nemo-evaluator-sdk

You can benchmark LLMs at scale using NVIDIA NeMo by deploying evaluation tasks across local Docker, Slurm HPC clusters, or cloud platforms to achieve reproducible, high-throughput results. This Skill automates scalable execution across these environments.

Yes, you can evaluate LLMs on MMLU, HumanEval, and GSM8K. The Skill supports a comprehensive suite of over 100 benchmarks across 18 harnesses, covering reasoning, coding, math, safety, and VLM tasks.

You can run reproducible LLM evaluations on a Slurm HPC cluster by utilizing this Skill's container-first architecture. This ensures consistent and reliable benchmarking results across local Docker and HPC environments.

The best way to benchmark proprietary models and export results to MLflow is using this Skill's multi-backend execution. It evaluates your models against industry-standard benchmarks and facilitates exporting results for comparison.

Yes, this LLM evaluation tool supports local Docker execution. It leverages a container-first architecture to run scalable and reproducible benchmark tasks locally or across cloud and HPC platforms.