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:

Use PyTorch-native libraries within the NVIDIA NeMo Framework to customize models to hit your exacting requirements for latency, speed, memory, and compute. @NVIDIA's blog demonstrates how quantization-aware distillation (QAD) improves the Nemotron 3.5 Lightning model using NVIDIA Model Optimizer. We walk through the entire training pipeline, and show how QAD consistently outperforms post-training quantization (PTQ) compression on agentic benchmarks, ensuring high quality while reducing memory usage. Read it here:

The right model depends on the task. NVIDIA NeMo Switchyard helps developers route each agent workflow step across a chosen model pool based on their own quality, latency and cost criteria. Kari Briski joins @MTSlive to explain why agent workflows need model routing.

One missing component can delay an entire AI system. @nvidia and @PalantirTech are bringing AI to critical supply chain decisions, combining NVIDIA Nemotron, cuOpt, and NeMo with Palantir Foundry and AIP to help planners flag emerging risks and act faster. ⬇️

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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