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lm-evaluation-harness

Benchmark language models across multiple datasets and tasks.

2|1|Updated May 10, 2026
Search Tags:#performance#benchmarking#evaluation#model comparison#ML#language models
One-click install
npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill lm-evaluation-harness-zli5460
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lm-evaluation-harness
Source: https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill lm-evaluation-harness-zli5460

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, datasets, and includes scripts (resource) and references (resource) components.

What problem does it solve?

It enables consistent and comparable evaluation of language models across a wide range of academic and industry benchmarks.

Core Features & Use Cases

  • Benchmarking: Evaluate models on tasks like MMLU, GSM8K, HumanEval, and others.
  • Model Comparison: Compare different models' performance reliably.
  • Progress Tracking: Monitor training improvements over time with automated workflows.
  • Use Case: Researchers can measure the quality of new language models before publication or deployment, ensuring reproducibility and standardization.

Quick Start

Use this Skill to evaluate your model on the MMLU benchmark with a simple command.

Frequently Asked Questions about lm-evaluation-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate language models on MMLU and other academic benchmarks?▼

To evaluate language models on MMLU and other benchmarks, you can run automated evaluation scripts designed for standardized, reproducible model comparison across multiple datasets.

What is the best way to compare language model performance reliably?▼

Comparing language model performance reliably requires standardized benchmarking across tasks like GSM8K and HumanEval, ensuring consistent metrics and reproducible evaluation workflows for different model types.

Do I need Python and transformers to run language model evaluations?▼

Yes, you need a Python environment with dependencies like transformers and datasets installed to execute language model evaluation scripts seamlessly and ensure accurate benchmarking results.

Can I track training improvements for language models using automated workflows?▼

You can track training improvements by running automated benchmarking workflows that monitor language model performance over time, providing standardized metrics to measure quality before deployment or publication.

Does lm-evaluation-harness support both industry and academic evaluations?▼

lm-evaluation-harness supports comprehensive benchmarking for both industry and academic evaluations, facilitating consistent evaluation of language models across a wide range of standardized datasets and tasks.

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