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evaluating-llms-harness

Benchmark large language models across academic datasets like MMLU, GSM8K, and HumanEval.

Updated May 4, 2026
Search Tags:#llm#benchmarking#evaluation#gsm8k#mmlu#model quality
One-click install
npx skills add https://github.com/InverterNetwork/hermes-agent --skill evaluating-llms-harness-inverternetwork
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/InverterNetwork/hermes-agent/tree/main/skills/mlops/evaluation/evaluating-llms-harness
Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill evaluating-llms-harness-inverternetwork

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of objectively measuring and comparing the performance of Large Language Models across a wide range of academic and reasoning benchmarks, ensuring reproducible and standardized results.

Core Features & Use Cases

  • Standardized Benchmarking: Evaluate models on over 60 academic datasets including MMLU, GSM8K, and HumanEval.
  • Training Progress Tracking: Automate periodic evaluations during model training to monitor performance improvements.
  • Use Case: A developer training a custom model can use this Skill to run a suite of reasoning benchmarks after every training epoch to generate a learning curve and verify model quality against industry standards.

Quick Start

Use the evaluating-llms-harness skill to run the MMLU benchmark on the specified HuggingFace model using a batch size of 8.

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 academic datasets like MMLU and GSM8K?▼

You can benchmark LLMs by running them against over 60 academic datasets to quantify reasoning, knowledge, and coding capabilities using standardized metrics. This provides reproducible evaluation results for research and production.

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

Yes, LLM evaluation integrates directly with HuggingFace, vLLM, and API-based models. This compatibility allows you to seamlessly load and benchmark different model architectures within the same evaluation harness.

How do I track training progress for a custom LLM?▼

You can track custom LLM training progress by automating periodic evaluations after every training epoch. This generates a learning curve to monitor performance improvements and verify model quality against industry standards.

What is the best way to compare Large Language Models objectively?▼

The best way to objectively compare Large Language Models is by running a standardized benchmarking suite across diverse datasets. This quantifies reasoning and coding capabilities to yield reproducible performance reports.

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