Lightweight Fine-Tuning with PEFT & LoRA

This project focuses on a practical question in NLP: how much useful adaptation can you obtain from a pretrained model without paying the full cost of fine-tuning everything?

Using LoRA on distilbert-base-uncased for sentiment analysis, the pipeline shows that a very small trainable subset of parameters can still deliver a strong performance jump over the zero-shot baseline. That makes the project less about squeezing out maximum benchmark accuracy and more about understanding the trade-off between performance and efficiency.

Built with the Hugging Face ecosystem, the implementation covers evaluation, LoRA configuration, training, and inference in a lightweight setup that remains accessible on modest hardware.