  Low-rank adapters (LoRA) and their variants are popular parameter-efficient
fine-tuning (PEFT) techniques that closely match full model fine-tune
performance while requiring only a small number of additional parameters. These
additional LoRA parameters are specific to the base model being adapted. When
the base model needs to be deprecated and replaced with a new one, all the
associated LoRA modules need to be re-trained. Such re-training requires access
to the data used to train the LoRA for the original base model. This is
especially problematic for commercial cloud applications where the LoRA modules
and the base models are hosted by service providers who may not be allowed to
host proprietary client task data. To address this challenge, we propose
$\textit{Trans-LoRA}$ -- a novel method for lossless, nearly data-free transfer
of LoRAs across base models. Our approach relies on synthetic data to transfer
LoRA modules. Using large language models, we design a synthetic data generator
to approximate the data-generating process of the $\textit{observed}$ task data
subset. Training on the resulting synthetic dataset transfers LoRA modules to
new models. We show the effectiveness of our approach using both LLama and
Gemma model families. Our approach achieves lossless (mostly improved) LoRA
transfer between models within and across different base model families, and
even between different PEFT methods, on a wide variety of tasks.
