Summary of A Survey Of Low-bit Large Language Models: Basics, Systems, and Algorithms, by Ruihao Gong et al.
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
by Ruihao Gong, Yifu Ding, Zining Wang, Chengtao Lv, Xingyu Zheng, Jinyang Du, Haotong Qin, Jinyang Guo, Michele Magno, Xianglong Liu
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 a comprehensive survey on low-bit quantization methods tailored for large language models (LLMs), covering fundamental principles, system implementations, and algorithmic strategies. The authors introduce basic concepts and new data formats specific to low-bit LLMs, followed by a review of frameworks and systems that facilitate low-bit LLMs across various hardware platforms. The paper categorizes and analyzes techniques and toolkits for efficient low-bit training and inference of LLMs. It concludes with a discussion on future trends and potential advancements of low-bit LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made tremendous progress in natural language processing, but they require significant memory and computational resources to deploy practically. To address this issue, researchers have proposed low-bit quantization methods that reduce the bit-width of model parameters, activations, and gradients, making them more efficient. This paper provides an overview of these methods, including their principles, implementations, and strategies for large language models. |
Keywords
» Artificial intelligence » Inference » Natural language processing » Quantization
