From 3a8265ebafc7efc2ee56c0ff98471d04de9d3b9a Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:14:37 -0400 Subject: [PATCH 1/3] update readme --- README.md | 6 +++--- services/inference/README.md | 5 +++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 06b3d32d..77e55d6b 100644 --- a/README.md +++ b/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 @@ -31,7 +31,7 @@ pip install ".[notebooks]" ## 📗 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`: diff --git a/services/inference/README.md b/services/inference/README.md index 3f1c422a..7c8b9e74 100644 --- a/services/inference/README.md +++ b/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 From 83f1e62b836382b04f2e46bca02d97d59259428e Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:32:06 -0400 Subject: [PATCH 2/3] minor update --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 77e55d6b..0cbad39d 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ 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. @@ -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. From b23f803769f130e38066f61c2dbdf0a23706f07b Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Fri, 2 Aug 2024 15:33:12 -0400 Subject: [PATCH 3/3] minor update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0cbad39d..b18c6e0c 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ Run the TTM tutorial in Google Colab, and quickly build a forecasting applicatio - [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]"