llm-tournament is a Python library that automates head-to-head matchups among different LLMs, applies custom scoring functions, and produces comparative reports. It simplifies benchmarking at scale.
llm-tournament is a Python library that automates head-to-head matchups among different LLMs, applies custom scoring functions, and produces comparative reports. It simplifies benchmarking at scale.
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.
Who will use llm-tournament?
AI researchers
Machine learning engineers
Data scientists
NLP developers
Technology evaluators
How to use the llm-tournament?
Step1: Install via pip (pip install llm-tournament)
Step2: Create a configuration file listing LLM endpoints and credentials
Step3: Define tournament structure with rounds and matchups
Step4: Implement scoring functions for your evaluation criteria
Step5: Run llm-tournament to execute all matchups
Step6: Review generated leaderboards and reports for analysis
Platform
mac
windows
linux
llm-tournament's Core Features & Benefits
The Core Features
Automated LLM matchups and bracket management
Customizable prompt pipelines
Pluggable scoring and evaluation functions
Leaderboard and ranking generation
Extensible plugin architecture
Batch execution across cloud or local
The Benefits
Streamlined LLM benchmarking
Reproducible evaluation workflows
Scalable tournament orchestration
Data-driven model selection
Time-saving automation
llm-tournament's Main Use Cases & Applications
Comparing OpenAI GPT-4 vs GPT-3.5 performance on Q&A tasks
Academic research on LLM capabilities under controlled conditions