[25]: Parameter-efficient fine-tuning of large-scale pre-trained language models, Nature Machine Intelligence, vol. 5, no. 3, pp. 220–235, 2023. [26]: Parameter efficient fine-tuning methods for pretrained language models: A critical review and assessment, arXiv preprint arXiv:2312.12148, ...
本文参考论文《An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models》 摘要 GPT-3 和 ChatGPT 等大型语言模型 (LLM) 的成功导致了众多具有成本效益且易于访问的替代方案的开发,这些替代方案是通过使用特定于任务的数据(例如 ChatDoctor)或指令数据(例如,Alpaca)。在各种微调方法中,基于ad...
Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain while minim...
Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. It also allows us to train AI models on a broader range of hardware, including devices with limited compu...
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveyhttp://t.cn/A6HGky5g 论文全面调查了参数高效微调对于大型模型的重要性。大型模型在多个应用领域取得了划时代的进步,使得在不同任务...
of research focusing on the parameter-efficient adaptation of PLMs, which optimizes a small portion of the model parameters while keeping the rest fixed, drastically cutting down computation and storage costs. In general, it demonstrates that large-scale models could be effectively stimulated by the...
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to new tasks. NVIDIA NIM for LLMs (NIM for LLMs) supports LoRA PEFT adapters trained by the NeMo Framework and Hugging Face Transformers libraries. When submitting inference requests to the NIM, ...
Exploring Parameter-Efficient Fine-Tuning of a Large-Scale Pre-Trained Model for scRNA-seq Cell Type Annotation However, the fine-tuning process of large-scale pre-trained models incurs substantial computational expenses. To tackle this issue, a promising avenue of ... Y Liu,T Li,Z Wang,......
为了解决这个问题,轻量化微调(Parameter-Efficient Fine-Tuning)技术应运而生。轻量化微调是一种优化策略,通过微调预训练模型的一部分参数,使其更适合特定任务。相比于传统的全参数微调,轻量化微调具有更低的计算成本和存储需求,能够显著减少模型的复杂度和大小。这种技术在保持模型性能的同时,能够适应资源受限的场景,...
In the rapidly evolving field of AI, using large language models in an efficient and effective manner is becoming more and more important. In this article, you will learn how to tune an LLM with Low-Rank Adaptation (LoRA) in computationally efficient manner! Why Finetuning? Pretrained large...