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evaluating-llms-harness

Evaluate large language models across 60+ academic benchmarks.

Updated Jun 15, 2026
Search Tags:#benchmarking#LLM#academic research#model evaluation#large language models
One-click install
npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill evaluating-llms-harness-piaoxuemoli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/Piaoxuemoli/qoobeeHermes/tree/main/hermes/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill evaluating-llms-harness-piaoxuemoli

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 framework for benchmarking large language models (LLMs) across a wide range of academic and industry-standard tasks, enabling efficient evaluation and comparison of model performance.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate LLMs on tasks like MMLU, GSM8K, and HumanEval.
  • Industry Standard: Used by EleutherAI, HuggingFace, and major labs for model quality assessment.
  • Custom Evaluation: Supports benchmarking on custom datasets or tasks.
  • Use Case: Compare the performance of different LLMs across various benchmarks to inform model selection and improve research results.

Quick Start

Run the evaluation for the MMLU benchmark with the provided HuggingFace model:

lm-evaluation-harness evaluate --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu

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 large language models on academic tasks like MMLU?▼

To benchmark large language models, you can evaluate them using a standardized suite of 60+ academic benchmarks including MMLU, GSM8K, and HumanEval to measure performance. This framework provides a comprehensive way to assess model quality across various tasks.

Can I evaluate HuggingFace models using lm-eval and vllm?▼

Yes, you can evaluate HuggingFace models by specifying the hf model type and passing model arguments like pretrained=meta-llama/Llama-2-7b-hf. This allows direct benchmarking of transformer models hosted on HuggingFace using the standardized evaluation harness.

What is the best way to compare LLM performance across multiple benchmarks?▼

The best way to compare LLM performance is using an industry-standard evaluation harness that runs models against over 60 academic benchmarks. This approach is used by major labs and organizations like EleutherAI and HuggingFace to ensure consistent model comparison.

Does this LLM evaluation framework support custom datasets?▼

Yes, the LLM evaluation framework supports custom evaluation on your own datasets or tasks beyond the standard academic benchmarks. This allows you to benchmark model performance on specialized data tailored to your specific research or application needs.

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