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Time-MoE/Time-MoE ★ 999 116 APPLICATION

Time-MoE is a library/framework for training and deploying time series foundation models using a Mixture-of-Experts (MoE) architecture. It provides tools for training the TimeMoE model from scratch or fine-tuning pretrained versions, evaluating on benchmark datasets, and generating forecasts. The codebase is organized into modules for models, datasets, training utilities, and I/O operations, with a clear separation of concerns. Key features include efficient handling of large sequential datasets via sharded binary files, a custom MoE layer with load balancing loss, and integration with Hugging Face Transformers for model management and generation.

RATING: 7.9/10 MAINT: active DRIFT: low
#time-series-forecasting #foundation-model #mixture-of-experts #moe #transformer #pytorch #huggingface #efficient-data-loading #time-series #time-series-foundation-model #deep-learning #large-model #machine-learning
▸ 2 reusable components found
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