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Large time-series model introduced in this [paper](https://arxiv.org/abs/2402.02368) and enhanced with our [further work](https://arxiv.org/abs/2410.04803).
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This version is pre-trained on **
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We evaluate the model on the following benchmarks: [TSLib Dataset](), [GIFT-Eval]().
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# Quickstart
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pip install transformers==4.40.1 #
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```
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## Specification
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* Architecture: Causal Transformer (Decoder-only)
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* Pre-training Scale:
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* Context Length: up to 2880
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* Parameter Count: 84M
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* Patch Length: 96
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Large time-series model introduced in this [paper](https://arxiv.org/abs/2402.02368) and enhanced with our [further work](https://arxiv.org/abs/2410.04803).
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This version is pre-trained on **260B** time points with **84M** parameters, a lightweight generative Transformer with the state-of-the-art performance on zero-shot forecasting:
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We evaluate the model on the following benchmarks: [TSLib Dataset](), [GIFT-Eval]().
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# Quickstart
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```
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pip install transformers==4.40.1 # use this version for the stable compatibility
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```
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```
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## Specification
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* Architecture: Causal Transformer (Decoder-only)
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* Pre-training Scale: 260B time points
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* Context Length: up to 2880
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* Parameter Count: 84M
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* Patch Length: 96
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