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evaluating-llms-harness

Benchmark LLMs across 60+ tasks using YAML-configured harness backends.

Updated Apr 20, 2026
Search Tags:#benchmarking#tasks#reproducibility#machine-learning#llm-evaluation#model-comparison
One-click install
npx skills add https://github.com/matthew-johnson/hermes-agent --skill evaluating-llms-harness-matthew-johnson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/matthew-johnson/hermes-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/matthew-johnson/hermes-agent --skill evaluating-llms-harness-matthew-johnson

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm.

What problem does it solve?

lm-evaluation-harness provides a unified, reproducible framework for benchmarking language models across 60+ tasks, enabling fair comparisons and progress tracking.

Core Features & Use Cases

  • Supports standard benchmarks (MMLU, GSM8K, HumanEval, TruthfulQA, ARC, and more) to assess model capabilities.
  • Enables model comparisons, progress tracking over time, and publication-ready result exports.
  • Works with multiple backends (HuggingFace HF, vLLM, and local API servers) and flexible task configurations.

Quick Start

Install the harness, define your task and model in a YAML configuration, and run lm_eval to start benchmarking.

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 reproducibly?▼

Standard benchmarks like MMLU, GSM8K, HumanEval, TruthfulQA, and ARC are supported natively to assess and compare model capabilities across diverse reasoning and knowledge tasks.

Can I use vLLM or HuggingFace backends for LLM evaluation?▼

No, the harness uses YAML task and model definitions for configuration, letting you specify benchmarks and execution parameters without writing custom Python evaluation scripts.

What is the best way to compare language model performance on standard benchmarks?▼

The best way to compare language model performance is using a unified harness to run 60+ standard tasks, enabling fair comparisons, progress tracking over time, and publication-ready exports.

Does evaluating LLMs with this harness support progress tracking over time?▼

Yes, evaluating LLMs with this harness supports progress tracking over time, enabling you to monitor model improvements and generate publication-ready result exports for analysis.

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