Summary of Fingpt-hpc: Efficient Pretraining and Finetuning Large Language Models For Financial Applications with High-performance Computing, by Xiao-yang Liu et al.
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing
by Xiao-Yang Liu, Jie Zhang, Guoxuan Wang, Weiqing Tong, Anwar Walid
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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 This paper tackles the issue of computationally intensive large language models (LLMs) by proposing high-performance GPU-based methods to pretrain and finetune LLMs for financial applications. The authors highlight that most parameters come from linear layers in transformer structures, which are highly redundant and contribute to 80% of computation workload and 99% of model size. To address this, they introduce two novel methods: replacing one conventional linear layer with two narrower ones, reducing parameters by several orders of magnitude; and quantizing parameters into low precision (8-bit and 4-bit), further reducing memory consumption. The proposed methods achieve a speedup of 1.3X and model compression ratio of 2.64X for pretraining without accuracy drop, as well as improved accuracy in finetuning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models more efficient to use. Right now, they take up a lot of computer power and memory, making it hard to run them on regular computers or even smartphones. The authors suggest two ways to make these models better: by combining smaller linear layers into one bigger layer, and by using less precise numbers to store the model’s parameters. This makes the models faster and takes up much less space without losing any accuracy. The results show that their methods can make the models run 1.3 times faster and use up to 2.6 times less memory. |
Keywords
* Artificial intelligence * Model compression * Precision * Pretraining * Transformer
