A new paper titled 'Mamba: Linear-Time Sequence Modeling with Selective State Spaces' introduces Mamba, a sequence model backbone that outperforms Transformers in language modeling, audio, and genomics. Mamba offers fast inference and 5× higher throughput than Transformers, with linear scaling in sequence length. The model challenges the dominant Transformer architecture and explores design space outside of PyTorch, requiring hands-on exploration to comprehend its potential.
Mamba - Incredible alternative to Transformers architecture - Paper released yesterday "Mamba: Linear-Time Sequence Modeling with Selective State Spaces" 🔥 🔥 5× higher throughput than Transformers 🔥 Linear scaling in sequence length, and 🔥 Performance improves on real… https://t.co/jVR4yut91M https://t.co/4Etv5KrbcL
Extremely Cool! Mamba, announced today, is a structured state space model that challenges the dominant Transformer architecture. Transformers are computationally inefficient, esp. when it comes to extended contexts. With some clever improvements, Mamba enjoys fast inference (5×… https://t.co/3veUFs0UGD https://t.co/Cf8dvZCjZh
[LG] Mamba: Linear-Time Sequence Modeling with Selective State Spaces https://t.co/6jOVEdOYI3 https://t.co/pvYaCteHCq
Mamba: Linear-Time Sequence Modeling with Selective State Spaces "As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics. On language modeling, our Mamba-3B model outperforms Transformers… https://t.co/e414WX8PoG
What's neat about the Mamba paper is that they're really exploring the design space outside of PyTorch. Like this model makes no sense if you aren't willing to get your hands dirty and prove it. https://t.co/72aPFfRxYm https://t.co/1RllozOAyu
What's neat about the Mamba paper is that they're really exploring the design space outside of PyTorch. Like none of this makes sense if you aren't willing to get your hands dirty. https://t.co/72aPFfRxYm
Mamba: Linear-Time Sequence Modeling with Selective State Spaces paper page: https://t.co/IIbOYoJRtR Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.… https://t.co/cAArkhTVgD https://t.co/nAxJHED8BM