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Showing 1 to 5 of 5 for “"Peft"”.

  1. 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 …

    mit Repository record for Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels (opens in a new tab)

  2. 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 …

    cambridge Repository record for Efficient and Composable Adaptation for Cross-Lingual Transfer (opens in a new tab)

  3. 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 …

    nus Repository record for DYNAMIC NEURAL NETWORKS FOR EFFICIENT VISION MODEL INFERENCE (opens in a new tab)

  4. 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 …

    exeter

  5. 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

    uiuc Repository record for Training a massively multimodal transformer on YouTube data: pre-training and parameter efficient fine-tuning on HPC infrastructure (opens in a new tab)