In this tutorial, we work with NVIDIA's Open-SWE-Traces dataset to study agentic software-engineering trajectories for fine-tuning. We stream the data directly from Hugging Face, so we can process it efficiently in Google Colab without down everything locally. We normalize multi-turn agent conversations, parse final code patches, and build an analysis DataFrame covering trajectory length, tool usage, patch size, language distribution, and resolution outcomes. We then curate a supervised fine-tuning subset using success labels, token limits, language filters, and patch availability.
The post Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics appeared first on MarkTechPost.