Awesome-LoRA is a collection of state-of-the-art (SOTA), novel low-rank adaptation methods (papers, codes and datasets). Any other interesting papers and codes are welcome. Any problems, please contact [email protected]. If you find this repository useful to your research or work, it is really appreciated to star this repository. ✨
LoRA is an efficient finetuning technique proposed by Microsoft researchers to adapt large models to specific tasks and datasets.
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2022 | LoRA: Low-Rank Adaptation of Large Language Models | ICLR | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| - | - | - | - | - |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2024 | AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models | arXiv | Link | - |
| 2024 | Parameter-Efficient Fine-Tuning with Discrete Fourier Transform | ICML | Link | Link |
| 2024 | LoNAS: Elastic Low-Rank Adapters for Efficient Large Language | COLING | Link | Link |
| 2024 | LoRA Learns Less and Forgets Less | arXiv | Link | - |
| 2024 | MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning | arXiv | Link | Link |
| 2024 | LoRA+: Efficient Low Rank Adaptation of Large Models | arXiv | Link | Link |
| 2024 | PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization | arXiv | Link | - |
| 2024 | Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models | arXiv | Link | Link |
| 2024 | Multi-LoRA Composition for Image Generation | arXiv | Link | Link |
| 2024 | BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models | arXiv | Link | - |
| 2024 | AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models | arXiv | Link | - |
| 2024 | LoRA Meets Dropout under a Unified Framework | arXiv | Link | - |
| 2024 | MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning | arXiv | Link | Link |
| 2024 | Galore: Memory-efficient llm training by gradient low-rank projection | ICML | Link | Link |
| 2024 | Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model | arXiv | Link | - |
| 2024 | LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning | arXiv | Link | - |
| 2023 | DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation | EACL | Link | Link |
| 2023 | The expressive power of low-rank adaptation | ICLR | Link | Link |
| 2023 | Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHF | arXiv | Link | Link |
| 2023 | Deep Learning Model Compression With Rank Reduction in Tensor Decomposition | TNNLS | Link | - |
| 2023 | Loramoe: Revolutionizing mixture of experts for maintaining world knowledge in language model alignment | arXiv | Link | - |
| 2023 | Bayesian Low-rank Adaptation for Large Language Models | ICLR | Link | Link |
| 2023 | Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning | arXiv | Link | - |
| 2023 | Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting | PMLR | Link | Link |
| 2023 | Sparse low-rank adaptation of pre-trained language models | EMNLP | Link | Link |
| 2023 | Low-Rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition | ASRU | Link | - |
| 2023 | SiRA: Sparse Mixture of Low Rank Adaptation | arXiv | Link | - |
| 2022 | LoRA: Low-Rank Adaptation of Large Language Models | ICLR | Link | Link |
| Year | Title | Venue | Paper | Code |
|---|---|---|---|---|
| 2021 | Compacter: Efficient low-rank hypercomplex adapter layers | NeurIPS | Link | Link |
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