The AI community is abuzz with the introduction of Mamba, an innovative selective state space model (SSM) that addresses limitations of both Transformers and traditional SSMs. Mamba's architecture integrates selective SSMs into a neural network, removing attention and MLP blocks. The model aims to improve sequence modeling by overcoming drawbacks associated with transformers. Additionally, Mamba-2, an advanced version of Mamba, has been released. Mamba-2 features 8x larger states, 50% faster training, and outperforms both Mamba and Transformer++ in perplexity and wall-clock time. This development is part of a broader theoretical framework called state space duality, which shows connections between SSMs and linear attention. The advancements are significant for fields like LLMs, DeepLearning, and DataScience.
excited to finally release Mamba-2!! 8x larger states, 50% faster training, and even more S's 🐍🐍 Mamba-2 aims to advance the theory of sequence models, developing a framework of connections between SSMs and (linear) attention that we call state space duality (SSD) w/@tri_dao https://t.co/xbNMzMeYL8
With @_albertgu, we’ve built a rich theoretical framework of state-space duality, showing that many linear attn variants and SSMs are equivalent! The resulting model, Mamba-2 is better & faster than Mamba-1, and still matching strong Transformer arch on language modeling. 1/ https://t.co/mqDwiYeSAl
[LG] Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality T Dao, A Gu [Princeton University & CMU] (2024) https://t.co/vkSNhzVgeq - This paper shows theoretical connections between structured state space models (SSMs),… https://t.co/1BwiroVH3l
Transformers are SSMs Generalized Models and Efficient Algorithms Through Structured State Space Duality While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown https://t.co/9400BttLNR
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Presents Mamba-2, which outperforms Mamba and Transformer++ in both perplexity and wall-clock time https://t.co/Sd3J3kPG5W https://t.co/C2nAisXcoN
Introducing Mamba, an innovative selective state space model that addresses the limitations of both Transformers and traditional SSMs. The Mamba architecture integrates selective SSMs into a neural network, removing attention and MLP blocks. Let's explore its key features! 👇 https://t.co/gb3FPTv4j1
Beyond Transformers: Structured State Space Sequence Models https://t.co/EP2TUAdv5Y #AI #MachineLearning #DeepLearning #LLMs #DataScience https://t.co/U2XeVAlqJD
We continue our AI 101 series with Mamba! An innovative selective state space model. Let's explore its origins, the issues it addresses, and why it might be a better alternative to transformer models 👀 https://t.co/ohsS3hhwJS
2nd topic of our new AI 101 series is Mamba selective space state model! In our article we discuss: - sequence modeling - the drawbacks of transformers - why Mamba may be a better alternative to transformer models Check it out👇 https://t.co/WJWyBs97yi