evaluating-llms-harness

Name: evaluating-llms-harness
Availability: InStock
Author: mark

Evaluate LLMs across 60+ benchmarks using the lm_eval CLI.

31|3|Updated May 7, 2026

Search Tags:#llm #evaluation #benchmark #lm-evaluation-harness #gsm8k #mmlu #humaneval

Ready to run

npx skills add https://github.com/markwang2658/hermes-windows-native --skill evaluating-llms-harness-markwang2658

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/markwang2658/hermes-windows-native/tree/main/hermes-agent/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/markwang2658/hermes-windows-native --skill evaluating-llms-harness-markwang2658

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

lm-evaluation-harness provides a unified framework to benchmark LLMs across 60+ tasks (MMLU, GSM8K, HumanEval, etc.), enabling reproducible comparisons and integrated metrics.

Core Features & Use Cases

Standardized task suite across 60+ benchmarks (MMLU, GSM8K, HumanEval, TruthfulQA, ARC, etc.)
Backend interoperability with HuggingFace, vLLM, and API-based models
Reproducible results with structured outputs and metrics

Quick Start

Run lm_eval against your model and tasks to reproduce standard benchmarks.

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 LLMs across multiple tasks like MMLU and GSM8K?▼

To benchmark LLMs across MMLU and GSM8K, you can use a standardized evaluation harness that provides unified prompts, scoring, and aggregation. It supports running over 60 tasks to enable reproducible academic and industry model comparisons.

Can I evaluate API-based models and local HuggingFace models with the same benchmark harness?▼

Yes, you can evaluate API-based models and local HuggingFace models with the same benchmark harness. The framework supports backend interoperability across HuggingFace, vLLM, and API-based models for unified scoring.

What is the best way to reproduce published LLM benchmarking results?▼

The best way to reproduce published LLM benchmarking results is using a unified evaluation harness with standardized task suites. It provides structured outputs and metrics, allowing you to run the CLI against your model and task list.

Does the lm-evaluation-harness support coding benchmarks like HumanEval?▼

Yes, the lm-evaluation-harness supports coding benchmarks like HumanEval. It includes a standardized task suite covering over 60 benchmarks, including MMLU, GSM8K, HumanEval, TruthfulQA, and ARC.

How do I run standardized LLM evaluations using a command line interface?▼

You run standardized LLM evaluations by installing dependencies with pip and using the lm_eval CLI. Execute the CLI against your specified model and task list to generate structured outputs and aggregated metrics.

What tasks are included in a standardized LLM evaluation harness?▼

A standardized LLM evaluation harness includes a task suite of over 60 benchmarks. This covers standardized prompts and scoring for datasets like MMLU, GSM8K, HumanEval, TruthfulQA, and ARC.