tommyip/mamba2-minimal

This project offers a highly efficient way to build language models for tasks like text generation or sequence processing, without the computational overhead of traditional Transformer models. It takes in sequential data (like text or time series) and processes it into output logits, enabling rapid training and constant-time inference, especially useful for very long sequences. It's designed for machine learning practitioners and researchers working with sequential data.

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