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evaluating-llms-harness

Evaluate large language models on benchmarks like MMLU and GSM8K.

Updated Mar 19, 2026
Search Tags:#benchmarking#evaluation#model comparison#performance tracking#GSM8K#language models
One-click install
npx skills add https://github.com/sunghwanism/TripVlog_gogle --skill evaluating-llms-harness-sunghwanism
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/sunghwanism/TripVlog_gogle/tree/main/.claude/commands/evaluating-llms-harness
Command: npx skills add https://github.com/sunghwanism/TripVlog_gogle --skill evaluating-llms-harness-sunghwanism

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 enables comprehensive evaluation and benchmarking of large language models across a wide range of academic and industry-standard benchmarks, simplifying performance tracking and model comparison.

Core Features & Use Cases

  • Benchmarking: Evaluate models on tasks such as MMLU, GSM8K, HumanEval, and more, to measure accuracy and capabilities.
  • Comparative Analysis: Compare different models' performances using standardized datasets and configurations.
  • Progress Tracking: Automate the process of tracking training or fine-tuning progress via periodic evaluations to monitor improvements over time.

Quick Start

Use this Skill to benchmark your language model on the GSM8K math dataset with a simple command.

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 language models on established datasets like GSM8K?▼

To benchmark language models on datasets like GSM8K, you can use this Skill to evaluate model accuracy across 60+ academic and industry-standard benchmarks using simple command-line scripts.

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

Comparing large language model performance is done by evaluating multiple models on standardized datasets like MMLU and HumanEval, allowing you to directly measure and track accuracy differences for research or monitoring.

Do I need vllm and transformers installed to run model evaluation benchmarks?▼

Yes, you need to install specific Python packages including vllm and transformers to enable flexible integration and execution across different model backends during the language model evaluation process.

Can I use lm-eval to track fine-tuning progress over time?▼

Yes, you can automate progress tracking by running periodic evaluations with lm-eval to monitor your large language model's performance improvements during training or fine-tuning cycles.

Does this benchmarking approach support both academic research and industry model comparisons?▼

Yes, this benchmarking approach supports both academic research and industry model comparison by providing a standardized evaluation framework across a wide range of established language model benchmarks.

How does evaluating LLMs on benchmarks handle different model backends?▼

Evaluating LLMs handles different model backends by requiring Python packages like transformers and vllm, which provide the flexible integration needed to execute benchmarking tasks across various environments.

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