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evaluating-llms-harness

Evaluate large language models on standardized benchmarks using Python libraries.

1|Updated Jun 23, 2026
Search Tags:#benchmarking#LLM#model evaluation#training tracking#API evaluation
One-click install
npx skills add https://github.com/whichguy/hermes-skills-marketplace --skill evaluating-llms-harness-whichguy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/whichguy/hermes-skills-marketplace/tree/main/skills/evaluating-llms-harness
Command: npx skills add https://github.com/whichguy/hermes-skills-marketplace --skill evaluating-llms-harness-whichguy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive framework for benchmarking large language models (LLMs) across various academic and industry-standard benchmarks, enabling users to assess model quality, compare models, and track training progress.

Core Features & Use Cases

  • Benchmarking: Evaluate LLMs on 60+ standardized benchmarks like MMLU, GSM8K, and HumanEval.
  • Model Comparison: Compare multiple models side-by-side using predefined benchmark suites.
  • Training Progress Tracking: Monitor model performance at different training stages.
  • API Evaluation: Support for evaluating OpenAI, Anthropic, and other API-based models.

Quick Start

Run the benchmark for the 'gsm8k' task on the 'llama2-7b' model:

lm-evaluation-harness evaluate gsm8k llama2-7b

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 on standardized tasks like MMLU and GSM8K?▼

You can evaluate API-based models from providers like OpenAI and Anthropic by configuring the benchmarking framework to target their endpoints. This allows you to assess API model quality and compare them side-by-side with local models using the same standardized tests.

Can I evaluate OpenAI and Anthropic API models using lm-eval?▼

You can evaluate API-based models from providers like OpenAI and Anthropic by configuring the benchmarking framework to target their endpoints. This allows you to assess API model quality and compare them side-by-side with local models using the same standardized tests.

Do I need vllm and transformers installed to run model evaluation?▼

You can track training progress by running the benchmark tests at different training stages. This monitors model performance across predefined benchmark suites, enabling you to compare multiple models side-by-side and observe quality improvements over time.

How do I track LLM training progress across different stages?▼

You can track training progress by running the benchmark tests at different training stages. This monitors model performance across predefined benchmark suites, enabling you to compare multiple models side-by-side and observe quality improvements over time.

What is the best way to compare multiple LLMs side-by-side?▼

To run a benchmark, execute a command specifying the task and model, such as evaluating the gsm8k task on the llama2-7b model. This triggers the evaluation harness to run the standardized test and generate a detailed performance report.

How to run a benchmark test on a local llama2-7b model?▼

To run a benchmark, execute a command specifying the task and model, such as evaluating the gsm8k task on the llama2-7b model. This triggers the evaluation harness to run the standardized test and generate a detailed performance report.

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