Update README.md

README.md

@@ -66,6 +66,8 @@ getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdem
   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-granite/granite-tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm-r1_benchmarking_1024_96.ipynb)
 
+We can also 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.
+For more variants (till forecast length 720), refer to our new model card [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2)
 
     
 
@@ -91,6 +93,11 @@ Moreover, TTMs are lightweight and can be executed even on CPU-only machines, en
 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. For other variants (TTM-B, TTM-E and TTM-A) please refer [here](https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2). For more details, refer to the paper.
 
+
+<p align="center" width="100%">
+<img src="benchmarks.webp" width="600">
+</p>
+
 ## 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.