Time Series Forecasting
Transformers
Safetensors
fela_grid_renewable
feature-extraction
fela
fourier-neural-operator
fno
cpu
on-device
energy-forecasting
solar-power
wind-power
probabilistic-forecasting
quantile-regression
custom_code
Instructions to use lowdown-labs/fela-power-grid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-power-grid with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowdown-labs/fela-power-grid", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download configuration_grid.py from lowdown-labs/fela-power-grid: direct link, hf CLI and curl.
- Browser
- Download file 998 Bytes
-
https://huggingface.co/lowdown-labs/fela-power-grid/resolve/main/configuration_grid.py
- Command line
-
hf download hf://lowdown-labs/fela-power-grid/configuration_grid.py
-
curl -L -o configuration_grid.py https://huggingface.co/lowdown-labs/fela-power-grid/resolve/main/configuration_grid.py
998 Bytes
| from transformers import PretrainedConfig | |
| class FelaGridConfig(PretrainedConfig): | |
| model_type = "fela_grid_renewable" | |
| def __init__( | |
| self, | |
| track="solar", | |
| Fin=20, | |
| L=6, | |
| D=64, | |
| modes=3, | |
| nblk=4, | |
| nq=99, | |
| arch="dual", | |
| tracks=None, | |
| **kwargs, | |
| ): | |
| if isinstance(tracks, dict) and track in tracks: | |
| dims = tracks[track].get("dims", {}) | |
| Fin = dims.get("Fin", Fin) | |
| L = dims.get("L", L) | |
| D = dims.get("D", D) | |
| modes = dims.get("modes", modes) | |
| nblk = dims.get("nblk", nblk) | |
| nq = dims.get("nq", nq) | |
| arch = dims.get("arch", arch) | |
| self.track = track | |
| self.Fin = Fin | |
| self.L = L | |
| self.D = D | |
| self.modes = modes | |
| self.nblk = nblk | |
| self.nq = nq | |
| self.arch = arch | |
| if tracks is not None: | |
| self.tracks = tracks | |
| super().__init__(**kwargs) | |