Summary of An Empirical Study Of Mamba-based Language Models, by Roger Waleffe et al.
An Empirical Study of Mamba-based Language Models
by Roger Waleffe, Wonmin Byeon, Duncan Riach, Brandon Norick, Vijay Korthikanti, Tri Dao, Albert Gu, Ali Hatamizadeh, Sudhakar Singh, Deepak Narayanan, Garvit Kulshreshtha, Vartika Singh, Jared Casper, Jan Kautz, Mohammad Shoeybi, Bryan Catanzaro
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 compares selective state-space models (SSMs) like Mamba with Transformers in a direct large-scale evaluation. SSMs, such as Mamba and Mamba-2, are attractive alternatives to Transformers due to their better computational complexity and memory requirements. The study trains 8B-parameter Mamba, Mamba-2, and Transformer models on the same datasets of up to 3.5T tokens and compares them using a diverse set of tasks. The results show that while pure SSMs match or exceed Transformers on many tasks, they lag behind on tasks requiring strong copying or in-context learning abilities. However, the hybrid architecture consisting of Mamba-2, attention, and MLP layers (Mamba-2-Hybrid) exceeds the 8B Transformer on all standard tasks evaluated and is predicted to be up to 8x faster at inference time. The study also evaluates variants of these models extended to support long sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research compares two types of artificial intelligence models, called Mamba and Transformers, to see which one works better. These models are used for language processing, like understanding what people mean when they say something. Some benefits of the Mamba model include being faster and using less memory than Transformers. The study trained these models on large amounts of text data and tested them on various tasks. While Mamba is good at many things, it struggles with certain tasks that require copying or learning from context. However, a new hybrid model that combines parts of Mamba and Transformer performs even better than the 8B Transformer and can generate text up to 8 times faster. |
Keywords
» Artificial intelligence » Attention » Inference » Transformer
