nemo-evaluator-sdk

Evaluate LLMs across 100+ benchmarks from 18+ harnesses.

Updated Mar 18, 2026

Search Tags:#benchmarking #evaluation #docker #benchmarks #multibackend #nemo-evaluator

Ready to run

npx skills add https://github.com/tadod12/fraud-detection-research --skill nemo-evaluator-sdk-tadod12

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nemo-evaluator-sdk
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/11-evaluation/nemo-evaluator
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill nemo-evaluator-sdk-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nemo-evaluator-launcher>=0.1.25, docker, and includes references (resource) components.

What problem does it solve?

NeMo Evaluator SDK enables scalable, containerized benchmarking of LLMs across 100+ benchmarks from 18+ harnesses, delivering reproducible results.

Core Features & Use Cases

100+ benchmarks across 18+ harnesses for comprehensive evaluation.
Multi-backend execution supports local Docker, Slurm HPC, and Lepton cloud for flexible deployment.
Enterprise-grade outputs with options to export results to MLflow or Weights & Biases and to integrate with OpenAI-compatible endpoints.

Quick Start

Install Nemo Evaluator, configure a minimal YAML evaluation, and run the benchmark to compare model performance.

Frequently Asked Questions about nemo-evaluator-sdk

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I benchmark LLMs across multiple harnesses using Docker?▼

You can benchmark LLMs across 100+ tests from 18+ harnesses using containerized, reproducible evaluation with Docker. This approach enables cross-model comparisons and consistent results across local, HPC, or cloud environments.

Can I run LLM evaluation workloads on Slurm HPC or Lepton cloud?▼

Yes, LLM evaluation supports multi-backend execution across local Docker, Slurm HPC, and Lepton cloud. This allows flexible deployment for enterprise benchmarking scenarios while maintaining reproducible cross-model comparison results.

What is the best way to compare LLM performance reproducibly?▼

The best way to compare LLM performance reproducibly is through containerized benchmarking across 100+ tests from 18+ harnesses. This method ensures consistent results and supports cross-model comparisons for enterprise evaluation scenarios.

Do I need Docker to evaluate models with OpenAI-compatible endpoints?▼

Yes, Docker is required to evaluate models with OpenAI-compatible endpoints. The evaluation framework uses containerized environments to ensure reproducible results across 100+ benchmarks from 18+ harnesses.

How do I export LLM benchmark results to MLflow or Weights & Biases?▼

You can export LLM benchmark results to MLflow or Weights & Biases after running containerized evaluations across 100+ benchmarks. This integration tracks enterprise-grade outputs and supports cross-model performance comparisons.