8. BitFit: only fine-tune the biases of the network 仅更新bias 9. DiffPruning: parameter mask is learned during model fine-tuning mask掉一些训练的参数 10. Freeze and Reconfigure (FAR): selects columns of parameter matrices to prune and reconfigures linear layers into trainable and frozen. ...
This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to address the challenges of fine-tuning large language models by training only a small subset of parameters. We provide a taxo...
[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, ...
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...
Fine-tuning methodsrange from instruction fine-tuning, where models are trained using specific examples that demonstrate the desired responses, to parameter-efficient fine-tuning (PEFT), which updates only a subset of the model's parameters to conserve computational resources and prevent catastrophic for...
2024 arXiv Space Domain Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture - 2024 arXiv Space Domain UniForensics: Face Forgery Detection via General Facial Representation - 2024 arXiv Benchmark DF40: Toward Next-Generation Deepfake Detection - 2024 arXiv...
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective 2024 Arxiv Class-Incremental Learning with CLIP: Adaptive Representation Adjustment and Parameter Fusion 2024 ECCV Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models 2024 ICML One Si...
As for the second question, I'm not familiar with 2.0 containers, so I need to look into it first, but I think it should be fine. class Singleton { public: static Singleton &GetInstance() { static Singleton instance; return instance; } XXXGetter() XXXSetter() private: Singleton() {}...
The distribution of atmospheric turbulence at the sixth frame is predicted based on the distribution from the first frame to the fifth. c, Architecture of our developed residual ConvLSTM network. CL, convolutional LSTM; conv, convolution. (For parameter definitions, see ‘Atmospheric turbulence ...
目前HuggingFace 的 PEFT (Parameter-Efficient Fine-Tuning) 提供了模型微调加速的方法,除了这里的 LoRA,还有 Prefix Tuning、P-Tuning、Prompt Tuning 等其他微调加速方法。PEFT 方法通过仅微调少量(额外)模型参数,同时冻结预训练模型中的大部分参数,大大降低了计算与存储成本,在当前的各个垂类微调领域得到了广泛应用。