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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
- 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
- HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large . . .
We propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs) While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks
- 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
- ME-LORA: MEMORY-EFFICIENT BAYESIAN LOW- RANK ADAPTATION FOR LARGE . . .
Bayesian Low-Rank Adaptation (LoRA) has shown excellent performance in reducing the overconfidence of inference by large language models as it can accurately quantify the inference uncertainty
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