Update README.md

README.md

@@ -56,18 +56,18 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
 
 ## Model Capabilities with example scripts
 
-The below model scripts can be used for any TTM models. Please update the HF model URL and branch name in the `from_pretrained` call appropriately to pick the model of your choice.
+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.
 
-- Getting Started [colab](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) 
-- Zeroshot Multivariate Forecasting [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_getting_started.ipynb)
+- Getting Started [[colab]](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) 
+- Zeroshot Multivariate Forecasting [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_getting_started.ipynb)
 - Finetuned Multivariate Forecasting:
-  - Channel-Independent Finetuning [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_getting_started.ipynb) [Example: M4-Hourly finetuning](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
-  - Channel-Mix Finetuning [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_channel_mix_finetuning.ipynb)
+  - Channel-Independent Finetuning [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_getting_started.ipynb) [M4-Hourly finetuning](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
+  - Channel-Mix Finetuning [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_channel_mix_finetuning.ipynb)
 - **New Releases (extended features released on October 2024)**
-  - Finetuning and Forecasting with Exogenous/Control Variables [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_with_exog_tutorial.ipynb)
+  - Finetuning and Forecasting with Exogenous/Control Variables [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_with_exog_tutorial.ipynb)
   - Finetuning and Forecasting with static categorical features [Example: To be added soon]
-  - Rolling Forecasts - Extend forecast lengths beyond 96 via rolling capability [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_rolling_prediction_getting_started.ipynb)
-  - Helper scripts for optimal Learning Rate suggestions for Finetuning [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_with_exog_tutorial.ipynb)
+  - Rolling Forecasts - Extend forecast lengths beyond 96 via rolling capability [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_rolling_prediction_getting_started.ipynb)
+  - Helper scripts for optimal Learning Rate suggestions for Finetuning [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/tutorial/ttm_with_exog_tutorial.ipynb)
     
 ## Benchmarks