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- # TriP-LLM
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-
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- This is the official checkpoints release for the **TriP-LLM**, a novel framework for unsupervised anomaly detection in multivariate time-series data using pretrained Large Language Models (LLMs).
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-
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- ## Model Description
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- - **Name**: TriP-LLM
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- - **Task**: Time-Series Anomaly Detection
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- - **Framework**: PyTorch
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- - **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM)
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-
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-
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- ## Usage
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-
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- Please refer to our [GitHub repository](https://github.com/YYZStart/TriP-LLM)
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- for model definitions, training code, and usage examples.
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-
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-
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- ## 📎 Citation
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-
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- If you find this repository useful for your research, please cite our paper:
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-
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- ```bibtex
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- @misc{TriPLLM,
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- title={TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection},
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- author={Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin},
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- year={2025},
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- eprint={2508.00047},
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- archivePrefix={arXiv},
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- primaryClass={cs.LG},
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- url={https://arxiv.org/abs/2508.00047},
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- }
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  ```
 
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+ # TriP-LLM
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+
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+ This is the official checkpoints release for the **TriP-LLM**, a novel framework for unsupervised anomaly detection in multivariate time-series data using pretrained Large Language Models (LLMs).
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+
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+ ## Model Description
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+ - **Name**: TriP-LLM
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+ - **Task**: Time-Series Anomaly Detection
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+ - **Framework**: PyTorch
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+ - **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM)
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+
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+
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+ ## Usage
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+
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+ Please refer to our [GitHub repository](https://github.com/YYZStart/TriP-LLM)
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+ for model definitions, training code, and usage examples.
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+
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+
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+ ## 📎 Citation
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+
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+ If you find this repository useful for your research, please cite our paper:
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+
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+ ```bibtex
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+ @misc{TriPLLM,
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+ title={TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection},
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+ author={Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin},
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+ journal={IEEE Access},
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+ year={2025},
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+ pages={168643-168653}
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+ }
 
 
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  ```