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evaluating-llms-harness

Run 60+ academic benchmarks on LLMs with lm-evaluation-harness.

Updated May 2, 2026
Search Tags:#benchmarking#llm evaluation#vllm#gsm8k#mmlu#human eval#model quality tracking
One-click install
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill evaluating-llms-harness-qcmuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/11-evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill evaluating-llms-harness-qcmuu

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you evaluate and compare large language models using a standardized, widely adopted benchmark harness.

Core Features & Use Cases

  • Standardized academic benchmarks: Runs 60+ tasks such as MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag with consistent prompts and metrics.
  • Multi-backend support: Benchmarks both open models (HuggingFace) and high-throughput inference setups (vLLM), plus API-backed evaluation via supported backends.
  • Reproducible research workflows: Produces comparable results for model releases and supports tracking evaluation across training checkpoints.

Use case: You want to release a new model version and report academic-style metrics across reasoning, code, and truthfulness benchmarks in a single repeatable run.

Quick Start

Run a 5-shot evaluation on MMLU, GSM8K, and HellaSwag for a HuggingFace model by executing the lm_eval command:
lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k,hellaswag --device cuda:0 --batch_size 8

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 tasks?▼

To benchmark LLMs on MMLU and GSM8K, run the lm_eval command with specified model arguments and tasks. This evaluates models across standardized academic benchmarks to quantify model quality and track improvements over time.

Can I use vLLM for high-throughput LLM evaluation?▼

Yes, you can use vLLM for high-throughput LLM evaluation. The Skill supports both HuggingFace open models and vLLM inference backends to run tasks like HumanEval and TruthfulQA with consistent metrics.

How do I evaluate training checkpoints across reasoning and code benchmarks?▼

You evaluate training checkpoints by executing a single repeatable lm_eval run across 60+ academic tasks. This produces comparable benchmark reporting for reasoning, code, and truthfulness to validate model releases.

Does lm-evaluation-harness support HuggingFace models for academic benchmarking?▼

Yes, lm-evaluation-harness supports HuggingFace models for academic benchmarking. You can configure the hf model backend with pretrained arguments, device mapping, and batch size to run tasks like HellaSwag.

What's the best way to compare research models using standardized prompts?▼

The best way to compare research models is running standardized academic benchmarks with consistent prompts and metrics. The harness evaluates models across 60+ tasks like MMLU and HumanEval to ensure reproducible research workflows.

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