adapters

We have hosted the application adapters in order to run this application in our online workstations with Wine or directly.

Quick description about adapters:

Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference. 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.

Features:

• Enables parameter-efficient fine-tuning of transformers
• Supports modular adapters for different NLP tasks
• Reduces memory and computational requirements
• Compatible with Hugging Face Transformers
• Allows quick adaptation to new languages and domains
• Provides a growing repository of pre-trained adapters

Programming Language: Python.
Categories: