Update README.md

README.md

@@ -57,17 +57,17 @@ getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdem
 
 
 
-- **512-96-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
+- **512-96-ft-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
     in future. (branch name: main)
   
-- **1024-96-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
-    in future.  (branch name: 1024-96-r2) [[Benchmarks]]
+- **1024-96-ft-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
+    in future.  (branch name: 1024-96-ft-r2) [[Benchmarks]]
 
-- **1536-96-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
-    in future. (branch name: 1536-96-r2)
+- **1536-96-ft-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
+    in future. (branch name: 1536-96-ft-r2)
 
-- Likewise, we have models released for forecast lengths up to 720 timepoints. The branch names for these are as follows: `512-192-r2`, `1024-192-r2`, `1536-192-r2`, `512-336-r2`,
-  `512-336-r2`, `1024-336-r2`, `1536-336-r2`, `512-720-r2`, `1024-720-r2`, `1536-720-r2`
+- Likewise, we have models released for forecast lengths up to 720 timepoints. The branch names for these are as follows: `512-192-ft-r2`, `1024-192-ft-r2`, `1536-192-ft-r2`, `512-336-r2`,
+  `512-336-ft-r2`, `1024-336-ft-r2`, `1536-336-ft-r2`, `512-720-ft-r2`, `1024-720-ft-r2`, `1536-720-ft-r2`
 
 - Please use the [[get_model]](https://github.com/ibm-granite/granite-tsfm/blob/main/tsfm_public/toolkit/get_model.py) utility to automatically select the required model based on your input context length and forecast length requirement.
 
@@ -79,6 +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.
 - 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:
@@ -107,7 +108,7 @@ Please note that the Granite TTM models are pre-trained exclusively on datasets
 with clear commercial-use licenses that are approved by our legal team. As a result, the pre-training dataset used in this release differs slightly from the one used in the research
 paper, which may lead to minor variations in model performance as compared to the published results. Please refer to our paper for more details.
 
-**Benchmarking Scripts: [here](https://github.com/ibm-granite/granite-tsfm/tree/main/notebooks/hfdemo/tinytimemixer/full_benchmarking)**
+**Benchmarking Scripts: [here](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/full_benchmarking/research-use-r2.sh)**
 
 
 ## Recommended Use