Build Custom LLM Benchmarks for Your Application

Learn how to create and implement custom benchmarks for evaluating large language models (LLMs) specific to your application needs in this 47-minute tutorial from Trelis Research. Explore YourBench from HuggingFace for quick-start benchmarking, discover techniques for running benchmarks locally, and master advanced data generation concepts including PDF conversion, difficulty estimation, citations, chunking, multi-hop reasoning, and filtering. The tutorial covers evaluating custom datasets using LightEval and demonstrates comprehensive evaluation and data inspection techniques with Trelis ADVANCED-evals tools. Access the repository at Trelis.com/ADVANCED-evals to follow along with practical examples and implementation guidance.