[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...
Delta-tuning not only provides a promising way to adapt large PLMs but also sheds light on the mechanisms behind such model adaptations. Compared with fine-tuning, delta-tuning makes model adaptation a considerably low-cost process. For instance, researchers find that the optimization problem of ...
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, ...
Paper tables with annotated results for Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
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 of large-scale pre-trained language models. Nat. Mach. Intell. 5, 220–235 (2023). Article Google Scholar Liu, H. et al. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. Adv. Neural Inf. Process. Syst. 35, 1950–...
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...