Update README.md

README.md

@@ -41,11 +41,11 @@ try both R1 and R2 variants and pick the best for your data.
 
 - **512-96:** 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 targeted towards a forecasting setting of context length 512 and forecast length 96 and
-  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). This model refers to the TTM-Q variant used in the paper. (branch name: main) [[Benchmark Scripts]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_512_96.ipynb)
+  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). This model refers to the TTM-Q variant used in the paper. (branch name: main) [[Benchmark Scripts]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r1_benchmarking_512_96.ipynb)
 
 - **1024-96:** 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 targeted towards a long forecasting setting of context length 1024 and forecast length 96 and
-  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-v1) [[Benchmark Scripts]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
+  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-v1) [[Benchmark Scripts]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r1_benchmarking_1024_96.ipynb)
 
 
     
@@ -54,17 +54,17 @@ try both R1 and R2 variants and pick the best for your data.
 
 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-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:
-  - 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)
+  - Channel-Independent Finetuning [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) [Finetuning](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
+  - Channel-Mix Finetuning [[Example]](https://github.com/ibm-granite/granite-tsfm/blob/main/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/main/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/main/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/main/notebooks/tutorial/ttm_with_exog_tutorial.ipynb)
+ 
 ## Benchmarks
 
 TTM outperforms popular benchmarks such as TimesFM, Moirai, Chronos, Lag-Llama, Moment, GPT4TS, TimeLLM, LLMTime in zero/fewshot forecasting while reducing computational requirements significantly.