Update README.md

README.md

@@ -79,7 +79,7 @@ but can provide any forecast lengths up to 720 in get_model() to get the require
 
 The below model scripts can be used for any of the above TTM models. Please update the HF model URL and branch name in the `from_pretrained` call appropriately to pick the model of your choice.
 
-Since these models use frequency prefix tuning, ensure your dataset yaml (as mentioned in the below notebooks) have frequency information and set `enable_prefix_tuning` to True in load_dataset.
+**Since these models use frequency prefix tuning, ensure your dataset yaml (as mentioned in the below notebooks) have frequency information and set `enable_prefix_tuning` to True in load_dataset.**
 - Getting Started [[colab]](https://colab.research.google.com/github/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) 
 - Zeroshot Multivariate Forecasting [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb)
 - Finetuned Multivariate Forecasting: