Update README.md

README.md

@@ -46,10 +46,8 @@ we opt for the approach of constructing smaller pre-trained models, each focusin
 yielding more accurate results. Furthermore, this approach ensures that our models remain extremely small and exceptionally fast, 
 facilitating easy deployment without demanding a ton of resources. 
 
-Hence, in this model card, we plan to release several pre-trained 
-TTMs that can cater to many common forecasting settings in practice. Additionally, we have released our source code along with 
-our pretraining scripts that users can utilize to pretrain models on their own. Pretraining TTMs is very easy and fast, taking 
-3-6 hours for R1 versions and 12-24 hours for R2 versions using 6 A100 GPUs, as opposed to several days or weeks in traditional approaches.
+Hence, in this model card, we release several pre-trained 
+TTMs that can cater to many common forecasting settings in practice. 
 
 Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our 
 getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) mentioning the branch name.
@@ -57,59 +55,22 @@ getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdem
 
 ## Model Releases (along with the branch name where the models are stored):
 
-
 - **512-96-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: main) [[Benchmarks]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r2_benchmarking_512_96.ipynb)
+    in future. (branch name: main)
   
 - **1024-96-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1024-96-r2) [[Benchmarks]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r2_benchmarking_1024_96.ipynb)
- 
+    in future.  (branch name: 1024-96-r2) [[Benchmarks]]
 
 - **1536-96-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1536-96-r2) [[Benchmarks]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r2_benchmarking_1536_96.ipynb)
-
-
-- **512-192-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to next 192 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 512-192-r2) 
-  
-- **1024-192-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 192 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1024-192-r2) 
-
-- **1536-192-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to next 192 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1536-192-r2)
+    in future. (branch name: 1536-96-r2)
 
-  
-- **512-336-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to next 336 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 512-336-r2) 
-  
-- **1024-336-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 336 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1024-336-r2) 
-
-- **1536-336-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to next 336 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1536-336-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
 
+- 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.
 
   
-- **512-720-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to next 720 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 512-720-r2) 
-  
-- **1024-720-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 720 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1024-720-r2) 
 
-- **1536-720-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to next 720 time-points (i.e. forecast length)
-    in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
-    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 1536-720-r2) 
     
 
 ## Model Capabilities with example scripts
@@ -140,6 +101,8 @@ 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)**
+
 ## Recommended Use
 1. Users have to externally standard scale their data independently for every channel before feeding it to the model (Refer to [TSP](https://github.com/IBM/tsfm/blob/main/tsfm_public/toolkit/time_series_preprocessor.py), our data processing utility for data scaling.)
 2. The current open-source version supports only minutely and hourly resolutions(Ex. 10 min, 15 min, 1 hour.). Other lower resolutions (say weekly, or monthly) are currently not supported in this version, as the model needs a minimum context length of 512 or 1024.