Summary of Scaling Laws For Post Training Quantized Large Language Models, by Zifei Xu et al.
Scaling Laws for Post Training Quantized Large Language Models
by Zifei Xu, Alexander Lan, Wanzin Yazar, Tristan Webb, Sayeh Sharify, Xin Wang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 discusses the predictability of large language models’ (LLMs) generalization abilities as they scale with model size. Unlike pre-training, where practical scaling laws apply, post-training compression of LLMs remains unpredictable and requires case-by-case validation. To address this gap, the authors conducted an empirical study on multiple LLM families quantized to various low-precision tensor data types using popular weight quantization techniques. The research identified key scaling factors related to local loss landscape characteristics that can reasonably predict the performance of quantized LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that get better at understanding and generating text as they become larger. But when we shrink them down to make them smaller, it’s hard to know how well they’ll work. The authors of this paper tried to figure out why this is the case by studying many different types of large language models that were made smaller using special techniques. They found some important clues about what makes these models work or not work when they’re shrunk down. |
Keywords
» Artificial intelligence » Generalization » Precision » Quantization » Scaling laws
