979125977,
+      "relative_humidity_2m_pct": 66.1208267211914,
+      "apparent_temperature_c": 12.156023025512695,
+      "precipitation_mm": 0.008600466884672642,
+      "pressure_msl_hpa": 1021.0518188476562,
+      "surface_pressure_hpa": 1013.7891845703125,
+      "cloud_cover_pct": 76.06925201416016,
+      "wind_speed_10m_kmh": 12.926268577575684,
+      "rain_probability": 0.3409281373023987,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
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+        "class_1": 0.7395681142807007,
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+        "class_4": 0.052331216633319855,
+        "class_5": 0.0010767169296741486,
+        "class_6": 4.629969771485776e-05
+      }
+    },
+    {
+      "lead_hours": 6,
+      "target_utc": "2026-05-09T00:00:00+00:00",
+      "target_local": "2026-05-08T17:00:00-07:00",
+      "temperature_2m_c": 13.963343620300293,
+      "relative_humidity_2m_pct": 66.67638397216797,
+      "apparent_temperature_c": 11.971813201904297,
+      "precipitation_mm": 0.010375693440437317,
+      "pressure_msl_hpa": 1020.6505126953125,
+      "surface_pressure_hpa": 1013.4520874023438,
+      "cloud_cover_pct": 73.21341705322266,
+      "wind_speed_10m_kmh": 12.721481323242188,
+      "rain_probability": 0.35606324672698975,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
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+        "class_5": 0.0010369947412982583,
+        "class_6": 4.249428093316965e-05
+      }
+    },
+    {
+      "lead_hours": 7,
+      "target_utc": "2026-05-09T01:00:00+00:00",
+      "target_local": "2026-05-08T18:00:00-07:00",
+      "temperature_2m_c": 13.449448585510254,
+      "relative_humidity_2m_pct": 68.29602813720703,
+      "apparent_temperature_c": 11.426795959472656,
+      "precipitation_mm": 0.012202607467770576,
+      "pressure_msl_hpa": 1020.3434448242188,
+      "surface_pressure_hpa": 1013.139892578125,
+      "cloud_cover_pct": 70.92017364501953,
+      "wind_speed_10m_kmh": 12.359071731567383,
+      "rain_probability": 0.3651714026927948,
+      "weather_class": 1,
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+    },
+    {
+      "lead_hours": 8,
+      "target_utc": "2026-05-09T02:00:00+00:00",
+      "target_local": "2026-05-08T19:00:00-07:00",
+      "temperature_2m_c": 12.755823135375977,
+      "relative_humidity_2m_pct": 70.44146728515625,
+      "apparent_temperature_c": 10.662925720214844,
+      "precipitation_mm": 0.014662384055554867,
+      "pressure_msl_hpa": 1020.1875610351562,
+      "surface_pressure_hpa": 1012.8895874023438,
+      "cloud_cover_pct": 69.15129852294922,
+      "wind_speed_10m_kmh": 11.787208557128906,
+      "rain_probability": 0.3489035665988922,
+      "weather_class": 1,
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+      "weather_class_probabilities": {
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+        "class_6": 4.789666854776442e-05
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+    },
+    {
+      "lead_hours": 9,
+      "target_utc": "2026-05-09T03:00:00+00:00",
+      "target_local": "2026-05-08T20:00:00-07:00",
+      "temperature_2m_c": 11.955390930175781,
+      "relative_humidity_2m_pct": 73.05667877197266,
+      "apparent_temperature_c": 9.77428913116455,
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+      "pressure_msl_hpa": 1020.2242431640625,
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+        "class_6": 3.510143869789317e-05
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+    },
+    {
+      "lead_hours": 10,
+      "target_utc": "2026-05-09T04:00:00+00:00",
+      "target_local": "2026-05-08T21:00:00-07:00",
+      "temperature_2m_c": 11.182319641113281,
+      "relative_humidity_2m_pct": 75.5196304321289,
+      "apparent_temperature_c": 8.978246688842773,
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+      "surface_pressure_hpa": 1012.8126831054688,
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+      "rain_probability": 0.35924479365348816,
+      "weather_class": 1,
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+      "weather_class_probabilities": {
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+        "class_6": 3.633901724242605e-05
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+    },
+    {
+      "lead_hours": 11,
+      "target_utc": "2026-05-09T05:00:00+00:00",
+      "target_local": "2026-05-08T22:00:00-07:00",
+      "temperature_2m_c": 10.499757766723633,
+      "relative_humidity_2m_pct": 77.536865234375,
+      "apparent_temperature_c": 8.306406021118164,
+      "precipitation_mm": 0.01860857754945755,
+      "pressure_msl_hpa": 1020.7318725585938,
+      "surface_pressure_hpa": 1012.9368896484375,
+      "cloud_cover_pct": 65.10720825195312,
+      "wind_speed_10m_kmh": 9.62015151977539,
+      "rain_probability": 0.3694237470626831,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
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+        "class_6": 5.289047476253472e-05
+      }
+    },
+    {
+      "lead_hours": 12,
+      "target_utc": "2026-05-09T06:00:00+00:00",
+      "target_local": "2026-05-08T23:00:00-07:00",
+      "temperature_2m_c": 9.956731796264648,
+      "relative_humidity_2m_pct": 79.21904754638672,
+      "apparent_temperature_c": 7.87125301361084,
+      "precipitation_mm": 0.017959173768758774,
+      "pressure_msl_hpa": 1021.0579833984375,
+      "surface_pressure_hpa": 1013.093994140625,
+      "cloud_cover_pct": 64.14817810058594,
+      "wind_speed_10m_kmh": 8.923616409301758,
+      "rain_probability": 0.3691202700138092,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
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+        "class_4": 0.0741729885339737,
+        "class_5": 0.003270090091973543,
+        "class_6": 5.8019890275318176e-05
+      }
+    }
+  ],
+  "sanity": {
+    "sequence_shape": [
+      72,
+      22
+    ],
+    "finite_features": true
+  }
+}
+PS C:\Users\Paulc> python3.12 weather_infer.py --model_dir "C:\Users\Paulc\weather_model\student_distilled" --city Seattle
+Warning: unexpected keys while loading checkpoint: ['distill_proj.weight']
+{
+  "city": "Seattle",
+  "location_id": "1",
+  "model_location_id": 0,
+  "data_source": "open-meteo forecast api (past-hours context only)",
+  "requested_at_utc": "2026-05-08T19:57:47.439276+00:00",
+  "context": {
+    "hours": 72,
+    "start_utc": "2026-05-05T19:00:00+00:00",
+    "end_utc": "2026-05-08T18:00:00+00:00",
+    "start_local": "2026-05-05T12:00:00-07:00",
+    "end_local": "2026-05-08T11:00:00-07:00"
+  },
+  "model": {
+    "encoder_type": "lstm",
+    "seq_len": 72,
+    "input_dim": 22,
+    "num_weather_classes": 7
+  },
+  "forecast": [
+    {
+      "lead_hours": 1,
+      "target_utc": "2026-05-08T19:00:00+00:00",
+      "target_local": "2026-05-08T12:00:00-07:00",
+      "temperature_2m_c": 13.681238174438477,
+      "relative_humidity_2m_pct": 69.90876770019531,
+      "apparent_temperature_c": 11.687149047851562,
+      "precipitation_mm": 0.0012515264097601175,
+      "pressure_msl_hpa": 1019.3030395507812,
+      "surface_pressure_hpa": 1018.5359497070312,
+      "cloud_cover_pct": 88.76920318603516,
+      "wind_speed_10m_kmh": 13.126839637756348,
+      "rain_probability": 0.1457637995481491,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.02097843959927559,
+        "class_1": 0.9304905533790588,
+        "class_2": 0.0025352241937071085,
+        "class_3": 0.03495039418339729,
+        "class_4": 0.01054275780916214,
+        "class_5": 0.0004654920194298029,
+        "class_6": 3.7044908822281286e-05
+      }
+    },
+    {
+      "lead_hours": 2,
+      "target_utc": "2026-05-08T20:00:00+00:00",
+      "target_local": "2026-05-08T13:00:00-07:00",
+      "temperature_2m_c": 14.486506462097168,
+      "relative_humidity_2m_pct": 67.52698516845703,
+      "apparent_temperature_c": 12.55270767211914,
+      "precipitation_mm": 0.0008608415955677629,
+      "pressure_msl_hpa": 1019.0198364257812,
+      "surface_pressure_hpa": 1018.4589233398438,
+      "cloud_cover_pct": 84.31889343261719,
+      "wind_speed_10m_kmh": 13.241435050964355,
+      "rain_probability": 0.19527363777160645,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.053049832582473755,
+        "class_1": 0.8794819712638855,
+        "class_2": 0.0021074640098959208,
+        "class_3": 0.0436088852584362,
+        "class_4": 0.02111213095486164,
+        "class_5": 0.0005800225771963596,
+        "class_6": 5.971627979306504e-05
+      }
+    },
+    {
+      "lead_hours": 3,
+      "target_utc": "2026-05-08T21:00:00+00:00",
+      "target_local": "2026-05-08T14:00:00-07:00",
+      "temperature_2m_c": 15.089279174804688,
+      "relative_humidity_2m_pct": 65.6773681640625,
+      "apparent_temperature_c": 13.181319236755371,
+      "precipitation_mm": 0.002190209459513426,
+      "pressure_msl_hpa": 1018.709716796875,
+      "surface_pressure_hpa": 1018.2867431640625,
+      "cloud_cover_pct": 80.14619445800781,
+      "wind_speed_10m_kmh": 13.32516860961914,
+      "rain_probability": 0.21867528557777405,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.08254168927669525,
+        "class_1": 0.8380688428878784,
+        "class_2": 0.0014072611229494214,
+        "class_3": 0.04709410294890404,
+        "class_4": 0.030200183391571045,
+        "class_5": 0.0006088378722779453,
+        "class_6": 7.912428554845974e-05
+      }
+    },
+    {
+      "lead_hours": 4,
+      "target_utc": "2026-05-08T22:00:00+00:00",
+      "target_local": "2026-05-08T15:00:00-07:00",
+      "temperature_2m_c": 15.403922080993652,
+      "relative_humidity_2m_pct": 64.67561340332031,
+      "apparent_temperature_c": 13.48654556274414,
+      "precipitation_mm": 0.0021571130491793156,
+      "pressure_msl_hpa": 1018.3944702148438,
+      "surface_pressure_hpa": 1018.0625,
+      "cloud_cover_pct": 76.4104995727539,
+      "wind_speed_10m_kmh": 13.275524139404297,
+      "rain_probability": 0.2299734503030777,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.11604393273591995,
+        "class_1": 0.7922014594078064,
+        "class_2": 0.0011422870447859168,
+        "class_3": 0.05158008262515068,
+        "class_4": 0.03833876550197601,
+        "class_5": 0.000621610670350492,
+        "class_6": 7.179530075518414e-05
+      }
+    },
+    {
+      "lead_hours": 5,
+      "target_utc": "2026-05-08T23:00:00+00:00",
+      "target_local": "2026-05-08T16:00:00-07:00",
+      "temperature_2m_c": 15.407997131347656,
+      "relative_humidity_2m_pct": 64.65668487548828,
+      "apparent_temperature_c": 13.48292350769043,
+      "precipitation_mm": 0.0026813943404704332,
+      "pressure_msl_hpa": 1018.1220703125,
+      "surface_pressure_hpa": 1017.7960205078125,
+      "cloud_cover_pct": 72.77561950683594,
+      "wind_speed_10m_kmh": 13.133893966674805,
+      "rain_probability": 0.23628145456314087,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.14982207119464874,
+        "class_1": 0.7523030638694763,
+        "class_2": 0.0008282885537482798,
+        "class_3": 0.05172016844153404,
+        "class_4": 0.04454538971185684,
+        "class_5": 0.0007084720418788493,
+        "class_6": 7.257604011101648e-05
+      }
+    },
+    {
+      "lead_hours": 6,
+      "target_utc": "2026-05-09T00:00:00+00:00",
+      "target_local": "2026-05-08T17:00:00-07:00",
+      "temperature_2m_c": 15.139252662658691,
+      "relative_humidity_2m_pct": 65.5518798828125,
+      "apparent_temperature_c": 13.192585945129395,
+      "precipitation_mm": 0.0026606114115566015,
+      "pressure_msl_hpa": 1017.9351196289062,
+      "surface_pressure_hpa": 1017.5604858398438,
+      "cloud_cover_pct": 69.60166931152344,
+      "wind_speed_10m_kmh": 12.789706230163574,
+      "rain_probability": 0.23617199063301086,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.17920561134815216,
+        "class_1": 0.7213184237480164,
+        "class_2": 0.0008871846948750317,
+        "class_3": 0.05144878104329109,
+        "class_4": 0.046307649463415146,
+        "class_5": 0.0007639037212356925,
+        "class_6": 6.843609298812225e-05
+      }
+    },
+    {
+      "lead_hours": 7,
+      "target_utc": "2026-05-09T01:00:00+00:00",
+      "target_local": "2026-05-08T18:00:00-07:00",
+      "temperature_2m_c": 14.66390609741211,
+      "relative_humidity_2m_pct": 67.1559066772461,
+      "apparent_temperature_c": 12.692171096801758,
+      "precipitation_mm": 0.002722225384786725,
+      "pressure_msl_hpa": 1017.8424072265625,
+      "surface_pressure_hpa": 1017.3685913085938,
+      "cloud_cover_pct": 66.97268676757812,
+      "wind_speed_10m_kmh": 12.329818725585938,
+      "rain_probability": 0.23410728573799133,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.20177967846393585,
+        "class_1": 0.697116494178772,
+        "class_2": 0.001064434414729476,
+        "class_3": 0.04921679198741913,
+        "class_4": 0.049886710941791534,
+        "class_5": 0.0008780939970165491,
+        "class_6": 5.7844863476930186e-05
+      }
+    },
+    {
+      "lead_hours": 8,
+      "target_utc": "2026-05-09T02:00:00+00:00",
+      "target_local": "2026-05-08T19:00:00-07:00",
+      "temperature_2m_c": 14.042488098144531,
+      "relative_humidity_2m_pct": 69.15681457519531,
+      "apparent_temperature_c": 12.045327186584473,
+      "precipitation_mm": 0.003981542307883501,
+      "pressure_msl_hpa": 1017.853759765625,
+      "surface_pressure_hpa": 1017.2957763671875,
+      "cloud_cover_pct": 64.85920715332031,
+      "wind_speed_10m_kmh": 11.780016899108887,
+      "rain_probability": 0.23837216198444366,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.23561197519302368,
+        "class_1": 0.6629505753517151,
+        "class_2": 0.0012489539803937078,
+        "class_3": 0.04817439988255501,
+        "class_4": 0.050942711532115936,
+        "class_5": 0.0010061608627438545,
+        "class_6": 6.52461385470815e-05
+      }
+    },
+    {
+      "lead_hours": 9,
+      "target_utc": "2026-05-09T03:00:00+00:00",
+      "target_local": "2026-05-08T20:00:00-07:00",
+      "temperature_2m_c": 13.325971603393555,
+      "relative_humidity_2m_pct": 71.42361450195312,
+      "apparent_temperature_c": 11.300540924072266,
+      "precipitation_mm": 0.0030152045655995607,
+      "pressure_msl_hpa": 1017.9505004882812,
+      "surface_pressure_hpa": 1017.2774047851562,
+      "cloud_cover_pct": 63.037109375,
+      "wind_speed_10m_kmh": 11.165238380432129,
+      "rain_probability": 0.23051044344902039,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.24986012279987335,
+        "class_1": 0.6375465989112854,
+        "class_2": 0.0016154011245816946,
+        "class_3": 0.04874761775135994,
+        "class_4": 0.06096767634153366,
+        "class_5": 0.0012142135528847575,
+        "class_6": 4.836557855014689e-05
+      }
+    },
+    {
+      "lead_hours": 10,
+      "target_utc": "2026-05-09T04:00:00+00:00",
+      "target_local": "2026-05-08T21:00:00-07:00",
+      "temperature_2m_c": 12.574642181396484,
+      "relative_humidity_2m_pct": 73.7841796875,
+      "apparent_temperature_c": 10.549814224243164,
+      "precipitation_mm": 0.004971037618815899,
+      "pressure_msl_hpa": 1018.10400390625,
+      "surface_pressure_hpa": 1017.2828979492188,
+      "cloud_cover_pct": 61.4162483215332,
+      "wind_speed_10m_kmh": 10.5538911819458,
+      "rain_probability": 0.23788221180438995,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.27152255177497864,
+        "class_1": 0.6182448863983154,
+        "class_2": 0.0025821358431130648,
+        "class_3": 0.04885515570640564,
+        "class_4": 0.05706281587481499,
+        "class_5": 0.0016854261048138142,
+        "class_6": 4.704758248408325e-05
+      }
+    },
+    {
+      "lead_hours": 11,
+      "target_utc": "2026-05-09T05:00:00+00:00",
+      "target_local": "2026-05-08T22:00:00-07:00",
+      "temperature_2m_c": 11.85836124420166,
+      "relative_humidity_2m_pct": 75.99488830566406,
+      "apparent_temperature_c": 9.845465660095215,
+      "precipitation_mm": 0.0059099141508340836,
+      "pressure_msl_hpa": 1018.2722778320312,
+      "surface_pressure_hpa": 1017.3274536132812,
+      "cloud_cover_pct": 60.944053649902344,
+      "wind_speed_10m_kmh": 10.019789695739746,
+      "rain_probability": 0.24793456494808197,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.27306604385375977,
+        "class_1": 0.6107510328292847,
+        "class_2": 0.004449337720870972,
+        "class_3": 0.0498417466878891,
+        "class_4": 0.05973493307828903,
+        "class_5": 0.002092213137075305,
+        "class_6": 6.465442857006565e-05
+      }
+    },
+    {
+      "lead_hours": 12,
+      "target_utc": "2026-05-09T06:00:00+00:00",
+      "target_local": "2026-05-08T23:00:00-07:00",
+      "temperature_2m_c": 11.196554183959961,
+      "relative_humidity_2m_pct": 78.09349060058594,
+      "apparent_temperature_c": 9.231935501098633,
+      "precipitation_mm": 0.00701399240642786,
+      "pressure_msl_hpa": 1018.4185791015625,
+      "surface_pressure_hpa": 1017.3386840820312,
+      "cloud_cover_pct": 60.26295471191406,
+      "wind_speed_10m_kmh": 9.483912467956543,
+      "rain_probability": 0.2565533518791199,
+      "weather_class": 1,
+      "weather_class_name": "class_1",
+      "weather_class_probabilities": {
+        "class_0": 0.27720507979393005,
+        "class_1": 0.6052833795547485,
+        "class_2": 0.007175811566412449,
+        "class_3": 0.04983551800251007,
+        "class_4": 0.05781771242618561,
+        "class_5": 0.0026247103232890368,
+        "class_6": 5.782112566521391e-05
+      }
+    }
+  ],
+  "sanity": {
+    "sequence_shape": [
+      72,
+      22
+    ],
+    "finite_features": true
+  }
+}
+```
+
+City=Nuuk
+```
+{
+  "city": "Nuuk",
+  "location_id": "83",
+  "model_location_id": 0,
+  "data_source": "open-meteo forecast api (past-hours context only)",
+  "requested_at_utc": "2026-05-08T20:40:35.109779+00:00",
+  "context": {
+    "hours": 72,
+    "start_utc": "2026-05-05T20:00:00+00:00",
+    "end_utc": "2026-05-08T19:00:00+00:00",
+    "start_local": "2026-05-05T19:00:00-01:00",
+    "end_local": "2026-05-08T18:00:00-01:00"
+  },
+  "model": {
+    "encoder_type": "lstm",
+    "seq_len": 72,
+    "input_dim": 22,
+    "num_weather_classes": 7
+  },
+  "forecast": [
+    {
+      "lead_hours": 1,
+      "target_utc": "2026-05-08T20:00:00+00:00",
+      "target_local": "2026-05-08T19:00:00-01:00",
+      "temperature_2m_c": 5.2753753662109375,
+      "relative_humidity_2m_pct": 93.01068115234375,
+      "apparent_temperature_c": 1.6396684646606445,
+      "precipitation_mm": 0.3556472063064575,
+      "pressure_msl_hpa": 1005.5432739257812,
+      "surface_pressure_hpa": 973.415771484375,
+      "cloud_cover_pct": 98.54638671875,
+      "wind_speed_10m_kmh": 13.717008590698242,
+      "rain_probability": 0.9789170026779175,
+      "weather_class": 3,
+      "weather_class_name": "class_3",
+      "weather_class_probabilities": {
+        "class_0": 0.000619232130702585,
+        "class_1": 0.057769663631916046,
+        "class_2": 0.003395488252863288,
+        "class_3": 0.401492714881897,
+        "class_4": 0.18206973373889923,
+        "class_5": 0.3545896112918854,
+        "class_6": 6.364739965647459e-05
+      }
+    },
+    {
+      "lead_hours": 2,
+      "target_utc": "2026-05-08T21:00:00+00:00",
+      "target_local": "2026-05-08T20:00:00-01:00",
+      "temperature_2m_c": 4.986588478088379,
+      "relative_humidity_2m_pct": 93.84243774414062,
+      "apparent_temperature_c": 1.352757453918457,
+      "precipitation_mm": 0.2776345908641815,
+      "pressure_msl_hpa": 1005.599853515625,
+      "surface_pressure_hpa": 973.536376953125,
+      "cloud_cover_pct": 98.45586395263672,
+      "wind_speed_10m_kmh": 13.445389747619629,
+      "rain_probability": 0.9556259512901306,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
+        "class_0": 0.00150326790753752,
+        "class_1": 0.09517476707696915,
+        "class_2": 0.004558710381388664,
+        "class_3": 0.32409900426864624,
+        "class_4": 0.1846529245376587,
+        "class_5": 0.38996437191963196,
+        "class_6": 4.702423757407814e-05
+      }
+    },
+    {
+      "lead_hours": 3,
+      "target_utc": "2026-05-08T22:00:00+00:00",
+      "target_local": "2026-05-08T21:00:00-01:00",
+      "temperature_2m_c": 4.788308143615723,
+      "relative_humidity_2m_pct": 94.0885238647461,
+      "apparent_temperature_c": 1.1667909622192383,
+      "precipitation_mm": 0.23039932548999786,
+      "pressure_msl_hpa": 1005.703857421875,
+      "surface_pressure_hpa": 973.5965576171875,
+      "cloud_cover_pct": 97.65797424316406,
+      "wind_speed_10m_kmh": 13.209211349487305,
+      "rain_probability": 0.9299042820930481,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
+        "class_0": 0.002542331349104643,
+        "class_1": 0.12809209525585175,
+        "class_2": 0.0058285691775381565,
+        "class_3": 0.3043138086795807,
+        "class_4": 0.17225369811058044,
+        "class_5": 0.3869440257549286,
+        "class_6": 2.5515650122542866e-05
+      }
+    },
+    {
+      "lead_hours": 4,
+      "target_utc": "2026-05-08T23:00:00+00:00",
+      "target_local": "2026-05-08T22:00:00-01:00",
+      "temperature_2m_c": 4.660131454467773,
+      "relative_humidity_2m_pct": 94.03594970703125,
+      "apparent_temperature_c": 1.0469179153442383,
+      "precipitation_mm": 0.19706439971923828,
+      "pressure_msl_hpa": 1005.7493896484375,
+      "surface_pressure_hpa": 973.7083740234375,
+      "cloud_cover_pct": 97.17306518554688,
+      "wind_speed_10m_kmh": 13.017566680908203,
+      "rain_probability": 0.9050359129905701,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
+        "class_0": 0.004407276399433613,
+        "class_1": 0.15539932250976562,
+        "class_2": 0.009657401591539383,
+        "class_3": 0.2794102430343628,
+        "class_4": 0.1521354615688324,
+        "class_5": 0.3989632725715637,
+        "class_6": 2.7043566660722718e-05
+      }
+    },
+    {
+      "lead_hours": 5,
+      "target_utc": "2026-05-09T00:00:00+00:00",
+      "target_local": "2026-05-08T23:00:00-01:00",
+      "temperature_2m_c": 4.5112457275390625,
+      "relative_humidity_2m_pct": 93.88682556152344,
+      "apparent_temperature_c": 0.9274702072143555,
+      "precipitation_mm": 0.1685791015625,
+      "pressure_msl_hpa": 1005.7725830078125,
+      "surface_pressure_hpa": 973.7322387695312,
+      "cloud_cover_pct": 96.03288269042969,
+      "wind_speed_10m_kmh": 12.944330215454102,
+      "rain_probability": 0.8804075121879578,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_6": 3.1046529329614714e-05
+      }
+    },
+    {
+      "lead_hours": 6,
+      "target_utc": "2026-05-09T01:00:00+00:00",
+      "target_local": "2026-05-09T00:00:00-01:00",
+      "temperature_2m_c": 4.33610725402832,
+      "relative_humidity_2m_pct": 94.00520324707031,
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+      "cloud_cover_pct": 95.56141662597656,
+      "wind_speed_10m_kmh": 12.845012664794922,
+      "rain_probability": 0.8599434494972229,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_6": 2.3752647393848747e-05
+      }
+    },
+    {
+      "lead_hours": 7,
+      "target_utc": "2026-05-09T02:00:00+00:00",
+      "target_local": "2026-05-09T01:00:00-01:00",
+      "temperature_2m_c": 4.140122413635254,
+      "relative_humidity_2m_pct": 94.25415802001953,
+      "apparent_temperature_c": 0.5866508483886719,
+      "precipitation_mm": 0.13218756020069122,
+      "pressure_msl_hpa": 1005.855712890625,
+      "surface_pressure_hpa": 973.7844848632812,
+      "cloud_cover_pct": 95.55270385742188,
+      "wind_speed_10m_kmh": 12.77564811706543,
+      "rain_probability": 0.8421469330787659,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_6": 1.9363311366760172e-05
+      }
+    },
+    {
+      "lead_hours": 8,
+      "target_utc": "2026-05-09T03:00:00+00:00",
+      "target_local": "2026-05-09T02:00:00-01:00",
+      "temperature_2m_c": 3.953939437866211,
+      "relative_humidity_2m_pct": 94.34648895263672,
+      "apparent_temperature_c": 0.3805828094482422,
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+      "pressure_msl_hpa": 1005.9537353515625,
+      "surface_pressure_hpa": 973.9803466796875,
+      "cloud_cover_pct": 95.32868957519531,
+      "wind_speed_10m_kmh": 12.735028266906738,
+      "rain_probability": 0.8208134174346924,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_5": 0.46144354343414307,
+        "class_6": 2.182190291932784e-05
+      }
+    },
+    {
+      "lead_hours": 9,
+      "target_utc": "2026-05-09T04:00:00+00:00",
+      "target_local": "2026-05-09T03:00:00-01:00",
+      "temperature_2m_c": 3.826430320739746,
+      "relative_humidity_2m_pct": 94.16539001464844,
+      "apparent_temperature_c": 0.16225242614746094,
+      "precipitation_mm": 0.11211992800235748,
+      "pressure_msl_hpa": 1006.1436767578125,
+      "surface_pressure_hpa": 974.2029418945312,
+      "cloud_cover_pct": 94.6148681640625,
+      "wind_speed_10m_kmh": 12.761141777038574,
+      "rain_probability": 0.8099679350852966,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_1": 0.23102521896362305,
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+        "class_4": 0.12005121260881424,
+        "class_5": 0.4399879574775696,
+        "class_6": 2.7478810807224363e-05
+      }
+    },
+    {
+      "lead_hours": 10,
+      "target_utc": "2026-05-09T05:00:00+00:00",
+      "target_local": "2026-05-09T04:00:00-01:00",
+      "temperature_2m_c": 3.8089590072631836,
+      "relative_humidity_2m_pct": 93.53528594970703,
+      "apparent_temperature_c": 0.12103080749511719,
+      "precipitation_mm": 0.10691206157207489,
+      "pressure_msl_hpa": 1006.42529296875,
+      "surface_pressure_hpa": 974.4669189453125,
+      "cloud_cover_pct": 93.8226318359375,
+      "wind_speed_10m_kmh": 12.700864791870117,
+      "rain_probability": 0.797008216381073,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
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+        "class_1": 0.22260957956314087,
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+        "class_5": 0.47975844144821167,
+        "class_6": 2.9636566978297196e-05
+      }
+    },
+    {
+      "lead_hours": 11,
+      "target_utc": "2026-05-09T06:00:00+00:00",
+      "target_local": "2026-05-09T05:00:00-01:00",
+      "temperature_2m_c": 3.9785900115966797,
+      "relative_humidity_2m_pct": 92.22869110107422,
+      "apparent_temperature_c": 0.24558448791503906,
+      "precipitation_mm": 0.10196752846240997,
+      "pressure_msl_hpa": 1006.7659301757812,
+      "surface_pressure_hpa": 974.8555297851562,
+      "cloud_cover_pct": 92.74380493164062,
+      "wind_speed_10m_kmh": 12.71017837524414,
+      "rain_probability": 0.7881969213485718,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
+        "class_0": 0.01727476716041565,
+        "class_1": 0.232261523604393,
+        "class_2": 0.019182473421096802,
+        "class_3": 0.15716883540153503,
+        "class_4": 0.10425136238336563,
+        "class_5": 0.46981269121170044,
+        "class_6": 4.833983985008672e-05
+      }
+    },
+    {
+      "lead_hours": 12,
+      "target_utc": "2026-05-09T07:00:00+00:00",
+      "target_local": "2026-05-09T06:00:00-01:00",
+      "temperature_2m_c": 4.317435264587402,
+      "relative_humidity_2m_pct": 90.45832824707031,
+      "apparent_temperature_c": 0.6276102066040039,
+      "precipitation_mm": 0.09439859539270401,
+      "pressure_msl_hpa": 1007.0864868164062,
+      "surface_pressure_hpa": 975.1940307617188,
+      "cloud_cover_pct": 91.2315673828125,
+      "wind_speed_10m_kmh": 12.740204811096191,
+      "rain_probability": 0.7812836170196533,
+      "weather_class": 5,
+      "weather_class_name": "class_5",
+      "weather_class_probabilities": {
+        "class_0": 0.019589632749557495,
+        "class_1": 0.23847728967666626,
+        "class_2": 0.020689163357019424,
+        "class_3": 0.159035325050354,
+        "class_4": 0.08879318088293076,
+        "class_5": 0.47335243225097656,
+        "class_6": 6.291128374869004e-05
+      }
+    }
+  ],
+  "sanity": {
+    "sequence_shape": [
+      72,
+      22
+    ],
+    "finite_features": true
+  }
+}
+```
+
+### Note
+In observed outputs, the model is often within **1°C** of the actual value, which is **0.7** more than Hweh-6M.
+
+Furthermore, you can pass locations that are not present in the model’s location embedding table. We’ve observed that the model can generalize to out-of-distribution (OOD) cities, with an estimated accuracy drop of only about 2–5%. However, this figure is an estimate and does not reflect a true ground-truth measurement.
+
+## Use Cases
+
+Intended for:
+
+1. Backup to API
+2. Offline forecasting if you have the data
+3. Research
+4. Or more simply, for fun
+
+Not intended for:
+
+1. Safety-critical forecasting (aviation, emergency response)
+2. Replacing meteorological or API services
+
+## Limitations
+
+1. The model is not perfectly accurate and will produce approximate forecasts rather than exact real-world weather conditions.
+2. Prediction accuracy decreases as the forecast horizon increases up to 12 hours.
+3. Performance may degrade on unseen or underrepresented geographic regions and climate types.
+4. The model does not enforce physical laws of atmospheric dynamics and may produce physically inconsistent outputs.
+5. Forecast quality is sensitive to the quality and completeness of input weather data.
+6. Rare or extreme weather events are underrepresented in training data and may be poorly predicted.
+7. Weather class outputs are simplified and do not capture fine-grained meteorological distinctions.
+
+# Inference
+
+```python
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import requests
+import torch
+from transformers import AutoConfig, AutoModel
+from zoneinfo import ZoneInfo
+
+# ----------------------------
+# Change these values here
+# ----------------------------
+MODEL_ID = r"Harley-ml/Hweh-446k"   # HF repo id or local path
+CITY = "New York"
+SEQUENCE_META_PATH = "Harley-ml/Hweh-446k/weather_sequences.metadata.json"
+CONTEXT_HOURS = 72
+FORECAST_HOURS = 12
+DEVICE = None  # "cpu", "cuda", "cuda:0", or None for auto
+
+API_BASE_URL = "https://api.open-meteo.com/v1/forecast"
+MAX_RETRIES = 6
+REQUEST_TIMEOUT_S = 60
+
+HOURLY_VARS = [
+    "temperature_2m",
+    "relative_humidity_2m",
+    "apparent_temperature",
+    "precipitation",
+    "weather_code",
+    "pressure_msl",
+    "surface_pressure",
+    "cloud_cover",
+    "visibility",
+    "wind_speed_10m",
+    "wind_direction_10m",
+]
+
+WEATHER_CODE_BUCKETS = 7
+TEMP_SCALE = 50.0
+HUMIDITY_SCALE = 100.0
+WIND_SCALE = 100.0
+
+# ----------------------------
+# City metadata (82 locations)
+# ----------------------------
+CITY_SPECS: dict[str, dict[str, Any]] = {
+    "Seattle": {"location_id": "1", "latitude": 47.6062, "longitude": -122.3321, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 56},
+    "Portland": {"location_id": "2", "latitude": 45.5152, "longitude": -122.6784, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 15},
+    "San Francisco": {"location_id": "3", "latitude": 37.7749, "longitude": -122.4194, "continent": "North America", "climate_tag": "foggy_mediterranean", "elevation": 16},
+    "Los Angeles": {"location_id": "4", "latitude": 34.0522, "longitude": -118.2437, "continent": "North America", "climate_tag": "sunny_mediterranean", "elevation": 71},
+    "Denver": {"location_id": "5", "latitude": 39.7392, "longitude": -104.9903, "continent": "North America", "climate_tag": "semi_arid_highland", "elevation": 1609},
+    "Chicago": {"location_id": "6", "latitude": 41.8781, "longitude": -87.6298, "continent": "North America", "climate_tag": "humid_continental", "elevation": 181},
+    "Dallas": {"location_id": "7", "latitude": 32.7767, "longitude": -96.7970, "continent": "North America", "climate_tag": "hot_subhumid", "elevation": 131},
+    "Atlanta": {"location_id": "8", "latitude": 33.7490, "longitude": -84.3880, "continent": "North America", "climate_tag": "humid_subtropical", "elevation": 320},
+    "New York": {"location_id": "9", "latitude": 40.7128, "longitude": -74.0060, "continent": "North America", "climate_tag": "humid_subtropical", "elevation": 10},
+    "Miami": {"location_id": "10", "latitude": 25.7617, "longitude": -80.1918, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 2},
+    "Phoenix": {"location_id": "11", "latitude": 33.4484, "longitude": -112.0740, "continent": "North America", "climate_tag": "hot_arid", "elevation": 331},
+    "Salt Lake City": {"location_id": "12", "latitude": 40.7608, "longitude": -111.8910, "continent": "North America", "climate_tag": "semi_arid", "elevation": 1288},
+    "Anchorage": {"location_id": "13", "latitude": 61.2181, "longitude": -149.9003, "continent": "North America", "climate_tag": "subarctic_snowy", "elevation": 31},
+    "Minneapolis": {"location_id": "14", "latitude": 44.9778, "longitude": -93.2650, "continent": "North America", "climate_tag": "cold_snowy", "elevation": 264},
+    "Toronto": {"location_id": "15", "latitude": 43.6532, "longitude": -79.3832, "continent": "North America", "climate_tag": "humid_continental", "elevation": 76},
+    "Montreal": {"location_id": "16", "latitude": 45.5017, "longitude": -73.5673, "continent": "North America", "climate_tag": "cold_snowy", "elevation": 233},
+    "Vancouver": {"location_id": "17", "latitude": 49.2827, "longitude": -123.1207, "continent": "North America", "climate_tag": "temperate_oceanic", "elevation": 70},
+    "Mexico City": {"location_id": "18", "latitude": 19.4326, "longitude": -99.1332, "continent": "North America", "climate_tag": "highland_subtropical", "elevation": 2240},
+    "Havana": {"location_id": "19", "latitude": 23.1136, "longitude": -82.3666, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 59},
+    "San Juan": {"location_id": "20", "latitude": 18.4655, "longitude": -66.1057, "continent": "North America", "climate_tag": "tropical_humid", "elevation": 8},
+
+    "Lima": {"location_id": "21", "latitude": -12.0464, "longitude": -77.0428, "continent": "South America", "climate_tag": "coastal_arid", "elevation": 154},
+    "Santiago": {"location_id": "22", "latitude": -33.4489, "longitude": -70.6693, "continent": "South America", "climate_tag": "mediterranean", "elevation": 520},
+    "Buenos Aires": {"location_id": "23", "latitude": -34.6037, "longitude": -58.3816, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 25},
+    "Bogotá": {"location_id": "24", "latitude": 4.7110, "longitude": -74.0721, "continent": "South America", "climate_tag": "highland_cool", "elevation": 2640},
+    "Quito": {"location_id": "25", "latitude": -0.1807, "longitude": -78.4678, "continent": "South America", "climate_tag": "highland_equatorial", "elevation": 2850},
+    "Caracas": {"location_id": "26", "latitude": 10.4806, "longitude": -66.9036, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 900},
+    "Rio de Janeiro": {"location_id": "27", "latitude": -22.9068, "longitude": -43.1729, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 5},
+    "São Paulo": {"location_id": "28", "latitude": -23.5505, "longitude": -46.6333, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 760},
+    "La Paz": {"location_id": "29", "latitude": -16.4897, "longitude": -68.1193, "continent": "South America", "climate_tag": "highland_cold", "elevation": 3640},
+    "Cusco": {"location_id": "30", "latitude": -13.5319, "longitude": -71.9675, "continent": "South America", "climate_tag": "highland_cool", "elevation": 3399},
+    "Montevideo": {"location_id": "31", "latitude": -34.9011, "longitude": -56.1645, "continent": "South America", "climate_tag": "temperate_oceanic", "elevation": 43},
+    "Asunción": {"location_id": "32", "latitude": -25.2637, "longitude": -57.5759, "continent": "South America", "climate_tag": "humid_subtropical", "elevation": 43},
+    "Manaus": {"location_id": "33", "latitude": -3.1190, "longitude": -60.0217, "continent": "South America", "climate_tag": "tropical_humid", "elevation": 92},
+    "Recife": {"location_id": "34", "latitude": -8.0476, "longitude": -34.8770, "continent": "South America", "climate_tag": "tropical_coastal", "elevation": 4},
+    "Punta Arenas": {"location_id": "35", "latitude": -53.1638, "longitude": -70.9171, "continent": "South America", "climate_tag": "cold_windy", "elevation": 34},
+
+    "London": {"location_id": "36", "latitude": 51.5074, "longitude": -0.1278, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 11},
+    "Paris": {"location_id": "37", "latitude": 48.8566, "longitude": 2.3522, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 35},
+    "Madrid": {"location_id": "38", "latitude": 40.4168, "longitude": -3.7038, "continent": "Europe", "climate_tag": "hot_summer_mediterranean", "elevation": 667},
+    "Rome": {"location_id": "39", "latitude": 41.9028, "longitude": 12.4964, "continent": "Europe", "climate_tag": "hot_summer_mediterranean", "elevation": 21},
+    "Berlin": {"location_id": "40", "latitude": 52.52, "longitude": 13.4050, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 34},
+    "Stockholm": {"location_id": "41", "latitude": 59.3293, "longitude": 18.0686, "continent": "Europe", "climate_tag": "cold_marine", "elevation": 28},
+    "Oslo": {"location_id": "42", "latitude": 59.9139, "longitude": 10.7522, "continent": "Europe", "climate_tag": "cold_snowy", "elevation": 23},
+    "Helsinki": {"location_id": "43", "latitude": 60.1699, "longitude": 24.9384, "continent": "Europe", "climate_tag": "cold_snowy", "elevation": 25},
+    "Reykjavik": {"location_id": "44", "latitude": 64.1466, "longitude": -21.9426, "continent": "Europe", "climate_tag": "cold_windy", "elevation": 12},
+    "Kyiv": {"location_id": "45", "latitude": 50.4501, "longitude": 30.5234, "continent": "Europe", "climate_tag": "humid_continental", "elevation": 179},
+    "Lisbon": {"location_id": "46", "latitude": 38.7223, "longitude": -9.1393, "continent": "Europe", "climate_tag": "sunny_mediterranean", "elevation": 7},
+    "Athens": {"location_id": "47", "latitude": 37.9838, "longitude": 23.7275, "continent": "Europe", "climate_tag": "sunny_mediterranean", "elevation": 70},
+    "Zurich": {"location_id": "48", "latitude": 47.3769, "longitude": 8.5417, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 408},
+    "Dublin": {"location_id": "49", "latitude": 53.3498, "longitude": -6.2603, "continent": "Europe", "climate_tag": "temperate_oceanic", "elevation": 20},
+    "Vienna": {"location_id": "50", "latitude": 48.2082, "longitude": 16.3738, "continent": "Europe", "climate_tag": "temperate_continental", "elevation": 171},
+
+    "Dubai": {"location_id": "51", "latitude": 25.2048, "longitude": 55.2708, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 16},
+    "Riyadh": {"location_id": "52", "latitude": 24.7136, "longitude": 46.6753, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 612},
+    "Delhi": {"location_id": "53", "latitude": 28.7041, "longitude": 77.1025, "continent": "Asia", "climate_tag": "hot_semi_arid", "elevation": 216},
+    "Mumbai": {"location_id": "54", "latitude": 19.0760, "longitude": 72.8777, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 14},
+    "Bangkok": {"location_id": "55", "latitude": 13.7563, "longitude": 100.5018, "continent": "Asia", "climate_tag": "tropical_monsoon", "elevation": 2},
+    "Singapore": {"location_id": "56", "latitude": 1.3521, "longitude": 103.8198, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 15},
+    "Tokyo": {"location_id": "57", "latitude": 35.6762, "longitude": 139.6503, "continent": "Asia", "climate_tag": "humid_subtropical", "elevation": 40},
+    "Seoul": {"location_id": "58", "latitude": 37.5665, "longitude": 126.9780, "continent": "Asia", "climate_tag": "humid_continental", "elevation": 38},
+    "Ulaanbaatar": {"location_id": "59", "latitude": 47.8864, "longitude": 106.9057, "continent": "Asia", "climate_tag": "cold_steppe", "elevation": 1350},
+    "Kathmandu": {"location_id": "60", "latitude": 27.7172, "longitude": 85.3240, "continent": "Asia", "climate_tag": "highland_subtropical", "elevation": 1400},
+    "Chiang Mai": {"location_id": "61", "latitude": 18.7883, "longitude": 98.9853, "continent": "Asia", "climate_tag": "tropical_seasonal", "elevation": 300},
+    "Lhasa": {"location_id": "62", "latitude": 29.6520, "longitude": 91.1721, "continent": "Asia", "climate_tag": "high_altitude_cold", "elevation": 3656},
+    "Jakarta": {"location_id": "63", "latitude": -6.2088, "longitude": 106.8456, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 8},
+    "Manila": {"location_id": "64", "latitude": 14.5995, "longitude": 120.9842, "continent": "Asia", "climate_tag": "tropical_humid", "elevation": 16},
+    "Karachi": {"location_id": "65", "latitude": 24.8607, "longitude": 67.0011, "continent": "Asia", "climate_tag": "hot_arid", "elevation": 10},
+
+    "Cairo": {"location_id": "66", "latitude": 30.0444, "longitude": 31.2357, "continent": "Africa", "climate_tag": "hot_arid", "elevation": 23},
+    "Alexandria": {"location_id": "67", "latitude": 31.2001, "longitude": 29.9187, "continent": "Africa", "climate_tag": "coastal_mediterranean", "elevation": 5},
+    "Casablanca": {"location_id": "68", "latitude": 33.5731, "longitude": -7.5898, "continent": "Africa", "climate_tag": "coastal_mediterranean", "elevation": 56},
+    "Marrakech": {"location_id": "69", "latitude": 31.6295, "longitude": -7.9811, "continent": "Africa", "climate_tag": "hot_semi_arid", "elevation": 466},
+    "Lagos": {"location_id": "70", "latitude": 6.5244, "longitude": 3.3792, "continent": "Africa", "climate_tag": "tropical_humid", "elevation": 41},
+    "Nairobi": {"location_id": "71", "latitude": -1.2921, "longitude": 36.8219, "continent": "Africa", "climate_tag": "temperate_highland", "elevation": 1795},
+    "Addis Ababa": {"location_id": "72", "latitude": 8.9806, "longitude": 38.7578, "continent": "Africa", "climate_tag": "temperate_highland", "elevation": 2355},
+    "Cape Town": {"location_id": "73", "latitude": -33.9249, "longitude": 18.4241, "continent": "Africa", "climate_tag": "mediterranean", "elevation": 25},
+    "Johannesburg": {"location_id": "74", "latitude": -26.2041, "longitude": 28.0473, "continent": "Africa", "climate_tag": "subtropical_highland", "elevation": 1753},
+    "Windhoek": {"location_id": "75", "latitude": -22.5609, "longitude": 17.0658, "continent": "Africa", "climate_tag": "semi_arid", "elevation": 1650},
+    "Accra": {"location_id": "76", "latitude": 5.6037, "longitude": -0.1870, "continent": "Africa", "climate_tag": "tropical_humid", "elevation": 61},
+    "Kigali": {"location_id": "77", "latitude": -1.9441, "longitude": 30.0619, "continent": "Africa", "climate_tag": "highland_tropical", "elevation": 1567},
+    "Tunis": {"location_id": "78", "latitude": 36.8065, "longitude": 10.1815, "continent": "Africa", "climate_tag": "mediterranean", "elevation": 4},
+    "Dakar": {"location_id": "79", "latitude": -14.7167, "longitude": -17.4677, "continent": "Africa", "climate_tag": "hot_coastal", "elevation": 25},
+    "Mombasa": {"location_id": "80", "latitude": -4.0435, "longitude": 39.6682, "continent": "Africa", "climate_tag": "tropical_coastal", "elevation": 17},
+
+    "Sydney": {"location_id": "81", "latitude": -33.8688, "longitude": 151.2093, "continent": "Oceania", "climate_tag": "humid_subtropical", "elevation": 58},
+    "Melbourne": {"location_id": "82", "latitude": -37.8136, "longitude": 144.9631, "continent": "Oceania", "climate_tag": "temperate_oceanic", "elevation": 31},
+}
+
+CITY_TIMEZONES: dict[str, str] = {
+    "Seattle": "America/Los_Angeles",
+    "Portland": "America/Los_Angeles",
+    "San Francisco": "America/Los_Angeles",
+    "Los Angeles": "America/Los_Angeles",
+    "Denver": "America/Denver",
+    "Chicago": "America/Chicago",
+    "Dallas": "America/Chicago",
+    "Atlanta": "America/New_York",
+    "New York": "America/New_York",
+    "Miami": "America/New_York",
+    "Phoenix": "America/Phoenix",
+    "Salt Lake City": "America/Denver",
+    "Anchorage": "America/Anchorage",
+    "Minneapolis": "America/Chicago",
+    "Toronto": "America/Toronto",
+    "Montreal": "America/Toronto",
+    "Vancouver": "America/Vancouver",
+    "Mexico City": "America/Mexico_City",
+    "Havana": "America/Havana",
+    "San Juan": "America/Puerto_Rico",
+    "Lima": "America/Lima",
+    "Santiago": "America/Santiago",
+    "Buenos Aires": "America/Argentina/Buenos_Aires",
+    "Bogotá": "America/Bogota",
+    "Quito": "America/Guayaquil",
+    "Caracas": "America/Caracas",
+    "Rio de Janeiro": "America/Sao_Paulo",
+    "São Paulo": "America/Sao_Paulo",
+    "La Paz": "America/La_Paz",
+    "Cusco": "America/Lima",
+    "Montevideo": "America/Montevideo",
+    "Asunción": "America/Asuncion",
+    "Manaus": "America/Manaus",
+    "Recife": "America/Recife",
+    "Punta Arenas": "America/Punta_Arenas",
+    "London": "Europe/London",
+    "Paris": "Europe/Paris",
+    "Madrid": "Europe/Madrid",
+    "Rome": "Europe/Rome",
+    "Berlin": "Europe/Berlin",
+    "Stockholm": "Europe/Stockholm",
+    "Oslo": "Europe/Oslo",
+    "Helsinki": "Europe/Helsinki",
+    "Reykjavik": "Atlantic/Reykjavik",
+    "Kyiv": "Europe/Kyiv",
+    "Lisbon": "Europe/Lisbon",
+    "Athens": "Europe/Athens",
+    "Zurich": "Europe/Zurich",
+    "Dublin": "Europe/Dublin",
+    "Vienna": "Europe/Vienna",
+    "Dubai": "Asia/Dubai",
+    "Riyadh": "Asia/Riyadh",
+    "Delhi": "Asia/Kolkata",
+    "Mumbai": "Asia/Kolkata",
+    "Bangkok": "Asia/Bangkok",
+    "Singapore": "Asia/Singapore",
+    "Tokyo": "Asia/Tokyo",
+    "Seoul": "Asia/Seoul",
+    "Ulaanbaatar": "Asia/Ulaanbaatar",
+    "Kathmandu": "Asia/Kathmandu",
+    "Chiang Mai": "Asia/Bangkok",
+    "Lhasa": "Asia/Shanghai",
+    "Jakarta": "Asia/Jakarta",
+    "Manila": "Asia/Manila",
+    "Karachi": "Asia/Karachi",
+    "Cairo": "Africa/Cairo",
+    "Alexandria": "Africa/Cairo",
+    "Casablanca": "Africa/Casablanca",
+    "Marrakech": "Africa/Casablanca",
+    "Lagos": "Africa/Lagos",
+    "Nairobi": "Africa/Nairobi",
+    "Addis Ababa": "Africa/Addis_Ababa",
+    "Cape Town": "Africa/Johannesburg",
+    "Johannesburg": "Africa/Johannesburg",
+    "Windhoek": "Africa/Windhoek",
+    "Accra": "Africa/Accra",
+    "Kigali": "Africa/Kigali",
+    "Tunis": "Africa/Tunis",
+    "Dakar": "Africa/Dakar",
+    "Mombasa": "Africa/Nairobi",
+    "Sydney": "Australia/Sydney",
+    "Melbourne": "Australia/Melbourne",
+}
+
+# ----------------------------
+# Helpers
+# ----------------------------
+def weather_code_to_bucket(code) -> int:
+    if code is None:
+        return 1
+    try:
+        if pd.isna(code):
+            return 1
+    except Exception:
+        pass
+
+    code = int(code)
+    if code == 0:
+        return 0
+    if code in (1, 2, 3):
+        return 1
+    if code in (45, 48):
+        return 2
+    if code in (51, 53, 55, 56, 57):
+        return 3
+    if code in (61, 63, 65, 66, 67, 80, 81, 82):
+        return 4
+    if code in (71, 73, 75, 77, 85, 86):
+        return 5
+    if code in (95, 96, 99):
+        return 6
+    return 1
+
+
+def cyc(x: np.ndarray, period: float) -> tuple[np.ndarray, np.ndarray]:
+    angle = 2.0 * np.pi * (x / period)
+    return np.sin(angle), np.cos(angle)
+
+
+def clamp_array(x: np.ndarray, lo: float | None = None, hi: float | None = None) -> np.ndarray:
+    return np.clip(x, lo, hi)
+
+
+def request_with_backoff(session: requests.Session, url: str, params: dict[str, Any]) -> dict[str, Any]:
+    last_exc: Exception | None = None
+    for attempt in range(MAX_RETRIES):
+        try:
+            resp = session.get(url, params=params, timeout=REQUEST_TIMEOUT_S)
+            if resp.status_code == 429:
+                retry_after = resp.headers.get("Retry-After")
+                sleep_s = float(retry_after) if retry_after else min(60.0, 2**attempt)
+                print(f"Rate limited. Sleeping {sleep_s:.1f}s and retrying.", flush=True)
+                time.sleep(sleep_s)
+                continue
+            resp.raise_for_status()
+            return resp.json()
+        except Exception as e:
+            last_exc = e
+            sleep_s = min(60.0, 2**attempt)
+            print(f"Request failed: {e}. Sleeping {sleep_s:.1f}s and retrying.", flush=True)
+            time.sleep(sleep_s)
+    raise RuntimeError(f"Failed after {MAX_RETRIES} retries: {params}") from last_exc
+
+
+def load_sequence_meta(path: str) -> dict[str, Any]:
+    p = Path(path)
+    if not p.exists():
+        return {"location_to_id": {}}
+    with open(p, "r", encoding="utf-8") as f:
+        meta = json.load(f)
+    meta.setdefault("location_to_id", {})
+    return meta
+
+
+def load_model():
+    config = AutoConfig.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModel.from_pretrained(MODEL_ID, config=config, trust_remote_code=True)
+    model.eval()
+    return model, config
+
+
+def fetch_recent_history(city: str, context_hours: int) -> pd.DataFrame:
+    if city not in CITY_SPECS:
+        raise ValueError(f"Unknown city: {city}")
+
+    spec = CITY_SPECS[city]
+    session = requests.Session()
+    session.headers.update({"User-Agent": "Mozilla/5.0"})
+
+    params = {
+        "latitude": spec["latitude"],
+        "longitude": spec["longitude"],
+        "hourly": ",".join(HOURLY_VARS),
+        "timezone": "UTC",
+        "temperature_unit": "celsius",
+        "wind_speed_unit": "kmh",
+        "precipitation_unit": "mm",
+        "past_hours": int(context_hours) + 2,
+        "forecast_hours": 0,
+    }
+
+    data = request_with_backoff(session, API_BASE_URL, params=params)
+    hourly = data.get("hourly", {})
+    if "time" not in hourly:
+        raise ValueError(f"No hourly data returned for {city}: {data}")
+
+    df = pd.DataFrame(hourly)
+    if df.empty:
+        raise ValueError(f"Empty hourly response for {city}.")
+
+    df["time"] = pd.to_datetime(df["time"], errors="coerce", utc=True)
+    df = df.dropna(subset=["time"]).sort_values("time").drop_duplicates(subset=["time"]).reset_index(drop=True)
+
+    needed = HOURLY_VARS
+    missing = [c for c in needed if c not in df.columns]
+    if missing:
+        raise ValueError(f"Missing hourly columns in API response: {missing}")
+
+    for c in needed:
+        df[c] = pd.to_numeric(df[c], errors="coerce")
+
+    df["weather_code"] = df["weather_code"].fillna(1)
+    df["precipitation"] = df["precipitation"].fillna(0.0)
+
+    for c in [
+        "temperature_2m",
+        "relative_humidity_2m",
+        "apparent_temperature",
+        "precipitation",
+        "pressure_msl",
+        "surface_pressure",
+        "cloud_cover",
+        "visibility",
+        "wind_speed_10m",
+        "wind_direction_10m",
+    ]:
+        df[c] = df[c].interpolate(limit_direction="both").ffill().bfill()
+
+    now_utc = pd.Timestamp.now(tz="UTC")
+    df = df[df["time"] <= now_utc].copy()
+
+    if len(df) < context_hours:
+        raise ValueError(f"Not enough observed rows: got {len(df)}, need {context_hours}")
+
+    return df.tail(context_hours).reset_index(drop=True)
+
+
+def build_single_sequence(df: pd.DataFrame) -> np.ndarray:
+    hour = df["time"].dt.hour.to_numpy()
+    doy = df["time"].dt.dayofyear.to_numpy()
+
+    hour_sin, hour_cos = cyc(hour.astype(float), 24.0)
+    doy_sin, doy_cos = cyc(doy.astype(float), 365.25)
+
+    temp = np.nan_to_num(df["temperature_2m"].astype(float).to_numpy(), nan=0.0)
+    humidity = np.nan_to_num(df["relative_humidity_2m"].astype(float).to_numpy(), nan=0.0)
+    apparent = np.nan_to_num(df["apparent_temperature"].astype(float).to_numpy(), nan=0.0)
+    precip = np.nan_to_num(df["precipitation"].astype(float).to_numpy(), nan=0.0)
+    pressure = np.nan_to_num(df["pressure_msl"].astype(float).to_numpy(), nan=0.0)
+    surface_pressure = np.nan_to_num(df["surface_pressure"].astype(float).to_numpy(), nan=0.0)
+    cloud_cover = np.nan_to_num(df["cloud_cover"].astype(float).to_numpy(), nan=0.0)
+    visibility = np.nan_to_num(df["visibility"].astype(float).to_numpy(), nan=0.0)
+    wind = np.nan_to_num(df["wind_speed_10m"].astype(float).to_numpy(), nan=0.0)
+    wind_dir = np.nan_to_num(df["wind_direction_10m"].astype(float).to_numpy(), nan=0.0)
+
+    humidity = clamp_array(humidity, 0.0, 100.0)
+    cloud_cover = clamp_array(cloud_cover, 0.0, 100.0)
+    precip = clamp_array(precip, 0.0, None)
+    wind = clamp_array(wind, 0.0, None)
+    visibility = clamp_array(visibility, 0.0, None)
+
+    wind_dir_sin, wind_dir_cos = cyc(wind_dir, 360.0)
+    weather_bucket = df["weather_code"].fillna(1).apply(weather_code_to_bucket).to_numpy(dtype=np.int64)
+
+    rows = []
+    for i in range(len(df)):
+        wc_oh = np.zeros(WEATHER_CODE_BUCKETS, dtype=np.float32)
+        wc_oh[weather_bucket[i]] = 1.0
+
+        row = np.concatenate(
+            [
+                np.array(
+                    [
+                        temp[i] / TEMP_SCALE,
+                        humidity[i] / HUMIDITY_SCALE,
+                        apparent[i] / TEMP_SCALE,
+                        np.log1p(max(precip[i], 0.0)) / 3.0,
+                        pressure[i] / 1100.0,
+                        surface_pressure[i] / 1100.0,
+                        cloud_cover[i] / 100.0,
+                        visibility[i] / 50000.0,
+                        wind[i] / WIND_SCALE,
+                        wind_dir_sin[i],
+                        wind_dir_cos[i],
+                        hour_sin[i],
+                        hour_cos[i],
+                        doy_sin[i],
+                        doy_cos[i],
+                    ],
+                    dtype=np.float32,
+                ),
+                wc_oh,
+            ]
+        )
+        rows.append(row)
+
+    seq = np.asarray(rows, dtype=np.float32)
+
+    if not np.isfinite(seq).all():
+        bad = np.argwhere(~np.isfinite(seq))
+        raise ValueError(f"Non-finite values remain in sequence at positions like: {bad[:10].tolist()}")
+
+    return seq
+
+
+def to_iso(ts: pd.Timestamp, tz_name: str | None = None) -> str:
+    if tz_name:
+        try:
+            return ts.tz_convert(ZoneInfo(tz_name)).isoformat()
+        except Exception:
+            pass
+    return ts.isoformat()
+
+
+def get_logits(out):
+    if isinstance(out, dict) and "logits" in out:
+        return out["logits"]
+    if hasattr(out, "logits"):
+        return out.logits
+    return out
+
+
+def resolve_location_index(seq_meta: dict[str, Any], city_location_id: str) -> int:
+    location_to_id = seq_meta.get("location_to_id", {})
+
+    if city_location_id in location_to_id:
+        return int(location_to_id[city_location_id])
+
+    try:
+        as_int = int(city_location_id)
+        if as_int in location_to_id:
+            return int(location_to_id[as_int])
+        if str(as_int) in location_to_id:
+            return int(location_to_id[str(as_int)])
+    except Exception:
+        pass
+
+    for unk_key in ("UNK", "<UNK>", "unknown", "UNKNOWN"):
+        if unk_key in location_to_id:
+            return int(location_to_id[unk_key])
+
+    return 0
+
+
+def predict():
+    seq_meta = load_sequence_meta(SEQUENCE_META_PATH)
+    model, config = load_model()
+
+    if CITY not in CITY_SPECS:
+        raise ValueError(f"Unknown city: {CITY}")
+
+    if CONTEXT_HOURS <= 0:
+        raise ValueError("CONTEXT_HOURS must be > 0")
+
+    if hasattr(config, "seq_len") and int(config.seq_len) != CONTEXT_HOURS:
+        raise ValueError(f"Set CONTEXT_HOURS to {int(config.seq_len)} for this model.")
+
+    city_spec = CITY_SPECS[CITY]
+    city_tz = CITY_TIMEZONES.get(CITY, "UTC")
+    model_location_id = resolve_location_index(seq_meta, str(city_spec["location_id"]))
+
+    df = fetch_recent_history(CITY, CONTEXT_HOURS)
+    seq = build_single_sequence(df)
+
+    X = torch.from_numpy(seq).unsqueeze(0)
+    loc = torch.tensor([model_location_id], dtype=torch.long)
+
+    target_device = torch.device(
+        DEVICE if DEVICE else ("cuda" if torch.cuda.is_available() else "cpu")
+    )
+    model = model.to(target_device)
+    X = X.to(target_device)
+    loc = loc.to(target_device)
+
+    weather_class_names = getattr(config, "weather_class_names", None)
+    if not weather_class_names:
+        weather_class_names = [f"class_{i}" for i in range(int(getattr(config, "num_weather_classes", 7)))]
+
+    with torch.no_grad():
+        out = model(X=X, location_id=loc)
+        logits = get_logits(out)
+
+        (
+            temp_pred,
+            humidity_pred,
+            apparent_pred,
+            precip_pred,
+            sea_level_pressure_pred,
+            surface_pressure_pred,
+            cloud_cover_pred,
+            wind_pred,
+            wind_dir_sin_pred,
+            wind_dir_cos_pred,
+            rain_logit,
+            weather_logits,
+        ) = logits
+
+        temp_pred = temp_pred.squeeze(0).detach().cpu().numpy()
+        humidity_pred = humidity_pred.squeeze(0).detach().cpu().numpy()
+        apparent_pred = apparent_pred.squeeze(0).detach().cpu().numpy()
+        precip_pred = precip_pred.squeeze(0).detach().cpu().numpy()
+        sea_level_pressure_pred = sea_level_pressure_pred.squeeze(0).detach().cpu().numpy()
+        surface_pressure_pred = surface_pressure_pred.squeeze(0).detach().cpu().numpy()
+        cloud_cover_pred = cloud_cover_pred.squeeze(0).detach().cpu().numpy()
+        wind_pred = wind_pred.squeeze(0).detach().cpu().numpy()
+        rain_prob = torch.sigmoid(rain_logit).squeeze(0).detach().cpu().numpy()
+        weather_probs = torch.softmax(weather_logits, dim=-1).squeeze(0).detach().cpu().numpy()
+        weather_idx = np.argmax(weather_probs, axis=-1).astype(np.int64)
+
+        humidity_pred = np.clip(humidity_pred, 0.0, 100.0)
+        cloud_cover_pred = np.clip(cloud_cover_pred, 0.0, 100.0)
+        precip_pred = np.clip(precip_pred, 0.0, None)
+        wind_pred = np.clip(wind_pred, 0.0, None)
+        rain_prob = np.clip(rain_prob, 0.0, 1.0)
+
+    context_start = df["time"].iloc[0]
+    context_end = df["time"].iloc[-1]
+    requested_at_utc = pd.Timestamp.now(tz="UTC")
+
+    horizon = min(
+        int(FORECAST_HOURS),
+        int(temp_pred.shape[0]),
+        int(humidity_pred.shape[0]),
+        int(weather_idx.shape[0]),
+    )
+
+    forecast = []
+    for lead in range(1, horizon + 1):
+        target_time = context_end + pd.Timedelta(hours=lead)
+        idx = lead - 1
+        w_idx = int(weather_idx[idx])
+
+        forecast.append(
+            {
+                "lead_hours": lead,
+                "target_utc": target_time.isoformat(),
+                "target_local": to_iso(target_time, city_tz),
+                "temperature_2m_c": float(temp_pred[idx]),
+                "relative_humidity_2m_pct": float(humidity_pred[idx]),
+                "apparent_temperature_c": float(apparent_pred[idx]),
+                "precipitation_mm": float(precip_pred[idx]),
+                "pressure_msl_hpa": float(sea_level_pressure_pred[idx]),
+                "surface_pressure_hpa": float(surface_pressure_pred[idx]),
+                "cloud_cover_pct": float(cloud_cover_pred[idx]),
+                "wind_speed_10m_kmh": float(wind_pred[idx]),
+                "rain_probability": float(rain_prob[idx]),
+                "weather_class": w_idx,
+                "weather_class_name": weather_class_names[w_idx] if w_idx < len(weather_class_names) else f"class_{w_idx}",
+                "weather_class_probabilities": {
+                    name: float(prob) for name, prob in zip(weather_class_names, weather_probs[idx])
+                },
+            }
+        )
+
+    result = {
+        "city": CITY,
+        "location_id": str(city_spec["location_id"]),
+        "model_location_id": int(model_location_id),
+        "data_source": "open-meteo forecast api (past-hours context only)",
+        "requested_at_utc": requested_at_utc.isoformat(),
+        "context": {
+            "hours": int(len(df)),
+            "start_utc": context_start.isoformat(),
+            "end_utc": context_end.isoformat(),
+            "start_local": to_iso(context_start, city_tz),
+            "end_local": to_iso(context_end, city_tz),
+        },
+        "model": {
+            "model_id": MODEL_ID,
+            "encoder_type": getattr(config, "encoder_type", None),
+            "seq_len": int(getattr(config, "seq_len", CONTEXT_HOURS)),
+            "input_dim": int(getattr(config, "input_dim", seq.shape[1])),
+            "num_weather_classes": int(getattr(config, "num_weather_classes", len(weather_class_names))),
+        },
+        "forecast": forecast,
+        "sanity": {
+            "sequence_shape": list(seq.shape),
+            "finite_features": bool(np.isfinite(seq).all()),
+        },
+    }
+
+    print(json.dumps(result, indent=2))
+
+
+if __name__ == "__main__":
+    predict()
+```
+
+
+## Citation
+
+```bibtex
+@misc{Hweh-6m,
+  title = {Hweh-446k: Knowledge Distillation in Short-Term Multivariate Weather Forecasting},
+  author    = {Paul Courneya; Harley-ml},
+  year      = {2026},
+  url       = {https://huggingface.co/Harley-ml/DistilHweh-446k}
+}
+```