#Parameter-Efficient

Adapters enrich HuggingFace's Transformers by integrating over 10 adapter methods into 20+ models, supporting efficient fine-tuning and transfer learning. Key features include full-precision and quantized training, adapter task arithmetics, and multi-adapter compositions, facilitating advanced research in NLP. Compatible with Python 3.8+ and PyTorch 1.10+, it's an essential tool for optimizing models with ease of implementation.

Access a comprehensive collection on parameter-efficient transfer learning, emphasizing fine-tuning techniques for pre-trained vision models. Discover key papers and methodologies, including adapter and prompt tuning. Keep updated with developments like the Visual PEFT Library/Benchmark release. This repository is perfect for researchers aiming to optimize transfer learning processes.