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  • 1
    Adapters

    Adapters

    A Unified Library for Parameter-Efficient Learning

    ...Adapters provide a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning), adapter merging via task arithmetics or the composition of multiple adapters via composition blocks, allowing advanced research in parameter-efficient transfer learning for NLP tasks.
    Downloads: 0 This Week
    Last Update:
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    OpenDelta

    OpenDelta

    A plug-and-play library for parameter-efficient-tuning

    ...OpenDelta is a toolkit for parameter-efficient tuning methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most parameters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora, or any other types of delta tuning with preferred PTMs.
    Downloads: 2 This Week
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