evaluating-llms-harness

Run language model evaluations across 60+ benchmarks with HuggingFace, vLLM, and API backends.

2|Updated Apr 25, 2026

Search Tags:#benchmarking #llm-evaluation #evaluation-harness #gsm8k #mmlu #humaneval #ml-evaluation

Ready to run

npx skills add https://github.com/AlexiosBluffMara/mercury --skill evaluating-llms-harness-alexiosbluffmara

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/AlexiosBluffMara/mercury/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/AlexiosBluffMara/mercury --skill evaluating-llms-harness-alexiosbluffmara

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Benchmarking and comparing LLMs across a comprehensive suite of benchmarks to provide reproducible, industry-standard results.

Core Features & Use Cases

Supports 60+ evaluation tasks including MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag
Enables standardized model benchmarking, cross-model comparisons, and progress tracking
Provides APIs to run, aggregate metrics, and export results for reporting

Quick Start

Run a standard evaluation against a chosen model to obtain benchmark results across MMLU, GSM8K, HumanEval, and other tasks.

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?▼

To benchmark LLMs across MMLU, GSM8K, and HumanEval, this evaluation harness coordinates multi-task execution and aggregates metrics for reproducible model comparison. It supports 60+ industry-standard tasks to provide comprehensive evaluation results.

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

Yes, you can use vLLM, HuggingFace, and API backends for LLM evaluation. The harness supports these diverse deployment scenarios to ensure reproducible results across different model serving environments.

What is the best way to compare language models reproducibly?▼

The best way to compare language models reproducibly is using a standardized evaluation harness. It executes 60+ benchmark tasks, aggregates performance metrics, and exports results for cross-model comparison and progress tracking.

Does this evaluation harness support TruthfulQA and HellaSwag benchmarks?▼

Yes, this evaluation harness supports TruthfulQA, HellaSwag, and over 60 other benchmark tasks. It enables standardized model benchmarking by coordinating multi-task execution and metric aggregation across these comprehensive evaluation suites.

How do I export benchmark results for model comparison reporting?▼

You can export benchmark results using the harness APIs that run evaluations, aggregate metrics, and generate reports. This enables standardized model benchmarking, cross-model comparisons, and progress tracking across 60+ tasks.