Adapter Transformers

What it is

Adapter Transformers is an open-source library designed to facilitate parameter-efficient transfer learning in natural language processing (NLP) tasks. It supports various transformer architectures like BERT and LoRA, focusing on modular and efficient tuning of model parameters. The library is implemented in Python and leverages PyTorch for deep learning operations. With a strong emphasis on adaptability, it allows developers to integrate adapters into existing transformer models, reducing the need for extensive retraining and resource consumption.

Focus on parameter-efficient tuning for transformers.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited to Python and PyTorch

   Implemented in Python and relies on PyTorch.

2. 02

   Moderate number of open issues

   48 open issues on GitHub.

3. 03

   Requires understanding of transformer models

   Focuses on advanced NLP techniques.

What it does well

• Parameter-efficient learning

  Supports adapters and LoRA for efficient tuning.

• Open-source and community-driven

  Hosted on GitHub with 2826 stars and 374 forks.

• Supports multiple transformer architectures

  Compatible with BERT and other models.

Pricing

Free tier

Yes

Paid from

—

Model

N/A

Enterprise plan

No

Pricing last confirmed Oct 1, 2026.