Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 - time-series
 ---
 
-# TinyTimeMixer (TTM) Model Card
+# TinyTimeMixer (TTM) 1M Model Card
 
 <p align="center" width="100%">
 <img src="ttm_image.webp" width="600">
@@ -21,7 +21,7 @@ TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Serie
 
 TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight 
 forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be 
-fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) for more details.
+fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive.  Refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) for more details.
 
 
 **The current open-source version supports point forecasting use-cases specifically ranging from minutely to hourly resolutions 
@@ -36,49 +36,33 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
 
 - **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).   (branch name: main) 
+  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: main) [Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_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) 
+  recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-v1) [Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
 
 - **New Releases (trained on larger pretraining datasets, released on October 2024)**:
 
   - **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: 512-96-r2)
+    resolutions (Ex. 10 min, 15 min, 1 hour, etc).   (branch name: 512-96-r2) [Benchmarks](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/tinytimemixer/ttm_v2_benchmarking_512_96.ipynb)
 
-  - **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)
- 
 
-  - **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)
     
 
 ## Model Capabilities with example scripts
-- Zeroshot Multivariate Forecasting 
+- 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
-  - Channel-Mix Finetuning
+  - Channel-Independent Finetuning [Example](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/ttm_getting_started.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
-  - Finetuning and Forecasting with static categorical features
-  - Rolling Forecasts - Extend forecast lengths beyond 96 via rolling capability
-  - 
+  - 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)
     
-  
-
-
-## How to Get Started with the Model
-
-Please refer to the below scrips for **zero-shot** and **finetuning** support:
-- [colab](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) 
-- [512-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_512_96.ipynb)
-- [1024-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
-- Script for Exogenous support - to be added soon
 
 
 ## Recommended Use
@@ -87,22 +71,12 @@ Please refer to the below scrips for **zero-shot** and **finetuning** support:
 3. Enabling any upsampling or prepending zeros to virtually increase the context length for shorter-length datasets is not recommended and will
    impact the model performance. 
 
-## Benchmark Highlights:
-
-- TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters [paper](https://arxiv.org/pdf/2401.03955v5.pdf):
-  - *GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting*
-  - *LLMTime (NeurIPS 23) by 24% in zero-shot forecasting*.
-  - *SimMTM (NeurIPS 23) by 17% in few-shot forecasting*.
-  - *Time-LLM (ICLR 24) by 2-8% in few-shot forecasting*
-  - *UniTime (WWW 24) by 27% in zero-shot forecasting.*
-- Zero-shot results of TTM surpass the *few-shot results of many popular SOTA approaches* including
-  PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
+## Other Benchmark Scripts:
 - TTM (1024-96, released in this model card with 1M parameters) outperforms pre-trained MOIRAI-Small (14M parameters) by 10%, MOIRAI-Base (91M parameters) by 2% and
   MOIRAI-Large (311M parameters) by 3% on zero-shot forecasting (horizon = 96). [[notebook]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
 - TTM quick fine-tuning also outperforms the competitive statistical baselines (Statistical ensemble and S-Naive) in
   M4-hourly dataset which existing pretrained TS models are finding difficult to outperform. [[notebook]](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_m4_hourly.ipynb)
-- TTM takes only a *few seconds for zeroshot/inference* and a *few minutes for finetuning* in 1 GPU machine, as
-  opposed to long timing-requirements and heavy computing infra needs of other existing pre-trained models.
+