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Fine-tuningFreeOpen Source
ADAPTERS
Parameter-efficient transfer learning with adapter methods
Apache-2.0
ABOUT
Full fine-tuning of large transformer models requires updating billions of parameters, demanding high-end GPUs and extensive memory. Adapters solves this by injecting small, trainable bottleneck modules into frozen pretrained models, reducing storage per task to a few megabytes and enabling fine-tuning on consumer hardware while retaining 95% or more of full fine-tuning performance across a wide range of NLP tasks.
INSTALL
pip install adaptersINTEGRATION GUIDE
1. Fine-tune billion-parameter LLMs on consumer GPUs using LoRA and QLoRA adapters, reducing trainable parameters by over 99 percent
2. Compose multiple task-specific adapters on a shared frozen backbone for efficient multi-task inference without separate models
3. Apply MAD-X language adapters to adapt multilingual models to new languages with minimal target-language data
4. Train and share lightweight adapter modules via the AdapterHub repository for easy community reuse
TAGS
peftparameter-efficient-fine-tuningloraadapterstransfer-learningnlptransformershuggingface