Update README.md

README.md

@@ -10,17 +10,20 @@ tags:
 - time-series
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-# TinyTimeMixer (TTM) Model Card
+# Granite-TimeSeries-TTM-R1 Model Card
 
 <p align="center" width="100%">
 <img src="ttm_image.webp" width="600">
 </p>
 
 TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. 
-**With less than 1 Million parameters, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** 
+**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 is accepted in NeurIPS 2024. 
+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