Update README.md

README.md

@@ -10,19 +10,23 @@ tags:
 - time-series
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-# TinyTimeMixer (TTM) R2 Model Card
+# Granite-TimeSeries-TTM-R2 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 model sizes starting from 1M params, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** 
 
 
 TTM is accepted in NeurIPS 2024. 
 
-**TTM-R2 comprises TTM variants pre-trained on larger pretraining datasets.**
+**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
+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