$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning

• URL: http://arxiv.org/abs/2405.17258v1
• Date: Mon, 27 May 2024 15:15:08 GMT
• Title: $\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning
• Authors: Runqian Wang, Soumya Ghosh, David Cox, Diego Antognini, Aude Oliva, Rogerio Feris, Leonid Karlinsky,
• Abstract summary: Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning techniques. When the base model needs to be replaced with a new one, all the associated LoRA modules need to be re-trained. This is especially problematic for commercial cloud applications where the LoRA modules and the base models are hosted by service providers.
• Score: 28.668326340001695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• In-Context Meta LoRA Generation [61.690065588534296]
  Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. We propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models. ICM-LoRA enables more accurate LoRA parameter reconstruction than current parameter reconstruction methods.
  arXiv  Detail & Related papers  (2025-01-29T13:12:01Z)
• LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation [48.22550575107633]
  A new adapter, Cross-Model Low-Rank Adaptation (LoRA-X), enables the training-free transfer of LoRA parameters across source and target models. Our experiments demonstrate the effectiveness of LoRA-X for text-to-image generation.
  arXiv  Detail & Related papers  (2025-01-27T23:02:24Z)
• LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement [5.162783756846019]
  Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning. Low-Rank Adaptation (LoRA) methods like Low-Rank Adaptation (LoRA) reduce this cost by introducing low-rank matrices for tuning fewer parameters. LoRA-FAIR maintains computational and communication efficiency, yielding superior performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-22T14:19:01Z)
• Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning [57.36978335727009]
  Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs) In this paper, we propose a framework that adaptively retrieves and composes multiple LoRAs based on input prompts.
  arXiv  Detail & Related papers  (2024-06-24T05:24:41Z)
• VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector Banks [10.266224162377371]
  Low-rank adaptation (LoRA) and its variants incur substantial storage and transmission costs. We introduce a "divide-and-share" paradigm that breaks the barriers of low-rank decomposition across matrix dimensions, modules, and layers. VB-LoRA achieves extreme parameter efficiency while maintaining comparable or better performance compared to state-of-the-art PEFT methods.
  arXiv  Detail & Related papers  (2024-05-24T03:24:34Z)
• Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning [31.036465632204663]
  We introduce Chain of LoRA, an iterative optimization framework inspired by the Frank-Wolfe algorithm. We demonstrate that COLA can consistently outperform LoRA without additional computational or memory costs.
  arXiv  Detail & Related papers  (2024-01-08T14:26:49Z)
• FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients [50.13097183691517]
  In real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources. We propose a novel federated tuning algorithm, FedRA. In each communication round, FedRA randomly generates an allocation matrix. It reorganizes a small number of layers from the original model based on the allocation matrix and fine-tunes using adapters.
  arXiv  Detail & Related papers  (2023-11-19T04:43:16Z)
• CA-LoRA: Adapting Existing LoRA for Compressed LLMs to Enable Efficient Multi-Tasking on Personal Devices [78.16679232748196]
  We introduce a Compression-Aware LoRA (CA-LoRA) framework to transfer Large Language Models (LLMs) to other tasks. Experiment results demonstrate that CA-LoRA outperforms the vanilla LoRA methods applied to a compressed LLM. The source code of CA-LoRA is available at https://github.com/thunlp/CA-LoRA.
  arXiv  Detail & Related papers  (2023-07-15T04:37:11Z)
• LoRA: Low-Rank Adaptation of Large Language Models [71.75808607987281]
  Low-Rank Adaptation, or LoRA, freezes the pre-trained model weights and injects trainable rank decomposition into each layer of the Transformer architecture. For GPT-3, LoRA can reduce the number of trainable parameters by 10,000 times and the computation hardware requirement by 3 times compared to full fine-tuning.
  arXiv  Detail & Related papers  (2021-06-17T17:37:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.