Summary of Bi-mamba: Towards Accurate 1-bit State Space Models, by Shengkun Tang and Liqun Ma and Haonan Li and Mingjie Sun and Zhiqiang Shen
Bi-Mamba: Towards Accurate 1-Bit State Space Models
by Shengkun Tang, Liqun Ma, Haonan Li, Mingjie Sun, Zhiqiang Shen
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces Bi-Mamba, a scalable and powerful 1-bit architecture designed for large language models with reduced memory footprint and energy consumption. Compared to traditional Transformers, Bi-Mamba addresses limitations such as quadratic computational complexity and significant inference-time memory requirements. The model is trained from scratch on data using an autoregressive distillation loss and achieves comparable performance to full-precision counterparts while reducing memory usage and energy consumption. Experimental results demonstrate the effectiveness of Bi-Mamba in language modeling tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bi-Mamba is a new way to make large language models work more efficiently. It’s like a superpower for computers! Right now, big language models use a lot of energy and take up lots of space on our devices. This new model helps fix that problem by using less memory and energy while still being really good at understanding and generating text. Scientists trained this model from scratch and tested it to make sure it works well with different sizes and types of data. The results are very promising! |
Keywords
» Artificial intelligence » Autoregressive » Distillation » Inference » Precision
