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evaluating-llms-harness

Benchmark large language models on standardized academic and code evaluation suites.

Updated May 12, 2026
Search Tags:#benchmarking#llm evaluation#gsm8k#mmlu#humaneval#api evaluation#distributed evaluation
One-click install
npx skills add https://github.com/hungthinh04/Hermes_AI_Agent --skill evaluating-llms-harness-hungthinh04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/hungthinh04/Hermes_AI_Agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/hungthinh04/Hermes_AI_Agent --skill evaluating-llms-harness-hungthinh04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the guesswork from language model evaluation by providing a structured way to benchmark model quality, compare systems fairly, and track progress over time.

Core Features & Use Cases

  • Standardized Benchmarks: Run common academic and industry tasks such as MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, ARC, and LongBench with consistent prompting and metrics.
  • Model Coverage: Evaluate HuggingFace models, vLLM-backed inference, API-based models, and distributed multi-GPU setups from one workflow.
  • Practical Workflows: Use it for release validation, training checkpoint tracking, model comparison tables, custom task evaluation, and faster throughput with batching or parallelism.
  • Troubleshooting and Reproducibility: Apply guidance for few-shot settings, batch sizing, caching, rate limits, and metric interpretation to get repeatable results.

Quick Start

Use the lm-evaluation-harness skill to benchmark my model on MMLU and GSM8K with five-shot evaluation and a concise results summary.

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 with few-shot evaluation?▼

You can benchmark LLMs on MMLU and GSM8K by configuring reproducible prompting and few-shot settings to generate concise results summaries with consistent metrics.

Can I evaluate API-based models and local HuggingFace models in the same workflow?▼

Yes, you can evaluate API-based models, HuggingFace models, and vLLM-backed inference within the same benchmarking workflow to compare systems fairly.

What is the best way to run distributed LLM evaluation across multiple GPUs?▼

For distributed LLM evaluation across multiple GPUs, you can apply batching, parallelism, and caching configurations to achieve faster throughput and repeatable results.

How do I ensure reproducible results when benchmarking large language models?▼

To ensure reproducible LLM benchmarking, apply guidance for batch sizing, caching, rate limits, and consistent prompting across academic suites like TruthfulQA and HumanEval.

Does LLM evaluation support long-context benchmarking tasks?▼

Yes, LLM evaluation supports long-context benchmarking tasks alongside standard suites like HellaSwag and ARC, providing standardized metrics for extended context windows.

Why are my LLM benchmark results inconsistent across runs?▼

Inconsistent LLM benchmark results often stem from unconfigured few-shot settings, improper batch sizing, or missing caching, which disrupt reproducible metric reporting.

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