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evaluating-llms-harness

Benchmarks LLM academic-task performance using lm-eval harnesses across multiple platforms and metrics.

9|3|Updated May 18, 2026
Search Tags:#llm#benchmarking#evaluation#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/jordanhubbard/mac --skill evaluating-llms-harness-jordanhubbard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/jordanhubbard/mac/tree/main/src/mac/_hermes/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/jordanhubbard/mac --skill evaluating-llms-harness-jordanhubbard

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive benchmarking solution for large language models (LLMs), allowing users to evaluate and compare models across a wide range of tasks and metrics.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate LLMs on tasks like MMLU, GSM8K, HumanEval, and more.
  • Standardized Metrics: Use standardized prompts and metrics for consistent evaluation.
  • Industry Standard: Widely used by EleutherAI, HuggingFace, and major labs.
  • Supports HuggingFace, vLLM, APIs: Evaluate models from various sources and platforms.
  • Use Case: Compare the performance of two LLMs on the MMLU benchmark to determine which one is better suited for a specific application.

Quick Start

Evaluate the MMLU benchmark for the 'meta-llama/Llama-2-7b-hf' model using the following command:

lm-evaluation-harness --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --output_path 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 an LLM on MMLU and GSM8K tasks?▼

To benchmark an LLM on MMLU and GSM8K, use the lm-evaluation-harness with the HuggingFace model name and specify the tasks. This provides standardized metrics across 60+ academic benchmarks for consistent model evaluation.

Can I evaluate models served by vLLM using this benchmarking harness?▼

Yes, you can evaluate models served by vLLM. The harness supports HuggingFace, vLLM, and APIs, allowing you to benchmark LLM performance across diverse platforms using standardized prompts and metrics.

What is the best way to compare two LLMs using standardized metrics?▼

The best way to compare two LLMs is running both through the lm-evaluation-harness on benchmarks like HumanEval. This yields standardized metrics, letting you directly compare performance to determine the best model for your application.

Do I need the transformers library to run LLM evaluations?▼

Yes, you need the transformers library along with lm-eval and vllm. These Python dependencies are required to load models and execute standardized evaluation tasks across the 60+ supported academic benchmarks.

Why use standardized prompts for LLM benchmarking?▼

Standardized prompts ensure consistent LLM evaluation across academic tasks like MMLU and GSM8K. This industry-standard approach, used by HuggingFace and EleutherAI, guarantees reliable model comparison metrics.

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