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evaluating-llms-harness

Benchmark LLMs on standardized tasks using the lm-evaluation-harness CLI.

Updated May 15, 2026
Search Tags:#benchmarking#llm evaluation#model comparison#vllm#gsm8k#mmlu#human eval
One-click install
npx skills add https://github.com/cabezno/bmb-encover-agent --skill evaluating-llms-harness-cabezno
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/cabezno/bmb-encover-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/cabezno/bmb-encover-agent --skill evaluating-llms-harness-cabezno

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates ad-hoc, non-comparable model testing by providing a standardized way to benchmark LLMs across widely used academic and industry evaluation suites.

Core Features & Use Cases

  • Run reproducible benchmark suites: Evaluate models on tasks such as MMLU, GSM8K, HumanEval, TruthfulQA, and HellaSwag to compare quality consistently.
  • Support multiple backends: Benchmark via HuggingFace models, vLLM for faster inference, or API endpoints using the harness’ unified interfaces.
  • Track training progress and compare models: Re-run evaluations across checkpoints to monitor improvements and produce side-by-side comparison tables for model releases.

Quick Start

Run an MMLU + GSM8K benchmark on a HuggingFace model and save results by using the harness command with hf backend, specifying the model name, tasks, few-shot count, batch size, and an output path.

Frequently Asked Questions about evaluating-llms-harness

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

FAQPage Schema
How do I benchmark LLMs on MMLU and GSM8K?▼

You can benchmark LLMs on MMLU and GSM8K by running the lm-evaluation-harness CLI with a HuggingFace model backend, specifying the model name, tasks, few-shot count, batch size, and an output path to save results.

Can I use vLLM for faster LLM evaluation?▼

Yes, you can use vLLM as a backend for faster inference during LLM evaluation, alongside standard HuggingFace models or API endpoints, using the harness' unified interfaces.

What is the best way to compare model quality during releases?▼

The best way to compare model quality is to run reproducible benchmark suites on tasks like MMLU, GSM8K, and HumanEval, generating side-by-side comparison tables for model releases.

How do I track training progress across model checkpoints?▼

Track training progress by re-running standardized evaluations across different model checkpoints to monitor improvements and produce consistent comparison tables.

Does lm-evaluation-harness support reproducible few-shot benchmarking?▼

Yes, lm-evaluation-harness supports reproducible benchmarking by allowing you to specify exact settings such as few-shot count and batch size during evaluation.

What academic tests are available for model comparison?▼

Available academic tests for model comparison include MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, and ARC, allowing teams to evaluate model quality fairly.

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