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Parameter-efficient fine-tuning of BERT for binary sentiment classification using QLoRA (4-bit NF4 quantization + LoRA adapters) on the IMDb 20k dataset. Reduces trainable parameters by ~99% and GPU memory by ~70% vs full fine-tuning. Runs on CPU locally and full QLoRA on GPU (Colab/Kaggle).