granite-timeseries-ttm-r1

# Load Model from HF Model Hub mentioning the branch name in revision field

model = TinyTimeMixerForPrediction.from_pretrained(
                "https://huggingface.co/ibm/TTM", revision="main"
            ) 

# Do zeroshot
zeroshot_trainer = Trainer(
        model=model,
        args=zeroshot_forecast_args,
        )
    )

zeroshot_output = zeroshot_trainer.evaluate(dset_test)


# Freeze backbone and enable few-shot or finetuning:

# freeze backbone
for param in model.backbone.parameters():
  param.requires_grad = False

finetune_forecast_trainer = Trainer(
        model=model,
        args=finetune_forecast_args,
        train_dataset=dset_train,
        eval_dataset=dset_val,
        callbacks=[early_stopping_callback, tracking_callback],
        optimizers=(optimizer, scheduler),
    )
finetune_forecast_trainer.train()
fewshot_output = finetune_forecast_trainer.evaluate(dset_test)