PyTorch-native NeMo AutoModel handles transformer pretraining in @nvidia's end-to-end workflow for building a transaction foundation model. The workflow combines GPU-accelerated data processing and tokenization, decoder-only model pretraining, embedding extraction, and XGBoost fraud classification. On the synthetic @IBM TabFormer dataset, combining raw features with learned embeddings increased Average Precision by 41.76% over the raw-feature baseline. 🔗 Read the full post:

We have worked with @nvidia to integrate their official Agent Skills catalog into the Hermes Skills Hub. These skills teach your agent how to use CUDA-X libraries, Omniverse and Physical AI workflows, NeMo training and inference tools, and other platform components.

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