Selective State Spaces — Topic Summaries
AI-powered summaries of 4 videos about Selective State Spaces.
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Mamba vs. Transformers: The Future of LLMs? | Paper Overview & Google Colab Code & Mamba Chat
Mamba’s core pitch is a way to make large language models handle much longer inputs without paying Transformers’ usual attention cost. Transformers...
Mamba sequence model - part 1
Mamba’s core pitch is that sequence models can match Transformer-quality results on language and other modalities while scaling linearly with...
Mamba part 2 - Can it replace Transformers?
Mamba’s core pitch is simple: it aims to match—and in some settings surpass—Transformer-style language modeling while scaling linearly with sequence...
Mamba part 3 - Details of Mamba and Structured State Space
Mamba’s core pitch is that sequence modeling can be made both fast and selective without attention’s quadratic cost. The approach builds on state...