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All digests tagged Synthetic Data Generation
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Learn how to post-train NVIDIA Nemotron 3.5 Lightning for local context using Nemotron-Personas and LoRA fine-tuning with NeMo AutoModel. This workflow ensures models adhere to specific policies.
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Learn how NVIDIA Data Designer builds synthetic data pipelines to teach AI agents complex search and reasoning skills. The framework uses DAGs, seed data, and tool calls to create scalable, diverse training records.
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This presentation covers three aspects of search agents: evaluation, synthetic data generation using ORBIT, and trajectory analysis via tools like Hawkeye. It emphasizes that efficiency is as critical as accuracy.
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When PHI is too sensitive to keep, how do you build datasets? Learn about reversing inference workflows and using LLMs to synthetically generate high-fidelity medical records.
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Synthetic personas use LLMs to simulate human behavior, but they require careful validation. Learn how failure modes like latent confounders and distribution shape loss can mislead results, emphasizing that models are forecasts, not ground truth.
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Scaling LLMs requires sophisticated synthetic data pipelines, rigorous weight hashing checks, and fixing numerical precision bugs (BF16/FP32) to build models like Laguna S (118B parameters).
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