nemo-evaluator-sdk

To benchmark LLMs reproducibly, you can evaluate models across 100+ benchmarks from 18+ harnesses using a containerized workflow. This process scales across local Docker, Slurm HPC, or cloud platforms to ensure enterprise-grade, consistent evaluation results.

Enterprise-grade LLM evaluation supports 100+ benchmarks from 18+ harnesses, including MMLU, HumanEval, GSM8K, safety, and VLM tasks. These benchmarks run via multi-backend execution against OpenAI-compatible endpoints for comprehensive model assessment.

Yes, you can run scalable LLM evaluations on Slurm HPC or cloud platforms using Docker. The framework supports multi-backend execution across these environments to deliver reproducible, containerized benchmarking results.

Yes, you need Docker and an API key to access containerized evaluation containers. Additionally, the Nemo Evaluator launcher is required to configure and execute the benchmarking workflow across your chosen deployment targets.

The best way to configure a full-scale LLM benchmark locally is by defining a minimal YAML file. You then execute the evaluation against your endpoint and local Docker deployment targets to generate reproducible results.

Yes, you can export LLM benchmarking results to MLflow and W&B. The framework enables enterprise-grade configuration and result export, allowing you to track and manage evaluation metrics through these platforms.