Home › Model Libraries & Training › OpenAI Evals

OpenAI Evals - AI Model Libraries & Training Tool

Overview

OpenAI Evals is an open-source framework for evaluating large language models (LLMs) and LLM systems. It provides a registry of benchmarks and tools for developers and researchers to run, customize, and manage evaluations to assess model performance and behavior.

Key Features

  • Open-source framework for evaluating LLMs
  • Registry of community and reference benchmarks
  • Tooling to run and manage evaluation suites
  • Configurable and extensible evaluation workflows
  • Designed for developers and researchers assessing behavior

Ideal Use Cases

  • Compare model performance on standard benchmarks
  • Develop and validate model evaluation suites
  • Customize benchmarks for domain-specific behavior testing
  • Integrate evaluations into model development workflows
  • Reproduce and share evaluation results across teams

Getting Started

  • Clone the OpenAI Evals GitHub repository
  • Read the README and documentation for setup instructions
  • Install required dependencies listed in the repository
  • Explore the benchmark registry to choose evaluation tasks
  • Configure evaluations for your model and desired metrics
  • Run evaluation jobs according to repository instructions
  • Examine logs, metrics, and outputs to assess performance

Pricing

Open-source project hosted on GitHub; no pricing or paid plans are provided in the repository.

Key Information

  • Category: Model Libraries & Training
  • Type: AI Model Libraries & Training Tool
Visit Official Website

Related Tools

Hugging Face Accelerate

A simple way to launch, train, and use PyTorch models on almost any device with support for distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP/DeepSpeed.
Diffusers

A library implementing state-of-the-art diffusion models for image, video, and audio generation, supporting both PyTorch and FLAX frameworks.
Hugging Face Transformers

A comprehensive library of pretrained models for text, vision, audio, video, and multimodal tasks, enabling fine-tuning and inference across many generative AI use cases.
Unsloth AI

Unsloth AI is an enterprise platform that accelerates fine-tuning of large language models and vision models by leveraging innovative quantization techniques. It enables faster performance (up to 2.2x faster) and uses significantly less VRAM, making model deployment and training more efficient. The organization also offers open-source tools and models, and is integrated with Hugging Face, with additional details available on its website.
AutoTrain

Hugging Face AutoTrain is an automated machine learning (AutoML) tool that allows users to train, evaluate, and deploy state-of-the-art ML models without writing code. It supports a range of tasks including text classification, image classification, token classification, summarization, question answering, translation, tabular data tasks, and LLM finetuning, with seamless integration into the Hugging Face ecosystem.
DeepScaleR

DeepScaleR is an open-source project that democratizes reinforcement learning (RL) for large language models (LLMs). The repository provides training scripts, model checkpoints, detailed hyperparameter configurations, datasets, and evaluation logs to reproduce and scale RL techniques on LLMs, aimed at reproducibility and research in advanced AI training.