Update README.md

README.md

@@ -46,12 +46,6 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
  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)
 
-- **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). This model refers to the TTM-B variant used in the paper (branch name: 512-96-r2) [[Benchmark Scripts]](https://github.com/ibm-granite/granite-tsfm/blob/ttm_v2_release/notebooks/hfdemo/tinytimemixer/ttm_v2_benchmarking_512_96.ipynb)
-
 
  
 
@@ -75,9 +69,7 @@ The below model scripts can be used for any of the above TTM models. Please upda
 TTM outperforms popular benchmarks such as TimesFM, Moirai, Chronos, Lag-Llama, Moment, GPT4TS, TimeLLM, LLMTime in zero/fewshot forecasting while reducing computational requirements significantly. 
 Moreover, TTMs are lightweight and can be executed even on CPU-only machines, enhancing usability and fostering wider
 adoption in resource-constrained environments. For more details, refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) TTM-Q referred in the paper maps to the `512-96` model
-uploaded in the main branch, and TTM-B referred in the paper maps to the `512-96-r2` model. 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.
+uploaded in the main branch. Please refer to our paper for more details.
 
 ## 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.)
@@ -188,10 +180,6 @@ The original r1 TTM models were trained on a collection of datasets from the Mon
  - Wind Farms Production data: https://zenodo.org/records/4654858 
  - Wind Power: https://zenodo.org/records/4656032
 
-In addition to the above datasets, the updated TTM model (512-96-r2) was trained on the following:
- - PEMSD3, PEMSD4, PEMSD7, PEMSD8, PEMS_BAY: https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe
- - LOS_LOOP: https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe
-
 
 ## Citation
 Kindly cite the following paper, if you intend to use our model or its associated architectures/approaches in your