llm-tournament provides a modular, extensible approach for benchmarking large language models. Users define participants (LLMs), configure tournament brackets, specify prompts and scoring logic, and run automated rounds. Results are aggregated into leaderboards and visualizations, enabling data-driven decisions on LLM selection and fine-tuning efforts. The framework supports custom task definitions, evaluation metrics, and batch execution across cloud or local environments.

Weights & Biases (W&B) is a comprehensive AI developer platform designed to streamline the process of machine learning model training, fine-tuning, and management. It provides tools that enable developers to track experiments, visualize results, and manage the lifecycle of ML models. By centralizing these operations, W&B ensures that data scientists and machine learning engineers can efficiently monitor the performance of their models, spot regressions, and maintain a clear documentation of model evolution.