Summary of Blackmamba: Mixture Of Experts For State-space Models, by Quentin Anthony et al.
BlackMamba: Mixture of Experts for State-Space Models
by Quentin Anthony, Yury Tokpanov, Paolo Glorioso, Beren Millidge
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents BlackMamba, a novel architecture that combines the State-space model (SSM) Mamba with the mixture-of-expert (MoE) model to achieve competitive performance in language modeling and long sequence processing tasks. The SSM-based approach offers linear time and memory complexity as a function of sequence length, while MoE models reduce compute and latency costs at the expense of increased memory usage. BlackMamba outperforms transformer baselines in both inference and training FLOPs and achieves competitive performance with Mamba. The paper releases open-source 340M/1.5B and 630M/2.8B BlackMamba models trained on a custom dataset, showcasing the benefits of combining SSM and MoE architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BlackMamba is a new way to make computers understand language better. It combines two ideas: State-space models that are fast and use little memory, and mixture-of-expert models that can do lots of things quickly but need more memory. This combination works well and helps computers learn from big datasets. The team behind BlackMamba tested it and found that it’s as good or even better than other methods that are popular now. They also shared all the details so others can use this new method to improve language understanding. |
Keywords
* Artificial intelligence * Inference * Language understanding * Transformer
