ShiZhengyan / DePTLinks
[ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"
☆96Updated last year
Alternatives and similar repositories for DePT
Users that are interested in DePT are comparing it to the libraries listed below
Sorting:
- [NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning☆32Updated 2 years ago
- One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning☆40Updated 2 years ago
- Source code of EMNLP 2022 Findings paper "SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters"☆18Updated last year
- [ACL 2023] Code for paper “Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation”(https://arxiv.org/abs/2305.…☆38Updated 2 years ago
- ☆38Updated last year
- Code for paper "UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning", ACL 2022☆63Updated 3 years ago
- Codes for Merging Large Language Models☆33Updated last year
- Code for paper "Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models."☆46Updated 11 months ago
- [NeurIPS 2023] Github repository for "Composing Parameter-Efficient Modules with Arithmetic Operations"☆61Updated last year
- Residual Prompt Tuning: a method for faster and better prompt tuning.☆56Updated 2 years ago
- [EMNLP 2023, Main Conference] Sparse Low-rank Adaptation of Pre-trained Language Models☆83Updated last year
- MoCLE (First MLLM with MoE for instruction customization and generalization!) (https://arxiv.org/abs/2312.12379)☆43Updated 2 months ago
- ☆30Updated last year
- Official code for our paper, "LoRA-Pro: Are Low-Rank Adapters Properly Optimized? "☆131Updated 5 months ago