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handsomelong922
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nemo-evaluator-sdk

Evaluate LLMs across 100+ benchmarks from 18+ harnesses.

Updated Apr 3, 2026
Search Tags:#benchmarking#docker#enterprise#reproducible#llm-evaluation#slurm#nemo-evaluator
One-click install
npx skills add https://github.com/handsomelong922/my-codex-skills --skill nemo-evaluator-sdk-handsomelong922
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nemo-evaluator-sdk
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/nemo-evaluator
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill nemo-evaluator-sdk-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

NeMo Evaluator provides a centralized, reproducible platform to benchmark LLMs across a wide spectrum of tasks from 100+ benchmarks and 18+ harnesses, enabling enterprises to compare model performance consistently.

Core Features & Use Cases

  • 100+ benchmarks from 18+ harnesses (MMLU, GSM8K, HumanEval, safety, VLM) with containerized execution.
  • Multi-backend support: local Docker, Slurm HPC, and Lepton cloud for scalable evaluation.
  • Reproducible results with container-first architecture and export capabilities (MLflow, Weights & Biases).
  • End-to-end workflow including setup, run, monitor, and export for model comparisons.

Quick Start

Install Nemo Evaluator Launcher, configure a YAML config with a target endpoint, and run your first evaluation.

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 evaluation harnesses reproducibly?▼

LLM benchmarking across 100+ benchmarks from 18+ harnesses is handled centrally through NeMo Evaluator using a containerized execution architecture to ensure consistent, reproducible model comparisons.

Can I run LLM evaluations on Slurm HPC or local Docker deployments?▼

LLM evaluations support multi-backend deployments across local Docker, Slurm HPC, and Lepton cloud, allowing you to scale model benchmarking operations according to your available compute infrastructure.

What benchmarks are available for enterprise LLM evaluation?▼

Enterprise LLM evaluation includes over 100 benchmarks from 18+ harnesses, covering tasks like MMLU, GSM8K, HumanEval, safety evaluations, and VLM assessments within a single centralized platform.

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

Exporting LLM benchmarking results to MLflow or Weights & Biases is supported natively, enabling you to track and compare model performance metrics seamlessly after completing your evaluation runs.

Do I need an OpenAI-compatible endpoint to run LLM evaluations with NeMo Evaluator?▼

An OpenAI-compatible endpoint is required to run LLM evaluations with NeMo Evaluator, along with a container runtime like Docker and the Nemo Evaluator Launcher configured via a YAML file.

What is the best way to compare LLM performance at scale?▼

Comparing LLM performance at scale is best achieved through a centralized platform that orchestrates end-to-end workflows including setup, execution, monitoring, and metric export for consistent benchmarking.

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