Going over Hermes + NeMo Relay with @NousResearch today at 10am PT with Lead Enterprise Engineer @VKyriazakos @NVIDIAAI Come hang out and bring your agent tracing questions!

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.

Your agent got the right answer. How much work did it take to get there? We worked with Nous Research/Teknium on a hands-on walkthrough of NVIDIA NeMo Relay to collect traces for Hermes Agent. Run two example scenarios, follow the agent’s calls and retries, and see the full trace in Arize Phoenix. The blog also looks at how Nous used traces and task results to evaluate fixes across repeated runs. Tech Blog:

Databricks and @nvidia are expanding our collaboration so enterprises can customize open models on their own data and take them all the way to production. With AI Runtime, developers can use NVIDIA NeMo, NGC containers and new Nemotron cookbooks to train and fine-tune models on serverless NVIDIA GPUs alongside governed enterprise data. Nemotron 3.5 lightning and Super VL are also ready to load, adapt and serve on Databricks from day one. Customize with governed enterprise data. Serve in production. All on Databricks.

Your agent got the right answer. How much work did it take to get there? We worked with @NousResearch/@Teknium on a hands-on walkthrough of NVIDIA NeMo Relay to collect traces for Hermes Agent. Run two example scenarios, follow the agent’s calls and retries, and see the full trace in Arize Phoenix. The blog also looks at how Nous used traces and task results to evaluate fixes across repeated runs. TechBlog: 📽️ by our own @Picomoorhead: