How does NeMo Evaluator ensure reproducibility?

The SDK uses a container-first architecture with Docker to ensure the evaluation environment and dependencies remain identical across different runs and systems.

What benchmarks are supported by NeMo Evaluator?

It supports over 100 benchmarks including MMLU, GSM8K, HumanEval, IFEval, GPQA, and specialized safety and vision-language tasks.

Where can I export the evaluation results?

Results can be exported to popular experiment tracking tools like MLflow and Weights & Biases (W&B), or saved locally as JSON and YAML files.

Can I use this for Vision-Language Models (VLMs)?

Yes, it includes dedicated support for VLM evaluation kits and tasks such as OCRBench, ChartQA, and MMMU.

Do I need specific hardware to use this skill?

While optimized for NVIDIA ecosystems, it can evaluate any OpenAI-compatible API endpoint (like vLLM or NIM) regardless of where the model is hosted.

NeMo Evaluator SDK

Name: NeMo Evaluator SDK
Author: zechenzhangAGI

byzechenzhangAGI

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908

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Data Science & ML

Benchmarks Large Language Models across 100+ standard and custom evaluation harnesses with enterprise-grade reproducibility.

NeMo Evaluator SDK is a comprehensive benchmarking tool designed to evaluate Large Language Models (LLMs) and Vision-Language Models (VLMs) across a vast library of over 100 academic and industry-standard benchmarks. Built with a container-first architecture, it ensures reproducible results whether running locally via Docker, on large-scale Slurm HPC clusters, or in the cloud. It streamlines the evaluation process by providing unified access to major harnesses like lm-evaluation-harness, simple-evals, and BigCode, making it an essential tool for AI researchers and engineers needing to validate model performance, safety, and reasoning capabilities.

Key Features

01Comprehensive support for both LLMs and Vision-Language Models (VLMs)

02Containerized architecture ensures fully reproducible benchmarking results

03Multi-backend execution support for Local Docker, Slurm HPC, and Cloud platforms

04Built-in result exporting to enterprise tools like MLflow, Weights & Biases, and JSON

05Access 100+ benchmarks from 18+ different evaluation harnesses in one platform

06908 GitHub stars

Use Cases

01Conducting safety and security probing using harnesses like Aegis and Garak

02Running large-scale evaluation jobs on enterprise HPC infrastructure using Slurm

03Comparing model performance across standard benchmarks like MMLU, GSM8K, and HumanEval

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zechenzhangagi/ai-research-skills nemo-evaluator

For use in Claude.ai and ChatGPT

Download Skill