Update README.md

README.md

@@ -20,12 +20,6 @@ TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Serie
 **With less than 1 Million parameters, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** 
 
 
-TTM-R1 comprises TTM variants pre-trained on 250M public training samples. We have another set of TTM models released under TTM-R2 trained on a much larger pretraining
-dataset (~700M samples) which can be accessed from [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2). In general, TTM-R2 models perform better than 
-TTM-R1 models as they are 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.
-
-
 TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight 
 forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be 
 fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive.  Refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) for more details.
@@ -37,6 +31,10 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
 **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
 
 
+TTM-R1 comprises TTM variants pre-trained on 250M public training samples. We have another set of TTM models released under TTM-R2 trained on a much larger pretraining
+dataset (~700M samples) which can be accessed from [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2). In general, TTM-R2 models perform better than 
+TTM-R1 models as they are 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 Releases (along with the branch name where the models are stored):