Unzipping file

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+fcn.mdlus filter=lfs diff=lfs merge=lfs -text
+global_means.npy filter=lfs diff=lfs merge=lfs -text
+global_stds.npy filter=lfs diff=lfs merge=lfs -text

README.md

@@ -22,7 +22,7 @@ Global
 Industry, academic, and government research teams interested in medium-range and
 subseasonal-to-seasonal weather forecasting, and climate modeling.
 
-### Reference(s)
+### References:
 
 **Papers**:
 
@@ -78,7 +78,7 @@ By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g.
 libraries), the model achieves faster training and inference times compared to
 CPU-only solutions.
 
-## Software Integration
+## Software Integration:
 
 **Runtime Engine:** Pytorch <br>
 **Supported Hardware Microarchitecture Compatibility:** <br>
@@ -91,11 +91,11 @@ CPU-only solutions.
 
 - Linux <br>
 
-## Model Version
+## Model Version:
 
 **Model Version:** v1 <br>
 
-## Training Dataset
+## Training Dataset:
 
 **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
 
@@ -112,7 +112,7 @@ ERA5 data for the period 1980-2015. ERA5 provides hourly estimates of various
 atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
 grid and resolves the atmosphere at 137 levels. <br>
 
-## Testing Dataset
+## Testing Dataset:
 
 **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
 
@@ -129,7 +129,7 @@ ERA5 data for the period 2016-2017. ERA5 provides hourly estimates of various
 atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
 grid and resolves the atmosphere at 137 levels. <br>
 
-## Evaluation Dataset
+## Evaluation Dataset:
 
 **Link:** [ERA5](https://cds.climate.copernicus.eu/) <br>
 
@@ -146,7 +146,7 @@ ERA5 data for the period 2018-2019. ERA5 provides hourly estimates of various
 atmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km
 grid and resolves the atmosphere at 137 levels. <br>
 
-## Inference
+## Inference:
 
 **Acceleration Engine:** Pytorch <br>
 **Test Hardware:**
@@ -155,7 +155,7 @@ grid and resolves the atmosphere at 137 levels. <br>
 - H100 <br>
 - L40S <br>
 
-## Ethical Considerations
+## Ethical Considerations:
 
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established
 policies and practices to enable development for a wide array of AI applications.

config.json

@@ -0,0 +1,3 @@
+{
+    "name": "fcn"
+}

fcn.zip → fcn.mdlus

@@ -1,3 +1,3 @@
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global_means.npy

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global_stds.npy

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metadata.json

@@ -0,0 +1,5 @@
+{
+	"entrypoint": {
+        "name": "earth2mip.networks.fcn:load"
+      }
+}