ZhengxiangShi / DePT

[ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"

Date Created 2023-09-11 (about a year ago)

Commits 29 (last one 11 months ago)

Stargazers 95 (0 this week)

Watchers 2 (0 this week)

Forks 16

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RepositoryStats indexes 623,832 repositories, of these ZhengxiangShi/DePT is ranked #314,516 (50th percentile) for total stargazers, and #478,496 for total watchers. Github reports the primary language for this repository as Python, for repositories using this language it is ranked #59,400/126,749.

ZhengxiangShi/DePT is also tagged with popular topics, for these it's ranked: nlp (#1,518/2501), natural-language-processing (#910/1464), large-language-models (#723/1194), language-model (#349/537), transfer-learning (#175/322), fine-tuning (#118/215)

Other Information

ZhengxiangShi/DePT has Github issues enabled, there are 2 open issues and 9 closed issues.

Homepage URL: http://arxiv.org/abs/2309.05173

All Topics

nlp peft fine-tuning prompt-tuning language-model transfer-learning nlp-machine-learning large-language-models parameter-efficient-tuning natural-language-processing parameter-efficient-fine-tuning

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29 commits on the default branch (main) since jan '22

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The only known language in this repository is Python

updated: 2025-02-25 @ 09:03pm, id: 689806286 / R_kgDOKR2bzg