From cb578f30c52797ffe335b0de0e6c0ca6432ad97f Mon Sep 17 00:00:00 2001 From: misha-chertushkin Date: Mon, 17 Feb 2025 12:00:20 +0000 Subject: [PATCH] Update readme --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 59e0e4e..c29d978 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,10 @@ We recommend at least 32GB RAM to load TimesFM dependencies. - Launched [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb) that lets you finetune the weights of the pretrained TimesFM model on your own data. - Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors. More details [here](https://github.com/google-research/timesfm?tab=readme-ov-file#covariates-support). +## Update - Feb. 17, 2024 +- We are providing the option for [finetuning using Pytorch](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning_torch.ipynb), which mimics the previously added functionality from [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb). +- We are also providing the Multi-GPU finetuining with Pytorch. We currently support DDP multi-gpu finetuning, other variants of multi-gpu training (pipeline parallelism/model parallelism) might be added later. In order to use it, follow the steps in [finetuning example](https://github.com/google-research/timesfm/blob/master/finetuning/finetuning_example.py) . + ## Checkpoint timesfm-1.0-200m (-pytorch) timesfm-1.0-200m is our first open model checkpoint: