Merge pull request #101 from ibm-granite/services_fixes

Services fixes

README.md

@@ -1,8 +1,8 @@
 # TSFM: Time Series Foundation Models
-Public notebooks and utilities for working with Time Series Foundation Models (TSFM)
+Public notebooks, utilities, and serving components for working with Time Series Foundation Models (TSFM).
 
 The core TSFM time series models have been made available on Hugging Face -- details can be found 
-[here](wiki.md).
+[here](wiki.md). Information on the services component can be found [here](services/inference/README.md).
 
 
 # Python Version
@@ -27,14 +27,14 @@ pip install ".[notebooks]"
 - Getting started with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb)
 - Transfer learning with `PatchTSMixer` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_transfer.ipynb)
 - Transfer learning with `PatchTST` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/patch_tst_transfer.ipynb)
-- Getting started with `TinyTimeMixer (TTM)` [Try it out](notebooks/hfdemo/ttm_getting_started.ipynb)
+- Getting started with `TinyTimeMixer (TTM)` [[Try it out]](https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb)
 
 ## 📗 Google Colab
 Run the TTM tutorial in Google Colab, and quickly build a forecasting application with pre-trained TSFM models.
-- [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/tutorial/notebooks/tutorial/ttm_tutorial.ipynb) 
+- [TTM Colab Tutorial](https://colab.research.google.com/github/IBM/tsfm/blob/main/notebooks/tutorial/ttm_tutorial.ipynb) 
 
 ## 💻 Demos Installation
-The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at later date). To install the requirements use `pip`:
+The demo presented at NeurIPS 2023 is available in `tsfmhfdemos`. This demo requires you to have pre-trained and finetuned models in place (we plan to release these at a later date). To install the requirements use `pip`:
 
 ```bash
 pip install ".[demos]"
@@ -46,7 +46,7 @@ Before opening a new issue, please search for similar issues. It's possible that
 
 
 # Notice
-The intention of this repository is to make it easier to use and demonstrate IBM Research TSFM components that have been made available in the [Hugging Face transformers library](https://huggingface.co/docs/transformers/main/en/index). As we continue to develop these capabilities we will update the code here.
+The intention of this repository is to make it easier to use and demonstrate Granite TimeSeries components that have been made available in the [Hugging Face transformers library](https://huggingface.co/docs/transformers/main/en/index). As we continue to develop these capabilities we will update the code here.
 
 
 IBM Public Repository Disclosure: All content in this repository including code has been provided by IBM under the associated open source software license and IBM is under no obligation to provide enhancements, updates, or support. IBM developers produced this code as an open source project (not as an IBM product), and IBM makes no assertions as to the level of quality nor security, and will not be maintaining this code going forward.

services/inference/README.md

@@ -1,7 +1,8 @@
 # TSFM Services
 
-This component provides basic RESTful services for the IBM tsfm-granite
-class of timeseries foundation models. At present it can serve the following models:
+
+
+This component provides RESTful services for the  tsfm-granite class of timeseries foundation models. At present it can serve the following models:
 
 * https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1
 * https://huggingface.co/ibm-granite/granite-timeseries-patchtst