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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 fine-tuning, which retrains all model parameters, becomes less feasible
- 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
- 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
- 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
- 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
- Dynamic Low-Rank Sparse Adaptation for Large Language Models
This framework enhances sparse Large Language Models (LLMs) by integrating low-rank adaptation (LoRA) into the sparsity framework with dynamically adjusted layer-wise sparsity rates and rank allocations
- 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
- PiSSA: Principal Singular Values and Singular Vectors Adaptation of . . .
The paper presents a novel approach to parameter-efficient fine-tuning (PEFT) of large language models (LLMs) The proposed method, PiSSA, is an enhancement of the existing LoRA (Low-Rank Adaptation) method PiSSA differentiates itself by initializing the adaptation matrices with the principal components obtained through SVD of the original model weights, as opposed to LoRA’s random
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