#LLM Evaluation

continuous-eval is an open-source tool providing a data-driven evaluation for LLM applications. It uses a modular approach to assess each pipeline segment with specific metrics, supporting RAG, code generation, and classification through diverse metric types. Leverage its ability to use feedback and synthetic datasets for thorough testing. Explore custom metrics for comprehensive evaluations.

DeepEval is an open-source tool that evaluates large-language models (LLMs) using metrics such as G-Eval and answer relevancy. It operates locally, supports CI/CD workflows, and offers integration with platforms like Hugging Face. DeepEval helps determine hyperparameters for optimal LLM performance and facilitates transitions between systems such as OpenAI and self-hosted Llama2.

TruLens provides tools for evaluating and tracking language model experiments, enabling developers to improve their applications by analyzing performance through detailed methods. Integrate feedback functions, explore the RAG Triad, and perform honest and helpful evaluations. Easily connect tools and logging in your development workflow, and iterate through app versions using an intuitive interface. Start by installing TruLens via PyPI and follow the quick guide to evaluate RAGs from scratch. Join the community for support and collaboration.

Prometheus-Eval is an open-source project offering sophisticated tools for the evaluation of language models, including the BiGGen-Bench with 77 tasks and 765 instances. It features Prometheus 2 models, such as the efficient 8x7B version, supporting both absolute and relative grading, aligned closely with human review standards. The project facilitates both local and API-based inference to ensure flexible assessment processes, providing robust and expandable tools for contemporary AI evaluation needs.

Discover a state-of-the-art evaluation suite for large language models using dynamic and ground-truth-based benchmarks which ensure precise and economical model assessment. MixEval stands out by providing a fast and budget-friendly evaluation, cutting time and costs to only 6% of standard evaluations, while keeping a strong correlation with actual model rankings. This methodical approach, updated routinely, employs both free-form and multiple-choice formats for comprehensive and unbiased AI model analysis, perfect for researchers and developers in need of dependable, reproducible evaluation solutions.