Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+library_name: pytorch
+---
+
+
+# Cloudcasting
+
+## Model Description
+
+These models are trained to predict future frames of satellite data from past frames. The model uses 3 hours
+of recent satellite imagery at 15 minute intervals and predicts 3 hours into the future also at
+15 minute intervals. The satellite inputs and predictions are multispectral with 11 channels.
+
+
+See [1] for the repo used to train the model.
+
+- **Developed by:** Open Climate Fix and the Alan Turing Institute
+- **License:** mit
+
+
+# Training Details
+
+## Data
+
+This was trained on EUMETSAT satellite imagery derived from the data stored in [this google public 
+dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-data/eumetsat-seviri-rss?hl=en-GB&inv=1&invt=AbniZA&project=solar-pv-nowcasting&pli=1). 
+
+The data was processed using the protocol in [2]
+
+
+## Results
+
+See the READMEs in each model dir for links to the wandb training runs
+
+
+## Usage
+
+These models rely on [1] being installed. Example usage to load the model is shown below
+
+```{python}
+import hydra
+import yaml
+from huggingface_hub import snapshot_download
+from safetensors.torch import load_model
+
+
+REPO_ID = "openclimatefix/cloudcasting_example_models"
+REVISION = <commit-id>
+MODEL = "simvp_model"
+
+# Download the model checkpoints
+hf_download_dir = snapshot_download(
+    repo_id=REPO_ID,
+    revision=REVISION,
+)
+
+# Create the model object
+with open(f"{hf_download_dir}/model_config.yaml", "r", encoding="utf-8") as f:
+    model = hydra.utils.instantiate(yaml.safe_load(f))
+
+# Load the model weights
+load_model(
+    model,
+    filename=f"{hf_download_dir}/model.safetensors", 
+    strict=True,
+)
+```
+
+### Software
+
+- [1] https://github.com/openclimatefix/sat_pred
+- [2] https://github.com/alan-turing-institute/cloudcasting