Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics
In this tutorial, we explore the Open-SWE-Traces dataset as a practical resource for studying and preparing agentic software-engineering trajectories for fine-tuning. We stream the dataset directly from Hugging Face, so we can work with a large dataset efficiently in Google Colab without downloading everything locally. We inspect individual records, normalize multi-turn agent conversations, parse final code patches, extract useful metadata, and build an analysis DataFrame to understand trajecto
This tutorial explains how to prepare software engineering trajectories from an open dataset for fine-tuning AI agents. The work involves streaming the dataset from Hugging Face, normalizing multi-turn agent conversations, parsing code patches, and extracting metadata about trajectory length, tool usage, and patch characteristics. The goal is to build a curated subset of high-quality training data by filtering based on success labels, token limits, language preferences, and code patch availability.
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Also reported by The Verge
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