Update README.md

README.md

@@ -32,8 +32,8 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
 
 
 **TTM-R2 comprises TTM variants pre-trained on larger pretraining datasets (~700M samples).** We have another set of TTM models released under `TTM-R1` trained on ~250M samples 
-which can be accessed from [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1) 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
+which can be accessed from [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1). In general, `TTM-R2` models perform better than `TTM-R1` models as they are 
+trained on larger pretraining dataset. In standard benchmarks, TTM-R2 outperform TTM-R1 by over 15%.  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.