Summary of Mamba-ptq: Outlier Channels in Recurrent Large Language Models, by Alessandro Pierro et al.
Mamba-PTQ: Outlier Channels in Recurrent Large Language Models
by Alessandro Pierro, Steven Abreu
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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 In this paper, researchers explore the potential of modern recurrent layers to deploy foundation models at the edge, particularly in the context of large language models (LLMs). They demonstrate that compressing input sequences enables recurrent layers to model long-range dependencies while maintaining a constant inference cost and fixed memory requirement. However, they also recognize that further model compression techniques like quantization and pruning are often required for practical deployment in resource-limited environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make big language models work on smaller devices. It shows that special types of layers called recurrent layers can be used to analyze long sequences of text while still being efficient. However, the team also recognizes that even these efficient layers often need to be made even smaller and lighter so they can run on devices with limited resources. |
Keywords
* Artificial intelligence * Inference * Model compression * Pruning * Quantization
