{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Adapters adds parameter-efficient fine-tuning methods to HuggingFace Transformers models, letting you train and compose multiple adapter modules on top of pre-trained models with minimal parameter overhead.","skillfed_tags":["parameter-efficient-tuning","transformer-extension","nlp-training"],"use_cases":["Fine-tune large language models on domain-specific tasks with minimal GPU memory by training only adapter weights instead of all parameters.","Load and apply pre-trained adapters from AdapterHub to perform sentiment analysis, question classification, or other NLP tasks on models like RoBERTa or T5.","Compose multiple task-specific adapters in parallel or sequence to handle multi-task learning or ensemble predictions in a single model.","Experiment with different parameter-efficient methods (LoRA, prefix tuning, bottleneck adapters) on the same base model using a unified configuration interface.","Train quantized adapters (Q-LoRA, Q-Bottleneck) for even smaller memory footprints on resource-constrained hardware."],"what_it_does":"Adapters is an extension library for HuggingFace Transformers that integrates parameter-efficient fine-tuning methods into transformer models. Instead of updating all model weights during training, adapters add small trainable modules (like LoRA, prefix tuning, or bottleneck adapters) that achieve competitive performance with far fewer parameters. The library provides a unified interface for training these adapters, loading pre-trained ones from AdapterHub, and composing multiple adapters together for multi-task or ensemble scenarios.\n\nThe package depends on transformers and packaging, and requires Python 3.9+ and PyTorch 2.0+. It's designed for researchers and practitioners working on NLP tasks who want efficient fine-tuning without the memory and compute costs of full model updates. You can use it to adapt existing model setups, load community-shared adapters, or combine multiple adapters in a single model using composition blocks.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem\u2014efficient fine-tuning of large models\u2014with a well-documented, unified interface. Install it if you're doing transfer learning on transformer models and want to reduce training costs and memory usage."},"id":"adapters","links":{"html":"https://skillfed.io/packages/adapters","md":"https://skillfed.io/packages/adapters.md","pypi":"https://pypi.org/project/adapters/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-26","license_spdx":null,"license_treatment":"permissive","name":"adapters","python_support":"supports_current","summary":"A Unified Library for Parameter-Efficient and Modular Transfer Learning"},"popularity":{"monthly_downloads":82826,"position":14130,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3.0"}