Summary of Learning From Students: Applying T-distributions to Explore Accurate and Efficient Formats For Llms, by Jordan Dotzel et al.
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
by Jordan Dotzel, Yuzong Chen, Bahaa Kotb, Sushma Prasad, Gang Wu, Sheng Li, Mohamed S. Abdelfattah, Zhiru Zhang
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents a study on the optimal format for large language models (LLMs) to improve accuracy while minimizing chip area. The authors conduct a large-scale analysis of LLM weights and activations, finding that most distributions follow a Student’s t-distribution. They then propose a new format, Student Float (SF4), which improves over Normal Float (NF4) across modern LLMs. Additionally, the authors introduce variants of supernormal support to augment E2M1 and increase model accuracy. The paper also explores the quality and efficiency frontier across 11 datatypes, discovering a Pareto curve that offers a continuous tradeoff between model accuracy and chip area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make big language models (LLMs) work better while using less power. They study what types of numbers LLMs use most often and find that most follow a certain pattern. Then, they create a new way to store these numbers called Student Float (SF4) that works better than an old method called Normal Float (NF4). The authors also suggest ways to make another type of storage, E2M1, work even better by adding extra support. |
