nemo-evaluator-sdk

To benchmark LLMs across diverse benchmarks like MMLU and HumanEval, you can evaluate models across 100+ benchmarks from 18+ harnesses for performance and safety. This process supports multi-backend execution and containerized evaluation for reproducibility.

Yes, LLM evaluation requires Docker and the nemo-evaluator-launcher for execution. You need to install the launcher via pip and configure your NGC API Key environment variable to run the containerized evaluation.

You can run scalable LLM evaluation on local Docker, Slurm HPC, or cloud platforms. This multi-backend execution support provides flexibility for researchers and developers needing to assess model performance and safety.

The best way to ensure reproducible LLM benchmarking results is using a container-first architecture. This approach guarantees consistent evaluation outcomes across various harnesses when assessing large language models.

To evaluate large language models for both performance and safety, this solution assesses models across a comprehensive set of 100+ benchmarks. It utilizes diverse harnesses to provide a scalable and reproducible evaluation process.

Supported harnesses for scalable LLM evaluation include 18+ frameworks featuring MMLU, HumanEval, and GSM8K, alongside various safety benchmarks. These harnesses enable comprehensive performance assessment across diverse evaluation criteria.