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evaluating-llms-harness

Benchmark LLMs across 60+ academic benchmarks and output standardized results.

1.0k|117|Updated Feb 26, 2026
Search Tags:#comparison#benchmark#llm-evaluation#gsm8k#humaneval#truthfulqa#hellaswag
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill evaluating-llms-harness-openlair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill evaluating-llms-harness-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Evaluates LLM performance across 60+ benchmarks to provide a standardized view of model quality and progress.

Core Features & Use Cases

  • Broad benchmark coverage across MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag, and more for consistent evaluation.
  • Cross-model comparison to identify strengths and weaknesses between model families.
  • Progress tracking & reporting for research papers, dashboards, and team collaboration.

Quick Start

Run evaluations across the supported benchmarks to generate a standardized results report for model comparison.

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 LLMs across academic datasets like GSM8K and HumanEval?▼

To benchmark LLMs, you can evaluate model performance across 60+ academic benchmarks including GSM8K, HumanEval, and TruthfulQA. This generates a standardized results schema to track training progress and compare model quality.

Can I use HuggingFace and vLLM backends to compare model performance?▼

Yes, you can compare model performance using HuggingFace, vLLM, and API backends. This allows cross-model comparison to identify strengths and weaknesses between different model families using a consistent task list.

What is the best way to generate standardized LLM evaluation reports for research?▼

The best way to generate standardized LLM evaluation reports is running evaluations across broad benchmark coverage like MMLU and HellaSwag. This provides a consistent view of model quality and outputs a standardized schema for research papers.

How does LLM evaluation work for tracking training progress across multiple benchmarks?▼

LLM evaluation tracks training progress by testing models against a consistent task list of 60+ benchmarks. This mechanism provides a standardized view of model quality over time, enabling teams to monitor improvements and generate academic reports.

Do I need lm-eval and transformers to run cross-model comparison on MMLU?▼

Yes, running cross-model comparison on benchmarks like MMLU requires dependencies including lm-eval, transformers, and vllm. These frameworks support the backend infrastructure needed to execute evaluations and output standardized results.

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