evaluating-llms-harness

Name: evaluating-llms-harness
Availability: InStock
Author: rnben

Evaluate LLMs across 60+ benchmarks using the lm-eval framework.

1|Updated Apr 18, 2026

Search Tags:#llm #evaluation #benchmark #gsm8k #human-eval #lm-eval

Ready to run

npx skills add https://github.com/rnben/hermes-skills --skill evaluating-llms-harness-rnben

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Evaluates LLMs across 60+ benchmarks to provide standardized, comparable performance metrics for model selection and research.

Core Features & Use Cases

Benchmark a wide spectrum of tasks including MMLU, GSM8K, HumanEval, TruthfulQA, ARC, and more
Compare models, track progress, and report academic results with reproducible prompts and settings
Integrate with HuggingFace, vLLM, and API backends for flexible experimentation

Quick Start

Run the lm-evaluation-harness against your model to begin benchmarking across the supported 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 multiple tasks like GSM8K and HumanEval?▼

You can benchmark LLMs across tasks like GSM8K and HumanEval using the lm-eval framework to configure tasks, models, and few-shot settings, which outputs structured and comparable performance results.

Can I evaluate models deployed via HuggingFace, vLLM, and API backends?▼

Yes, you can evaluate models via HuggingFace, vLLM, and API backends. The framework integrates with transformers and vllm to enable flexible experimentation across these deployment environments.

What is the best way to compare LLM performance using standardized metrics?▼

The best way to compare LLM performance is evaluating models across 60+ benchmarks, which provides standardized, reproducible results with consistent prompts and few-shot settings for accurate selection.

How do I configure few-shot settings for LLM evaluation tasks?▼

You configure few-shot settings for LLM evaluation tasks through the provided CLI workflow, which allows you to specify tasks, models, and few-shot parameters before running the lm-evaluation-harness.

Does the lm-eval framework support benchmarking custom API deployments?▼

Yes, the lm-eval framework supports benchmarking custom API deployments. It integrates with API backends alongside HuggingFace and vLLM to enable flexible experimentation and academic reporting.

When do I need to use a standardized benchmark harness for LLM evaluation?▼

You need a standardized benchmark harness for LLM evaluation when comparing model quality, tracking progress, or reporting academic results, ensuring reproducible prompts and settings across 60+ benchmarks.