Researchers from Princeton University and Carnegie Mellon University have introduced Mamba-2, a new state-space model (SSM) that advances the theory of sequence models through the State Space Duality (SSD) framework. Mamba-2, developed by Tri Dao and Albert Gu, outperforms its predecessor, Mamba, and Transformer++ in both perplexity and wall-clock time. The model features 8x larger states and 50% faster training, making it a significant improvement over previous versions. The SSD framework reveals theoretical connections between SSMs and linear attention mechanisms, suggesting that many linear attention variants and SSMs are equivalent. This breakthrough has potential implications for efficient language modeling, generalized models, efficient algorithms, and downstream learning tasks.
STARTING NOW! List of papers we'll be covering: Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (special appearance by authors)… https://t.co/FCJzxemzhi
Beyond Quadratic Bottlenecks: Mamba-2 and the State Space Duality Framework for Efficient Language Modeling https://t.co/P0lOuUBlBc #LanguageModeling #Mamba2 #SSDframework #AIbusinesssolutions #AIsalesbot #ai #news #llm #ml #research #ainews #innovation #artificialintelligenc… https://t.co/8ZjgGcIfrb
Mamba got a sequel: Mamba-2 Six months ago, the Mamba research team Tri Dao and Albert Gu introduced their new model architecture. The community loved it. It has been examined on recall abilities, in-context learning, and formal language expressivity. So what's new? 🧵 https://t.co/9AhOfshdTF
Mamba-2 and State Space Models https://t.co/4BZ95eBBhb
Beyond Quadratic Bottlenecks: Mamba-2 and the State Space Duality Framework for Efficient Language Modeling Researchers from Princeton University and Carnegie Mellon University have introduced the State Space Duality (SSD) framework, which connects SSMs and attention mechanisms.… https://t.co/ccf5FRfxjz
Mamba State-Space Models Can Be Strong Downstream Learners. https://t.co/pzXO7ZjMZd
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality https://t.co/4qDhFboeLY
mamba-2 is here 👀 if you want to work on bleeding edge ssms with a world class research team led by @_albertgu, we're hiring @cartesia_ai https://t.co/bJlwCsnTAH
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
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality Tri Dao, Albert Gu https://t.co/cjgo9q65UN
[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