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llm-evaluation

Runs lm-evaluation-harness benchmarks for LLMs across multiple configurable backends and tasks.

Updated Aug 23, 2026
Search Tags:#benchmarking#lm-evaluation-harness#llm-evaluation#evaluation-pipeline#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/tylertitsworth/skills --skill llm-evaluation-tylertitsworth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/tylertitsworth/skills/tree/main/llm-evaluation
Command: npx skills add https://github.com/tylertitsworth/skills --skill llm-evaluation-tylertitsworth

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Evaluate LLMs with lm-evaluation-harness and standard benchmarks to enable consistent, reproducible model evaluation across multiple backends and task sets.

Core Features & Use Cases

  • Standard benchmarking with 60+ tasks (MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA, etc.) enabling comprehensive model assessment.
  • Backend versatility: supports HuggingFace local models, vLLM, OpenAI-compatible endpoints, and API-based configurations.
  • Custom evaluation pipelines and tasks, including LLM-as-judge evaluations, wandb integration, and scalable orchestration.

Quick Start

Run the lm-evaluation-harness with your chosen backend and a task set to obtain baseline model benchmarks.

Frequently Asked Questions about llm-evaluation

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

FAQPage Schema
How do I benchmark LLMs with standard tasks like MMLU and GSM8K?▼

You can benchmark LLMs using a standard evaluation workflow that supports over 60 tasks including MMLU, GSM8K, and HumanEval, ensuring consistent and reproducible model assessment.

Can I evaluate a local HuggingFace model and an OpenAI-compatible endpoint with the same pipeline?▼

Yes, you can evaluate local HuggingFace models, vLLM, and OpenAI-compatible endpoints using the same standard, configurable benchmarking pipeline for consistent model selection and deployment evaluation.

What is the best way to run reproducible LLM evaluations across different backends?▼

The best way to run reproducible LLM evaluations is using a standard harness that supports backend configurations for HuggingFace and vLLM, providing consistent results for research and deployment pipelines.

How do I customize evaluation pipelines for LLM-as-judge assessments?▼

You can customize evaluation pipelines by defining custom tasks and utilizing LLM-as-judge evaluations, integrating with wandb for tracking to achieve scalable orchestration and tailored model assessment.

Does LLM benchmarking support HumanEval for code generation tasks?▼

Yes, LLM benchmarking supports HumanEval alongside other tasks like HellaSwag and TruthfulQA, enabling comprehensive model assessment across reasoning, factual accuracy, and code generation benchmarks.

When do I need to use a standard evaluation harness for model selection?▼

You need a standard evaluation harness for model selection when comparing multiple backends requires consistent task configurations and reproducible benchmarking results to validate deployment readiness.

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