Summary of Matmul or No Matmul in the Era Of 1-bit Llms, by Jinendra Malekar et al.
Matmul or No Matmul in the Era of 1-bit LLMs
by Jinendra Malekar, Mohammed E. Elbtity, Ramtin Zand
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The abstract presents an adaptation of Amdahl’s Law tailored for large language models (LLMs) with extreme quantization. The goal is to understand the actual improvements in computation and memory usage that these models can deliver, rather than just applying extreme quantization to a few layers. The authors find key nuances across different model architectures and hardware configurations through extensive experiments, providing a roadmap for future research in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make language models work better on devices with limited memory and processing power. It’s called “1-bit large language models” because the numbers used in the calculations are very simple (either 0 or 1). Right now, these models only help a little bit by making some parts of the calculation simpler. The authors want to know how much these models can actually speed up things and use less memory. They did lots of tests with different types of models and computers to figure out what works best. |
Keywords
» Artificial intelligence » Quantization
