MambaTransformer and HSSS
These are ecosystem siblings where MambaTransformer represents a hybrid architectural approach that combines two sequence modeling paradigms, while HSSS provides a specialized hierarchical variant of the pure state-space model approach that MambaTransformer partially incorporates.
About MambaTransformer
kyegomez/MambaTransformer
Integrating Mamba/SSMs with Transformer for Enhanced Long Context and High-Quality Sequence Modeling
This project helps AI developers build advanced language models that can understand and generate very long sequences of text more effectively. It takes in raw text or tokenized sequences and outputs predictions or generated text, suitable for tasks requiring deep understanding of extensive content. Developers working on sophisticated natural language processing applications will find this useful.
About HSSS
kyegomez/HSSS
Implementation of a Hierarchical Mamba as described in the paper: "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling"
This project offers an implementation of a Hierarchical Mamba architecture, which is a type of neural network. It takes a single sequence of input data, processes it through multiple levels of state space models, and produces multiple output sequences. This is designed for machine learning researchers and engineers who are experimenting with advanced sequence-to-sequence modeling techniques.
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