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  • [2106. 09685] LoRA: Low-Rank Adaptation of Large Language Models - arXiv. org
    We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks
  • 论文精读：LoRa: Low-Rank Adaptation of Large Language . . .
    我们提出了低秩适应（Low-Rank Adaptation，简称LoRA），该方法冻结预训练模型的权重，并在Transformer架构的每一层注入可训练的秩分解矩阵，大大减少了下游任务的可训练参数数量。
  • LoRA: Low-Rank Adaptation of Large Language Models - GitHub
    LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices while freezing the original weights This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency
  • 【论文笔记】LoRA LOW-RANK ADAPTATION OF LARGE . . .
    LoRA，全称为Low-Rank Adaptation of Large Language Models，是一种针对大型语言模型进行有效微调的技术。 在当前的自然语言处理（NLP）领域，预训练的大型 语言模型 如BERT、GPT或T5等，已经成为解决各种任务的基础。
  • [2106. 09685] LoRA: Low-Rank Adaptation of Large Language Models - ar5iv
    A new paradigm emerged with BERT (Devlin et al , 2019b) and GPT-2 (Radford et al , b) – both are large Transformer language models trained on a large amount of text – where fine-tuning on task-specific data after pre-training on general domain data provides a significant performance gain compared to training on task-specific data directly
  • LoRA: Low-Rank Adaptation of Large Language Models - ICLR
    We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks
  • LoRA: Low-Rank Adaptation of Large Language Models 笔记 . . .
    LoRA（Low-Rank Adaptation) 通过引⼊低秩矩阵分解，在减少计算资源和存储需求的同时，保持了预训练模型的初 始性能，稳定了微调过程，并降低了存储和部署成本。
  • LoRA: Low-Rank Adaptation of Large Language Models
    We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks
  • LoRA: Low-Rank Adaptation of Large Language Models
    We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks
  • LORA论文中英对照版-LORA: LOW-RANK ADAPTATION . . .
    We propose Low-Rank Adaptation, or LoRA, which freezes the pretrained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks





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