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evaluating-llms-harness

Benchmark large language models against standardized academic tasks.

Updated May 14, 2026
Search Tags:#model evaluation#vllm#gsm8k#mmlu#humaneval#distributed inference#llm benchmarking
One-click install
npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill evaluating-llms-harness-sethypagna
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/SethyPagna/Secretary-Jarvis/tree/main/src/capabilities/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill evaluating-llms-harness-sethypagna

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It removes the guesswork from LLM evaluation by providing a single, reproducible harness for comparing models on standardized benchmarks and tracking progress over time.

Core Features & Use Cases

  • Standard Benchmarks: Run common academic tasks such as MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA, ARC, and LongBench.
  • Model Comparison: Compare HuggingFace checkpoints, vLLM deployments, OpenAI or Anthropic APIs, and local OpenAI-compatible servers with the same metrics.
  • Production Workflows: Use it to monitor training checkpoints, validate releases, and generate repeatable reports for research or internal model audits.

Quick Start

Run the lm-evaluation-harness skill on your target model and benchmark suite to produce a reproducible score report for tasks like MMLU, GSM8K, or HumanEval.

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 LLM models against standardized academic tasks?▼

To benchmark LLMs against standardized tasks, you can use this evaluation harness to run models on benchmarks like MMLU, GSM8K, and HumanEval, producing a reproducible score report for research or audits.

Can I compare HuggingFace checkpoints and OpenAI APIs using the same evaluation metrics?▼

Yes, you can compare HuggingFace checkpoints, vLLM deployments, OpenAI or Anthropic APIs, and local OpenAI-compatible servers using the same standardized metrics for consistent model comparison.

How do I run distributed multi-GPU inference for LLM benchmarking?▼

You can run distributed multi-GPU inference for LLM benchmarking by configuring tensor parallelism and batch sizing within the evaluation harness, supporting scalable execution across supported model backends.

Does this LLM evaluation harness support few-shot prompting on custom task definitions?▼

Yes, the LLM evaluation harness supports configurable few-shot prompting and custom task definitions, allowing you to tailor standardized benchmarks like TruthfulQA or LongBench to specific evaluation needs.

What is the best way to track training regressions in large language models?▼

Tracking training regressions in large language models is best handled by running this evaluation harness on training checkpoints to generate repeatable reports, validating model progress over time.

What standard benchmarks are available for evaluating large language models?▼

Standard benchmarks available for evaluating large language models include MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA, ARC, and LongBench, covering diverse academic and reasoning tasks.

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