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@@ -12,7 +12,6 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
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  **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
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- TTM Few-shot tuning is
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  ## Benchmark Highlights:
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@@ -56,9 +55,9 @@ For more details on TTM architecture and benchmarks, refer to our [paper](https:
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  TTM-1 currently supports 2 modes:
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- - Zeroshot forecasting: Directly apply the pre-trained model on your target data to get an initial forecast (with no training).
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- - Finetuned forecasting: Finetune the pre-trained model with your target data to further improve the forecast.
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  **Since, TTM models are extremely small and fast, it is practically very easy to finetune the model with your available target data in few minutes
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  to get more accurate forecasts.**
 
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  **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
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  ## Benchmark Highlights:
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  TTM-1 currently supports 2 modes:
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+ - **Zeroshot forecasting**: Directly apply the pre-trained model on your target data to get an initial forecast (with no training).
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+ - **Finetuned forecasting**: Finetune the pre-trained model with a subset of your target data to further improve the forecast.
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  **Since, TTM models are extremely small and fast, it is practically very easy to finetune the model with your available target data in few minutes
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  to get more accurate forecasts.**