PEFT

PEFT (Parameter-Efficient Fine-Tuning) is a machine learning technique that adapts pre-trained large language models to specific tasks or domains by training only a small subset of parameters while keeping the majority of the original model frozen. This approach dramatically reduces computational requirements, memory usage, and training time compared to full fine-tuning while maintaining comparable or superior performance on target tasks. PEFT methods include techniques such as LoRA (Low-Rank Adaptation), adapters, prefix tuning, and prompt tuning that introduce trainable parameters strategically within the model architecture without modifying the core pre-trained weights. These techniques enable efficient customization of foundation models for enterprise-specific use cases, domain adaptation, and multi-task scenarios where full model retraining would be prohibitively expensive. Modern PEFT implementations allow organizations to create specialized AI models for customer service, document processing, code generation, or industry-specific applications while leveraging the general knowledge embedded in large pre-trained models.

Advanced PEFT approaches support multi-task learning, parameter sharing across related tasks, and modular adaptation strategies that enable rapid deployment of customized AI solutions with minimal computational overhead and reduced infrastructure requirements for businesses seeking tailored AI capabilities.