Update readme

README.md

@@ -12,7 +12,7 @@ tags:
 
 # Moirai-2.0-R-Small
 
-Moirai 2.0 is a decoder-only universal time series forecasting transformer Model pre-trained on:
+Moirai 2.0 is a decoder-only universal time series forecasting transformer model pre-trained on:
 - Subset of [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain), and [Train](https://huggingface.co/datasets/Salesforce/GiftEval) datasets (Non-leaking historical context). 
 - Mixup data generated from non-leaking subsets of [Chronos Dataset](https://arxiv.org/abs/2403.07815).
 - Synthetic time series produced via KernelSynth introduced in [Chronos paper](https://arxiv.org/abs/2403.07815).
@@ -52,19 +52,9 @@ touch .env
 
 A simple notebook to get started: [github_notebook_link](https://github.com/SalesforceAIResearch/uni2ts/blob/main/example/moirai_forecast.ipynb)
 
-
-## The Moirai Family
-
-| # Model | # Parameters |
-| :---: | :---: |
-| [Moirai-2.0-R-Small](https://huggingface.co/Salesforce/moirai-1.0-R-small) | 11m |
-| [Moirai-1.0-R-Small](https://huggingface.co/Salesforce/moirai-1.0-R-small) | 14m |
-| [Moirai-1.0-R-Base](https://huggingface.co/Salesforce/moirai-1.0-R-base) | 91m |
-| [Moirai-1.0-R-Large](https://huggingface.co/Salesforce/moirai-1.0-R-large) | 311m |
-
 ## Citation
 
-If you're using Uni2TS in your research or applications, please cite it using this BibTeX:
+If you're using any Moirai model or Uni2TS in your research or applications, please cite it using this BibTeX:
 
 ```markdown
 @article{woo2024unified,