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Model2Vec is a technique to distill large sentence transformer models into highly efficient static embedding models. This process significantly reduces model size and computational requirements for inference. For a detailed understanding of how Model2Vec works, including the distillation process and model training, please refer to the [main Model2Vec Python repository](https://github.com/MinishLab/model2vec) and its [documentation](https://github.com/MinishLab/model2vec/blob/main/docs/what_is_model2vec.md).
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