Say what the checkpoint is for and how to regenerate it

README.md

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+---
+library_name: transformers
+tags:
+- weathernext2
+- tiny-random
+- testing
+---
+
+# Tiny random WeatherNext 2
+
+A randomly initialized [`WeatherNext2ForWeatherForecasting`](https://huggingface.co/docs/transformers/model_doc/weathernext2)
+for use in the Transformers test suite. 13K parameters, 152 KB. It forecasts nothing: the weights are
+noise and the feature extractor's statistics are zero mean and unit variance.
+
+What it is for is the **geometry**. WeatherNext 2 has no learned positional encodings, so the mesh,
+both grid-mesh graphs and the banded attention mask are built once at conversion time, with `scipy`
+and `trimesh`, and stored in the checkpoint as buffers. A model built from a config alone gets a
+placeholder instead, which is enough to run but is not a mesh. This checkpoint carries the real
+thing at a size CI can download, so the tests that depend on a real mesh have one.
+
+The grid is 15 degrees and the mesh is a twice-split icosahedron: 162 mesh nodes over 312 grid
+points, an attention bandwidth of 43, so four attention blocks. Four is the point, since it gives an
+interior block with a neighbour on either side.
+
+## Regenerating it
+
+```python
+import torch
+from transformers import WeatherNext2Config, WeatherNext2FeatureExtractor, WeatherNext2ForWeatherForecasting
+from transformers.models.weathernext2.convert_weathernext2_original_checkpoint import geometry_state_dict
+
+config = WeatherNext2Config(
+    hidden_size=16, intermediate_size=32, num_hidden_layers=2, num_attention_heads=2,
+    edge_hidden_size=4, noise_channels=4, mesh_splits=2, attention_k_hop=2,
+    grid_latitudes=13, grid_longitudes=24, pressure_levels=(500, 850), aggregate_normalization=None,
+)
+geometry = geometry_state_dict(config)  # needs scipy and trimesh; sets the two derived sizes
+
+torch.manual_seed(0)
+model = WeatherNext2ForWeatherForecasting(config)
+model.load_state_dict(geometry, strict=False)
+```
+
+The feature extractor is built from the same config with zero means and unit standard deviations for
+every variable.