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Hey @Syed Umair Hassan, Thanks for using the library. Here is a tutorial on transfer learning with NeuralForecast: https://nixtla.github.io/neuralforecast/examples/transfer_learning.html

Sorry, I typed my question wrong, I have seen this. What I wanted to know if is their a way to unfreeze some of the layers and tune them to a given dataset?

We have not performed fine tune to the fullest extent, where we freeze the encoder and tune the last decoder layers. For the moment, like in the tutorial, we tune the whole network. If you are interested in the fine-tune approach here is an example: https://wandb.ai/wandb/wandb-lightning/reports/Transfer-Learning-Using-PyTorch-Lightning--VmlldzoyODk2MjA I think it should be easy to implement something similar on the NeuralForecast models too