---
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.