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NVIDIA's blog post describes an end-to-end workflow using PyTorch-native NeMo AutoModel for pretraining a transaction foundation model. The workflow uses GPU-accelerated data processing and tokenization, decoder-only model pretraining, and embedding extraction to improve fraud classification performance by over 40% on the IBM TabFormer dataset.