[ 58] 的工作将 NAS 应用于 Bitfit,其中 S-BitFit 保留了 Bitfit 中的结构性质,限制了 NAS 算法必须为每个偏置模块选择是否 =0。与 Transformer 中微调特定模块的 Bitfit 类似,Xattn Tuning [ 67] 仅微调交叉注意力层。SPT(灵敏度感知视觉参数高效微调)[68]首先识别调谐时通过损耗降低测量的敏感参数。
来自Northeastern University 及University of California, Riverside等大学的研究者发表了“Parameter-Efficient Fine-Tuning for Large models: A Comprehensive Survey”对PEFT技术进行全面综述,探讨各种PEFT算法及其应用,为研究人员提供深入的理解。 论文地址:https://arxiv.org/abs/2403.14608 以下为论文主要内容: 一、...
Ideally, only a small number of parameters needs to be changed in this process of fine-tuning, which can then be more easily distributed. In this Analysis, different methods of fine-tuning with only a small number of parameters are compared on a large set of natural language processing tasks...
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...
Ideally, only a small number of parameters needs to be changed in this process of fine-tuning, which can then be more easily distributed. In this Analysis, different methods of fine-tuning with only a small number of parameters are compared on a large set of natural language processing tasks...
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, ...
github:GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. 概念:其核心理念是通过仅调整模型的一小部分参数,而保持大部分预训练参数不变,从而大幅减少计算资源和存储需求 LORA(Low-Rank Adaptation低秩适应) github:GitHub - microsoft/LoRA: Code for loralib, an implement...
PEFT is a popular technique used to efficiently finetune large language models for use in various downstream tasks. When finetuning with PEFT, the base model weights are frozen, and a few trainable adapter modules are injected into the model, resulting in a very small number (<< 1%) of ...
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models Pytorch implementation of the SPP methods as presented in:SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models (ICML 2024)Xudong Lu*, Aojun Zhou*, Yuhui Xu*, Renrui Zhang, Peng Gao, ...
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adap...