PEFT
Parameter-Efficient Fine-Tuning (PEFT) methods for large pretrained models
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github.com
About PEFT
PEFT is a library that enables efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of model parameters. It is integrated with Transformers, Diffusers, and Accelerate for easy model training and inference.
Description summarised by AI from the sources listed below.
Key features
- Parameter-Efficient Fine-Tuning (PEFT) methods
- parameter-efficient fine-tuning
- Integration with Transformers
- reduced computational costs
- reduced computational and storage costs
- integration with Transformers, Diffusers, and Accelerate
- reduced storage costs
- Diffusers for conveniently managing different adapters
- Accelerate for distributed training and inference
Use cases
- downstream applications
- large pretrained models
Pricing
Pricing model: Open source. Detailed plans are not recorded; check the official website for current prices.
Pricing summarised by AI from the sources listed below.
