Update README.md

README.md

@@ -36,6 +36,25 @@ which can be accessed from [here](https://huggingface.co/ibm-granite/granite-tim
 trained on larger pretraining dataset. However, the choice of R1 vs R2 depends on your target data distribution. Hence requesting users to try both
 R1 and R2 variants and pick the best for your data.
 
+
+
+## Model Description
+
+TTM falls under the category of “focused pre-trained models”, wherein each pre-trained TTM is tailored for a particular forecasting 
+setting (governed by the context length and forecast length). Instead of building one massive model supporting all forecasting settings, 
+we opt for the approach of constructing smaller pre-trained models, each focusing on a specific forecasting setting, thereby 
+yielding more accurate results. Furthermore, this approach ensures that our models remain extremely small and exceptionally fast, 
+facilitating easy deployment without demanding a ton of resources. 
+
+Hence, in this model card, we plan to release several pre-trained 
+TTMs that can cater to many common forecasting settings in practice. Additionally, we have released our source code along with 
+our pretraining scripts that users can utilize to pretrain models on their own. Pretraining TTMs is very easy and fast, taking 
+3-6 hours for R1 versions and 12-24 hours for R2 versions using 6 A100 GPUs, as opposed to several days or weeks in traditional approaches.
+
+Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our 
+getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) mentioning the branch name.
+
+
 ## Model Releases (along with the branch name where the models are stored):
 
 
@@ -89,22 +108,6 @@ paper, which may lead to minor variations in model performance as compared to th
    impact the model performance. 
 
 
-## Model Description
-
-TTM falls under the category of “focused pre-trained models”, wherein each pre-trained TTM is tailored for a particular forecasting 
-setting (governed by the context length and forecast length). Instead of building one massive model supporting all forecasting settings, 
-we opt for the approach of constructing smaller pre-trained models, each focusing on a specific forecasting setting, thereby 
-yielding more accurate results. Furthermore, this approach ensures that our models remain extremely small and exceptionally fast, 
-facilitating easy deployment without demanding a ton of resources. 
-
-Hence, in this model card, we plan to release several pre-trained 
-TTMs that can cater to many common forecasting settings in practice. Additionally, we have released our source code along with 
-our pretraining scripts that users can utilize to pretrain models on their own. Pretraining TTMs is very easy and fast, taking 
-only 3-6 hours using 6 A100 GPUs, as opposed to several days or weeks in traditional approaches.
-
-Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our 
-getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) mentioning the branch name.
-
 
 ## Model Details