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Post-training techniques have become essential as demand for Reasoning AI systems explodes. This talk provides a practical overview of how to enhance the reasoning capabilities of open-weight models—using Mistral as a working example. We’ll explore the full pipeline: sourcing high-quality reasoning datasets, selecting the right model checkpoints, and using tools that extend the functionality of PyTorch like NVIDIA NeMo and TensorRT-LLM. Whether you’re working on chatbots, agents, or task-specific models, you’ll leave with a clear understanding of the tools and workflows to take advantage of open models.
