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  • LoRA: Low-Rank Adaptation of Large Language Models
    An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains As we pre-train larger models, full
  • LORA: L -R ADAPTATION OF LARGE LAN GUAGE M - OpenReview
    ABSTRACT An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible Using GPT-3 175B as an example – deploying independent instances of fine-tuned models, each with 175B parameters, is
  • Federated Residual Low-Rank Adaptation of Large Language Models
    Low-Rank Adaptation (LoRA) presents an effective solution for federated fine-tuning of Large Language Models (LLMs), as it substantially reduces communication overhead However, a straightforward combination of FedAvg and LoRA results in suboptimal performance, especially under data heterogeneity
  • Dynamic Low-Rank Sparse Adaptation for Large Language Models
    Despite the efficacy of network sparsity in alleviating the deployment strain of Large Language Models (LLMs), it endures significant performance degradation Applying Low-Rank Adaptation (LoRA) to
  • QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
    In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation
  • On the Optimization Landscape of Low Rank Adaptation Methods for Large . . .
    Training Large Language Models (LLMs) poses significant memory challenges, making low-rank adaptation methods an attractive solution Previously, Low-Rank Adaptation (LoRA) addressed this by adding a trainable low-rank matrix to the frozen pre-trained weights in each layer, reducing the number of trainable parameters and optimizer states
  • BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate. . .
    BA-LoRA employs a low-rank adaptation technique, allowing for efficient parameter updates during fine-tuning Experimental results demonstrate that this approach effectively mitigates forgetting and reduces bias in adapted models, outperforming traditional fine-tuning methods
  • LoRA-FA: Memory-efficient Low-rank Adaptation for Large Language Models . . .
    The low-rank adaptation (LoRA) method can largely reduce the amount of trainable parameters for fine-tuning large language models (LLMs) and it becomes a very common technique in fine-tuning LLMs However, during fine-tuning, it still requires very expensive activation memory to update low-rank weights
  • GoRA: Gradient-driven Adaptive Low Rank Adaptation - OpenReview
    Low-Rank Adaptation (LoRA) is a crucial method for efficiently fine-tuning large language models (LLMs), with its effectiveness influenced by two key factors: rank selection and weight initialization
  • SP-LoRA: Sparsity-Preserved Low-Rank Adaptation for Sparse Large . . .
    However, these methods often result in performance gaps, particularly for smaller models, and lack efficient fine-tuning strategies that preserve sparsity This paper introduces SP-LoRA, a novel approach that combines the benefits of low-rank adaptation (LoRA) with the efficiency of sparse models





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