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misha-chertushkin
2025-02-17 12:00:20 +00:00
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@@ -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: