Configuration

FreeEval uses a comprehensive configuration system that allows you to define every aspect of your evaluation pipeline. Through configuration, you specify which models to evaluate, what datasets to use, which evaluation steps to run, and how each component should behave.

Configuration Structure

A FreeEval configuration defines the complete recipe for an evaluation. It consists of several key sections that work together to create a cohesive evaluation pipeline:

The models section defines which language models you want to evaluate. Here you can specify multiple models, each with its own configuration including the model type (local, remote, or API-based), specific parameters, and any authentication requirements.

The datasets section specifies what data will be used for evaluation. You can define multiple datasets with different characteristics, allowing you to test models across various domains or difficulty levels.

The steps section outlines the evaluation methods to apply. Each step represents a specific evaluation technique or benchmark, and can be configured with parameters that control its behavior. Steps are executed in sequence, with each contributing to the overall assessment.

The output section determines how results are recorded and formatted. This allows you to customize how evaluation results are saved, making it easier to analyze and share your findings.

Configuration Flexibility

FreeEval’s configuration system is designed to be flexible, allowing for both simple evaluations and complex, multi-faceted assessments. You can start with a basic configuration that evaluates a single model on a standard dataset, then gradually expand it to include more models, datasets, or evaluation steps as your needs evolve.

This flexibility extends to each component within the configuration. For models, you can specify detailed parameters like context length, temperature, or specialized capabilities. For datasets, you can define filtering criteria or transformations to apply. For steps, you can fine-tune the evaluation methodology to focus on specific aspects of model performance.

The configuration-driven approach makes it easy to replicate evaluations consistently, share evaluation recipes with others, and maintain a record of how each assessment was conducted. This supports reproducible research and enables systematic comparison of model improvements over time.

Configuration Management

FreeEval provides several ways to manage configurations, from simple configuration files to programmatic creation. You can define configurations in YAML or JSON format, making them easy to read, edit, and version control. Alternatively, you can use the configuration builder API to construct configurations programmatically, which is particularly useful for dynamic or parameterized evaluations.

For complex evaluation needs, configurations can be modularized, allowing you to reuse common components across different evaluations. You can define template configurations for specific evaluation scenarios, then customize them for particular experiments or research questions.

By understanding FreeEval’s configuration system, you gain precise control over your model evaluations, ensuring they align with your specific research or development objectives. The declarative nature of configurations also makes your evaluation methodology transparent and reproducible, which is essential for credible machine learning research and development.