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evaluating-llms-harness

Benchmark large language models on academic suites like MMLU and GSM8K.

Updated May 5, 2026
Search Tags:#benchmarking#llm evaluation#vllm#gsm8k#mmlu#human-eval#reproducible results
One-click install
npx skills add https://github.com/Z43L/zeus-agent --skill evaluating-llms-harness-z43l
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/Z43L/zeus-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/Z43L/zeus-agent --skill evaluating-llms-harness-z43l

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the guesswork of evaluating language models by providing a standardized way to benchmark performance across widely used academic tasks, producing comparable results.

Core Features & Use Cases

  • Unified LLM benchmarking: Runs a single evaluation workflow across 60+ benchmarks such as MMLU, GSM8K, HumanEval, TruthfulQA, and HellaSwag.
  • Flexible model backends: Supports HuggingFace, vLLM, and API-based models via the harness interfaces.
  • Reproducible reporting: Writes structured outputs (e.g., JSON results) suitable for tracking runs over time and comparing model versions.
  • Use case: When releasing or fine-tuning a model, evaluate against a consistent task suite to track regressions, validate improvements, and report academic-style metrics.

Quick Start

Run MMLU, GSM8K, and HellaSwag for a HuggingFace model using lm-eval on your GPU setup.

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 against standard academic tasks like MMLU and GSM8K?▼

To benchmark an LLM against MMLU and GSM8K, you use lm-evaluation-harness to select standardized tasks, configure target model arguments, and run evaluations to generate comparable machine-readable metrics.

Can I run LLM evaluation on local HuggingFace models and vLLM backends?▼

Yes, you can run LLM evaluation on local HuggingFace models and vLLM backends by configuring the target model arguments and device batching within the lm-evaluation-harness framework to produce reproducible results.

What is the best way to compare model quality after fine-tuning a large language model?▼

The best way to compare model quality after fine-tuning is to benchmark the model against a consistent task suite like TruthfulQA and HellaSwag to track regressions, validate improvements, and report academic-style metrics.

Does lm-evaluation-harness support evaluating API-compatible endpoints?▼

Yes, lm-evaluation-harness supports evaluating API-compatible endpoints, allowing you to benchmark remote models alongside local HuggingFace and vLLM backends within the same standardized evaluation workflow.

How do I get reproducible results when benchmarking large language models?▼

You get reproducible results when benchmarking large language models by using lm-evaluation-harness to write structured JSON outputs, which are suitable for tracking runs over time and comparing model versions consistently.

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