evaluating-llms-harness

Benchmark LLMs across standard tasks using lm-evaluation-harness.

Updated Mar 18, 2026

Search Tags:#llm #evaluation #benchmark #gsm8k #mmlu #humaneval #truthfulqa

Ready to run

npx skills add https://github.com/tadod12/fraud-detection-research --skill evaluating-llms-harness-tadod12

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/11-evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill evaluating-llms-harness-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm.

What problem does it solve?

Automates rigorous benchmarking of large language models across a broad set of standardized tasks, enabling reproducible model comparisons and progress tracking.

Core Features & Use Cases

A curated suite of 60+ evaluation benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with support for HuggingFace, vLLM, and API-based models.
Ideal for research teams benchmarking model quality, publishing results, or monitoring progress across iterations, including model releases and paper replication.
Generates standardized metrics and comparison reports to facilitate objective model evaluation and decision-making.

Quick Start

Install the lm-evaluation-harness package, configure your model and tasks, and run a benchmark to obtain comparable 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 LLMs across standard tasks like MMLU and GSM8K?▼

You can benchmark LLMs across standard tasks by configuring your model and tasks within the lm-evaluation-harness interface. This Skill automates running evaluations for benchmarks like MMLU and GSM8K, generating standardized metrics for objective comparison.

Can I evaluate API-based models using lm-evaluation-harness?▼

Yes, you can evaluate API-based models. The Skill supports benchmarking for HuggingFace, vLLM, and OpenAI/Anthropic-style API endpoints, allowing you to generate comparable evaluation metrics across different model hosting environments.

What is the best way to run HumanEval and TruthfulQA benchmarks for a research model?▼

The best way to run HumanEval and TruthfulQA benchmarks is using this Skill's unified interface built on lm-evaluation-harness. It automates rigorous evaluations for research models, generating standardized metrics and comparison reports to facilitate objective evaluation.

Does this LLM evaluation tool support vLLM and HuggingFace transformers?▼

Yes, this LLM evaluation tool supports both vLLM and HuggingFace transformers. It integrates these dependencies to provide a unified interface for benchmarking models across 60+ academic tasks and generating reproducible comparison reports.

How do I get reproducible model comparisons across multiple LLM benchmarks?▼

You get reproducible model comparisons by running this Skill to apply standardized evaluation benchmarks via lm-evaluation-harness. It automates the process across 60+ tasks, generating standardized metrics and comparison reports to facilitate objective decision-making.