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ADAPTERHUB
Parameter-efficient fine-tuning with adapter modules
Apache-2.0
ABOUT
Full fine-tuning of large transformer models is computationally expensive and requires storing a separate copy of the model for each downstream task. AdapterHub solves this by allowing you to train small adapter modules inserted into pretrained transformer layers, enabling parameter-efficient multi-task learning with drastically reduced memory and storage requirements.
INSTALL
pip install adapter-transformersINTEGRATION GUIDE
1. Fine-tuning large language models for multiple downstream tasks without retraining the full model
2. Implementing parameter-efficient transfer learning for NLP tasks with limited computational resources
3. Combining multiple adapter modules for multi-task and multi-domain learning from a single base model
TAGS
fine-tuningadapterstransformersnlpparameter-efficientlorapytorchtransfer-learning