Update README.md

README.md

@@ -123,7 +123,8 @@ the upper atmosphere (e.g., 50 hPa) contribute relatively little to the total lo
 
 Data parallelism is used for training, with a batch size of 16. One model instance is split across four 40GB A100
 GPUs within one node. Training is done using mixed precision (Micikevicius et al. [2018]), and the entire process
-takes about one week, with 64 GPUs in total.
+takes about one week, with 64 GPUs in total. The checkpoint size is 1.19 GB and it does not include the optimizer 
+state.
 
 ## Evaluation
 
@@ -163,36 +164,16 @@ takes about one week, with 64 GPUs in total.
 
 {{ model_examination | default("[More Information Needed]", true)}}
 
-## Environmental Impact
+## Technical Specifications
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+### Hardware
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+<!--  {{ hardware_requirements | default("[More Information Needed]", true)}} -->
 
-- **Hardware Type:** {{ hardware_type | default("[More Information Needed]", true)}}
-- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
-- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
-- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
-- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}
+We acknowledge PRACE for awarding us access to Leonardo, CINECA, Italy. In particular, this AIFS version has been trained 
+over 64 A100 GPUs (40GB).
 
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-{{ model_specs | default("[More Information Needed]", true)}}
-
-### Compute Infrastructure
-
-{{ compute_infrastructure | default("[More Information Needed]", true)}}
-
-#### Hardware
-
-{{ hardware_requirements | default("[More Information Needed]", true)}}
-
-We acknowledge PRACE for awarding us access to Leonardo, CINECA, Italy
-
-
-#### Software
+### Software
 
 The model was developed and trained using the [AnemoI framework](https://anemoi-docs.readthedocs.io/en/latest/index.html).
 AnemoI is a framework for developing machine learning weather forecasting models. It comprises of components or packages