{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"}],"enrichment":{"capability":"PEFT implements parameter-efficient fine-tuning methods (LoRA, QLoRA, and others) that adapt large pretrained language models by training only a small fraction of parameters instead of all weights, dramatically reducing memory and compute requirements.","skillfed_tags":["model-adaptation","efficient-training","llm-tools"],"use_cases":["Fine-tune a 7B or 12B parameter LLM on a 16GB consumer GPU using QLoRA and 8-bit quantization.","Train task-specific adapters for multiple datasets without storing full model copies for each task.","Adapt Stable Diffusion or other diffusion models with LoRA while keeping memory footprint under 10GB.","Deploy inference with multiple task-specific adapters loaded from the same base model.","Reduce checkpoint storage from gigabytes to megabytes by saving only adapter weights."],"what_it_does":"PEFT is a library for efficiently adapting large pretrained models to downstream tasks by training only a small subset of parameters\u2014typically less than 1% of the model\u2014rather than all weights. This dramatically cuts GPU memory and storage costs while achieving performance comparable to full fine-tuning. It integrates with transformers, Diffusers, and Accelerate, making it practical to fine-tune billion-parameter models on consumer-grade hardware.\n\nThe library provides multiple parameter-efficient methods (LoRA, QLoRA, soft prompts, IA3, and others) and handles the mechanics of wrapping base models with adapter configurations, managing training, and loading adapters for inference. You prepare a model by passing it through `get_peft_model` with a configuration, train normally, then save only the adapter weights\u2014typically a few megabytes instead of gigabytes.","worth_installing":"Yes. PEFT is production-stable, actively maintained, widely adopted in the Hugging Face ecosystem, has no known vulnerabilities, and solves a genuine problem\u2014making large model fine-tuning accessible on modest hardware. Install it if you need to adapt pretrained models efficiently; skip it only if you are doing full-model training or not fine-tuning at all."},"id":"peft","links":{"html":"https://skillfed.io/packages/peft","md":"https://skillfed.io/packages/peft.md","pypi":"https://pypi.org/project/peft/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":null,"license_treatment":"permissive","name":"peft","python_support":"supports_current","summary":"Parameter-Efficient Fine-Tuning (PEFT)"},"popularity":{"monthly_downloads":11618527,"position":1378,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.20.0"}