LoRTA: Low Rank Tensor Adaptation of Large Language Models

• URL: http://arxiv.org/abs/2410.04060v2
• Date: Tue, 15 Oct 2024 16:03:20 GMT
• Title: LoRTA: Low Rank Tensor Adaptation of Large Language Models
• Authors: Ignacio Hounie, Charilaos Kanatsoulis, Arnuv Tandon, Alejandro Ribeiro,
• Abstract summary: Low Rank Adaptation (LoRA) is a popular Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. We propose a novel approach that employs a low rank tensor parametrization for model updates. Our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.
• Score: 70.32218116940393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low Rank Adaptation (LoRA) is a popular Parameter Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. LoRA parameterizes model updates using low-rank matrices at each layer, significantly reducing the number of trainable parameters and, consequently, resource requirements during fine-tuning. However, the lower bound on the number of trainable parameters remains high due to the use of the low-rank matrix model. In this paper, we address this limitation by proposing a novel approach that employs a low rank tensor parametrization for model updates. The proposed low rank tensor model can significantly reduce the number of trainable parameters, while also allowing for finer-grained control over adapter size. Our experiments on Natural Language Understanding, Instruction Tuning, Preference Optimization and Protein Folding benchmarks demonstrate that our method is both efficient and effective for fine-tuning large language models, achieving a substantial reduction in the number of parameters while maintaining comparable performance.

Related papers

• Dynamic Subset Tuning: Expanding the Operational Range of Parameter-Efficient Training for Large Language Models [14.762222323897978]
  We propose a novel parameter-efficient training (PET) method for large language models. Unlike prior methods, this subset is not fixed in location but rather which parameters are modified over the course of training. Our method enables a seamless scaling of the subset size across an arbitrary proportion of the total model size.
  arXiv  Detail & Related papers  (2024-11-13T13:53:10Z)
• SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation [52.6922833948127]
  In this work, we investigate the importance of parameters in pre-trained diffusion models. We propose a novel model fine-tuning method to make full use of these ineffective parameters. Our method enhances the generative capabilities of pre-trained models in downstream applications.
  arXiv  Detail & Related papers  (2024-09-10T16:44:47Z)
• Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning [38.80020737321214]
  We propose a framework for efficient parameter fine-tuning (PEFT) based on structured unrestricted-rank matrices (SURM) SURMs achieve 5-7% accuracy gains on various image classification tasks while replacing low-rank matrices in LoRA. It also results in up to 12x reduction of the number of parameters in adapters (with virtually no loss in quality) on the GLUE benchmark.
  arXiv  Detail & Related papers  (2024-06-25T17:26:05Z)
• Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation [12.07880147193174]
  We show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of over parameterization without the computational burdens. We demonstrate the effectiveness of this approach for deep low-rank matrix completion as well as fine-tuning language models.
  arXiv  Detail & Related papers  (2024-06-06T14:29:49Z)
• Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models [73.88009808326387]
  We propose a novel spectrum-aware adaptation framework for generative models. Our method adjusts both singular values and their basis vectors of pretrained weights. We introduce Spectral Ortho Decomposition Adaptation (SODA), which balances computational efficiency and representation capacity.
  arXiv  Detail & Related papers  (2024-05-31T17:43:35Z)
• MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning [71.50432879573614]
  Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional. We present MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters while maintaining a higher rank. Our experimental results show that, compared to LoRA, MELoRA achieves better performance with 8 times fewer trainable parameters on natural language understanding tasks and 36 times fewer trainable parameters on instruction following tasks.
  arXiv  Detail & Related papers  (2024-02-27T07:14:12Z)
• LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models [20.5908375260123]
  Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance. We present LoRETTA, a framework that significantly reduces trainable parameters through tensor-train decomposition. LoRETTA achieves comparable or better performance than most widely used PEFT methods with up to $100times$ fewer parameters on the LLaMA-2-7B models.
  arXiv  Detail & Related papers  (2024-02-18T01:20:00Z)
• PELA: Learning Parameter-Efficient Models with Low-Rank Approximation [16.9278983497498]
  We propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks.
  arXiv  Detail & Related papers  (2023-10-16T07:17:33Z)
• AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning [143.23123791557245]
  Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. We propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA.
  arXiv  Detail & Related papers  (2023-03-18T22:36:25Z)
• 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.