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evaluating-llms-harness

Evaluates LLMs on standardized benchmarks including MMLU, GSM8K, and HumanEval.

Updated Jun 26, 2026
Search Tags:#benchmarking#machine learning#LLM#performance metrics#model evaluation
One-click install
npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill evaluating-llms-harness-nitish-gitbit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/NITISH-gitbit/hermes-custom/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/NITISH-gitbit/hermes-custom --skill evaluating-llms-harness-nitish-gitbit

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows users to benchmark large language models (LLMs) across a variety of standardized benchmarks, providing a comprehensive evaluation of model quality.

Core Features & Use Cases

  • Benchmarking: Evaluate LLMs on tasks such as MMLU, GSM8K, and HumanEval.
  • Model Quality: Track training progress and compare model performance.
  • Industry Standard: Used by major labs and industry leaders for evaluating model quality.
  • Customizable: Configure benchmarking tasks and model configurations to fit specific needs.

Quick Start

Evaluate the performance of the model 'llama2-7b' on the MMLU benchmark using the following command:

lm-evaluation-harness --model hf --model_args pretrained=llama2:7b --tasks mmlu --num_fewshot 5 --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 an LLM on the MMLU dataset?▼

You benchmark an LLM on MMLU by configuring the lm-evaluation-harness with the model arguments, specifying the mmlu task, and executing the command to generate standardized performance metrics.

What is the best way to evaluate large language model performance across standardized tasks?▼

Evaluating large language model performance is best achieved by running standardized benchmarks like MMLU, GSM8K, and HumanEval to comprehensively measure and compare model quality.

Do I need vllm and transformers to evaluate LLMs?▼

Yes, you need vllm and transformers as dependencies to execute model-specific tasks and generate evaluation metrics when benchmarking large language models.

Can I customize benchmarking tasks for my own model evaluation?▼

Yes, you can customize benchmarking tasks and model configurations to fit specific needs, allowing you to track training progress and compare model performance.

How does evaluating LLMs with lm-eval work for GSM8K and HumanEval?▼

Evaluating LLMs with lm-eval works by running the model against standardized datasets like GSM8K and HumanEval to generate metrics for model comparison and quality tracking.

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