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evaluating-llms-harness

Benchmark large language models on academic and reasoning tasks with standardized prompts.

Updated May 5, 2026
Search Tags:#llm#benchmarking#evaluation#gsm8k#mmlu#model quality
One-click install
npx skills add https://github.com/iani-kuli/harness_bro --skill evaluating-llms-harness-iani-kuli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/iani-kuli/harness_bro/tree/main/.claude/skills/ported/evaluating-llms-harness
Command: npx skills add https://github.com/iani-kuli/harness_bro --skill evaluating-llms-harness-iani-kuli

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the challenge of inconsistently evaluating large language models by providing a standardized, reproducible framework for benchmarking model performance across academic and reasoning tasks.

Core Features & Use Cases

  • Standardized Benchmarking: Run evaluations on over 60 academic datasets including MMLU, GSM8K, and HumanEval.
  • Training Progress Tracking: Automate periodic evaluations during model training to monitor performance improvements.
  • Model Comparison: Generate comparative tables to evaluate different model architectures or checkpoints side-by-side.

Quick Start

Run the lm-evaluation-harness on a specified HuggingFace model using the mmlu and gsm8k tasks to generate a performance report.

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 LLM performance across academic datasets like MMLU and GSM8K?▼

To benchmark LLM performance, run standardized evaluations across over 60 academic and reasoning datasets including MMLU, GSM8K, and HumanEval using the lm-eval library to generate reproducible performance reports.

Can I track model training progress by automating periodic LLM evaluations?▼

Yes, you can track training progress by automating periodic LLM evaluations during model training. This monitors performance improvements across standardized academic tasks to ensure consistent quality assessment.

Do I need GPU support to run large language model evaluations with lm-eval?▼

Yes, you need a compute environment with GPU support for efficient inference when running large language model evaluations. The lm-eval library requires this setup to process standardized prompts across multiple academic datasets.

What's the best way to compare different model architectures or checkpoints side-by-side?▼

The best way to compare model architectures is by generating comparative tables that evaluate different models or checkpoints side-by-side using standardized metrics from the lm-evaluation-harness framework.

Can I use vllm to benchmark API-based and open-source models together?▼

Yes, the benchmarking framework supports comparative analysis of both open-source and API-based models. It uses vllm and the lm-eval library to assess model quality across standardized academic and reasoning tasks.

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