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Showing 1 to 5 of 5 for “"Peft"”.
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Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels
… Parameter-efficient finetuning techniques (PEFT) have been proposed to address this issue by significantly reducing the number of trainable parameters, achieving comparable results to full-parameter finetuning. Despite widespread adoption, PEFT methods are often used interchangeably without …
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Efficient and Composable Adaptation for Cross-Lingual Transfer
Parameter-efficient fine-tuning (PEFT) has emerged as a important technique for moderating the growing cost of fine-tuning state-of-the-art pre-trained language models. The modular properties of some PEFT techniques, such as reusability, composability and resistance to overfitting, lend them to …
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DYNAMIC NEURAL NETWORKS FOR EFFICIENT VISION MODEL INFERENCE
… contributions: Parameter-Efficient Fine-Tuning (PEFT): We demonstrate that dynamic networks can be developed with negligible trainable parameters, reducing FLOPs by 30% while significantly lowering training costs. Diffusion Transformers (DiT) Acceleration: We introduce a dynamic architecture for …
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GNN-Enhanced Hierarchical Federated Learning in Device-to-Device Networks
… federated parameter-efficient fine-tuning (PEFT) method to enable the efficient adaptation of foundation models in hierarchical D2D-assisted FL architectures. To address the vulnerability of lightweight PEFT parameters to modality heterogeneity and the potential bias propagation through …
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Training a massively multimodal transformer on YouTube data: pre-training and parameter efficient fine-tuning on HPC infrastructure
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms