Project Title

peft — State-of-the-art Parameter-Efficient Fine-Tuning for Large Pretrained Models

Overview

PEFT (Parameter-Efficient Fine-Tuning) is a Python library developed by Hugging Face that enables efficient adaptation of large pretrained models to various downstream applications by fine-tuning only a small number of parameters. This significantly reduces computational and storage costs while achieving performance comparable to fully fine-tuned models. PEFT integrates seamlessly with Transformers, Diffusers, and Accelerate for easy model training, inference, and distributed processing.

Key Features

Integration with Transformers, Diffusers, and Accelerate for comprehensive model training and inference.
Support for various state-of-the-art PEFT methods, including adapters, soft prompts, and IA3.
Easy installation via pip and straightforward model preparation for training with PEFT methods.
Comprehensive API reference and conceptual guides for understanding and applying PEFT methods.

Use Cases

Researchers and developers looking to fine-tune large pretrained models for specific tasks without incurring high computational costs.
Teams working on natural language processing applications that require efficient model adaptation.
Educators and students exploring the latest techniques in parameter-efficient fine-tuning for educational purposes.

Advantages

Reduces computational and storage costs by fine-tuning only a small percentage of model parameters.
Achieves performance comparable to fully fine-tuned models, making it a cost-effective solution.
Offers a wide range of PEFT methods, providing flexibility in choosing the most suitable approach for different tasks.

Limitations / Considerations

May require additional setup and configuration for specific use cases, depending on the complexity of the model and task.
The performance of PEFT methods can vary depending on the model and task, requiring careful selection and tuning of parameters.

AdapterHub: A framework for training and sharing adapter modules, similar to PEFT in its focus on parameter-efficient fine-tuning.
Transformers: A widely-used library for state-of-the-art NLP models, which PEFT integrates with for model training and inference.
Diffusers: A library for diffusion models, which PEFT complements by providing parameter-efficient fine-tuning capabilities.

Basic Information

GitHub: https://github.com/huggingface/peft
Stars: 19,499
License: Unknown
Last Commit: 2025-09-07

📊 Project Information

Project Name: peft
GitHub URL: https://github.com/huggingface/peft
Programming Language: Python
⭐ Stars: 19,499
🍴 Forks: 2,021
📅 Created: 2022-11-25
🔄 Last Updated: 2025-09-07

🏷️ Project Topics

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📚 Documentation

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peft

Project Description