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evaluating-llms-harness

Benchmark LLMs with lm-evaluation-harness to produce reproducible metrics.

Updated Apr 9, 2026
Search Tags:#benchmarking#benchmark#llm-evaluation#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/nadicodeai/argo-agent --skill evaluating-llms-harness-nadicodeai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/nadicodeai/argo-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/nadicodeai/argo-agent --skill evaluating-llms-harness-nadicodeai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm, and includes references (resource) components.

What problem does it solve?

This Skill provides a standardized framework to benchmark language models (LLMs) using the lm-evaluation-harness, enabling reproducible quality metrics across tasks.

Core Features & Use Cases

  • Standardized benchmarks for LLMs across MMLU, GSM8K, HumanEval, TruthfulQA, and more.
  • Supports OpenAI, local, and API-based models, with options for vLLM, HF, and custom endpoints.
  • Useful for research publications, model comparison, and progress tracking in ML projects.

Quick Start

Run an evaluation with your model and a selected task list to produce benchmark results.

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 across standardized tasks like MMLU and GSM8K?▼

You can benchmark LLMs across MMLU and GSM8K by applying a standardized evaluation harness to produce reproducible quality metrics suitable for model comparison and publication workflows.

Can I evaluate local and API-based models using vLLM and Hugging Face transformers?▼

Yes, you can evaluate local and API-based models using vLLM and Hugging Face transformers. The harness supports OpenAI, local, and custom endpoints for comprehensive benchmarking.

What is the best way to generate reproducible LLM evaluation metrics for research publications?▼

The best way to generate reproducible LLM evaluation metrics for research publications is to use a standardized benchmarking harness that applies consistent evaluation logic across core tasks.

Do I need lm-eval and vllm dependencies to run standardized LLM benchmarks?▼

Yes, you need the lm-eval, transformers, and vllm dependencies to run these standardized LLM benchmarks efficiently and produce comparable evaluation results.

Does the LLM evaluation harness support tasks beyond MMLU, GSM8K, and HumanEval?▼

Yes, the LLM evaluation harness supports tasks beyond MMLU, GSM8K, and HumanEval, including TruthfulQA and more, allowing for comprehensive model quality tracking.

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