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evaluating-llms-harness

Benchmarks LLM performance across standardized academic tasks using lm-evaluation-harness on HuggingFace, vLLM and API backends.

Updated Apr 25, 2026
Search Tags:#benchmarking#benchmark#vllm#llm-evaluation#gsm8k#mmlu#human-eval
One-click install
npx skills add https://github.com/danmartinez78/echo-ghost-backup --skill evaluating-llms-harness-danmartinez78
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/danmartinez78/echo-ghost-backup/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/danmartinez78/echo-ghost-backup --skill evaluating-llms-harness-danmartinez78

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates and standardizes the evaluation of language models across 60+ academic benchmarks, enabling reproducible comparisons and progress tracking.

Core Features & Use Cases

  • Comprehensive Benchmark Coverage: Supports 60+ tasks including MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag.
  • Multi-backend Flexibility: Works with HuggingFace, vLLM, and API-based models.
  • Reproducible Evaluation: Consistent prompts, seeds, and scoring for publication-quality results.
  • Workflow Integration: Integrates into CI and research pipelines for ongoing model evaluation.

Quick Start

Run a full benchmark by specifying a model and tasks and letting the harness compute baseline metrics.

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 performance on academic tasks like MMLU and GSM8K?▼

You can benchmark LLM performance across standardized academic tasks by specifying a model and tasks like MMLU and GSM8K, allowing the harness to compute baseline metrics automatically. It supports 60+ tasks for reproducible evaluation.

Can I use vLLM and HuggingFace models for LLM evaluation?▼

Yes, LLM evaluation supports multi-backend flexibility, allowing you to test models via HuggingFace, vLLM, and API-based backends. This ensures reproducible benchmarking across different model hosting environments.

What is the best way to run reproducible LLM benchmarking for research?▼

Reproducible LLM benchmarking is achieved by using consistent prompts, seeds, and scoring via lm-evaluation-harness. This enforces YAML frontmatter metadata and supports publication-quality results across standardized tasks.

How do I integrate LLM evaluation into a CI pipeline?▼

You can integrate LLM evaluation into CI and research pipelines for ongoing model evaluation by running the harness with specified models and tasks, automating benchmark checks across supported backends.

Does LLM benchmarking support tasks beyond MMLU and HumanEval?▼

Yes, LLM benchmarking supports over 60 tasks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag. This comprehensive coverage enables broad academic performance comparisons.

What metadata is required to evaluate LLMs across standardized tasks?▼

Evaluating LLMs requires YAML frontmatter metadata containing a name and description. This ensures standardized documentation and reproducible benchmarking results across all 60+ supported tasks.

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