Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Nine different sources on Mamba are reviewed, including the paper that introduced it.The provided sources explore Mamba, a linear recurrent neural network (RNN) architecture, and its integration with Transformers to create hybrid models for large language models (LLMs). A key focus is on Mamba's efficiency and long-context handling compared to Transformers' memory and computational demands due to their KV cache. While Transformers excel at in-context learning, pure Mamba models initially struggled, leading to the development of hybrid architectures like Jamba and Zamba that combine both for improved performance and efficiency. Discussions also touch upon distillation techniques to transfer Transformer capabilities to Mamba, the benefits of character-level tokenization for Mamba, and ongoing research into optimizing state updates and selectivity mechanisms in these next-generation sequence models.Sources:1) https://venturebeat.com/ai/falcon-mamba-7bs-powerful-new-ai-architecture-offers-alternative-to-transformer-models2) https://www.ai21.com/research/jamba-a-hybrid-transformer-mamba-language-model/3) https://nathanpaull.substack.com/p/mamba-will-never-beat-the-transformer-24-03-084) https://n1o.github.io/posts/ssm-transformer-hybrids-guide5) https://youtu.be/yceNl9C6Ir0?si=LTVLnBtTwiU5j1SK6) https://www.together.ai/blog/the-mamba-in-the-llama-distilling-and-accelerating-hybrid-models7) https://arxiv.org/pdf/2312.007528) https://www.reddit.com/r/MachineLearning/comments/18d65bz/d_thoughts_on_mamba/9) https://arxiv.org/pdf/2403.19887

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