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evaluating-llms-harness

Evaluate large language models across standardized academic benchmarks with HuggingFace and vLLM backends.

2|Updated Apr 12, 2026
Search Tags:#llm#benchmarking#vllm#gsm8k#mmlu#humaneval#lm-eval
One-click install
npx skills add https://github.com/Clay-HHK/claude-config --skill evaluating-llms-harness-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/Clay-HHK/claude-config/tree/main/skills/AI-research-SKILLs/11-evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/Clay-HHK/claude-config --skill evaluating-llms-harness-clay-hhk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Provides a reproducible, standardized way to evaluate and compare large language models across academic and industry benchmarks so teams can measure model quality, track training progress, and report results consistently.

Core Features & Use Cases

  • Standardized Benchmarks: Run 60+ tasks including MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA, ARC, and more with consistent prompts and metrics.
  • Multi-backend Support: Evaluate models via HuggingFace, vLLM, and API-based providers for local and remote model comparisons.
  • Research & Production Workflows: Use for paper baselines, model comparisons across checkpoints, automated periodic evaluation, and distributed/multi-GPU runs with exportable JSON results.

Quick Start

Evaluate meta-llama/Llama-2-7b-hf on MMLU, GSM8K, and HellaSwag and save the output to results/ using the lm-eval harness.

Frequently Asked Questions about evaluating-llms-harness

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

FAQPage Schema
How do I evaluate large language models on standardized academic benchmarks?▼

You can evaluate large language models on standardized academic benchmarks by applying consistent prompts and metrics to tasks like MMLU, GSM8K, and HumanEval. This supports reproducible few-shot and zero-shot evaluations across local and remote models.

What is the best way to compare LLM performance across multiple checkpoints?▼

Comparing LLM performance across checkpoints involves running multi-model evaluations on standardized tasks and exporting the results to JSON. This allows teams to measure model quality and track training progress consistently over time.

Can I use vLLM and HuggingFace backends for batched LLM evaluations?▼

Yes, you can use vLLM and HuggingFace backends to run batched LLM evaluations for local models. The system also supports distributed multi-GPU configurations and API-based providers for evaluating remote models.

Does the lm-eval harness support zero-shot and few-shot testing for research papers?▼

Yes, the lm-eval harness supports reproducible zero-shot and few-shot testing to establish research paper baselines. It provides standardized evaluation across 60+ tasks including TruthfulQA, ARC, and HellaSwag to ensure consistent reporting.

How do I export LLM benchmark results to JSON for automated reporting?▼

You export LLM benchmark results to JSON by completing the evaluation runs across the specified tasks. The output provides standardized metrics suitable for automated periodic evaluation and consistent quality tracking.

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