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Design Experiment is a specialized workflow tool for researchers and developers conducting social research with Large Language Models. It streamlines the complex process of planning fine-tuning runs and evaluation benchmarks by providing a structured 9-step parameter selection process, validating resource availability, and generating comprehensive YAML configurations. By ensuring consistency between training prompts and evaluation metrics, it reduces errors in experimental design and creates a machine-readable audit trail for reproducible AI research, specifically optimized for tools like torchtune and inspect-ai.
