A hands-on tutorial for using PyTorch 2.0 with Hugging Face Transformers to fine-tune a BERT model for text classification. PyTorch 2.0 introduces torch.compile(), TorchDynamo, AOTAutograd, and a fused AdamW optimizer that together reduce training time by up to 52.5% with no code changes beyond adding torch_compile to TrainingArguments. The tutorial walks through environment setup, loading the BANKING77 dataset, tokenization, fine-tuning with the Hugging Face Trainer, and running inference — all on an NVIDIA A10G GPU.
