Guides the planning, parameter selection, and documentation of LLM fine-tuning and evaluation experiments.
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.
主な機能
01Automatic resource verification for models, datasets, and evaluation scripts
02Interactive 9-step parameter selection for fine-tuning and evaluation
038 GitHub stars
04Automated generation of structured experiment_summary.yaml configurations
05System prompt validation to ensure consistency between training and testing
06Detailed JSONL logging to maintain a machine-readable audit trail
ユースケース
01Planning large-scale LLM fine-tuning runs for social science research
02Creating structured, reproducible experiment documentation for ML workflows
03Designing evaluation matrices to compare base models against checkpoints
