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evaluating-llms-harness

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

2|1|Updated Jul 14, 2026
Search Tags:#llm#benchmarking#evaluation#huggingface#vllm#mmlu
One-click install
npx skills add https://github.com/heysuhas/hermes_cli --skill evaluating-llms-harness-heysuhas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/heysuhas/hermes_cli/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/heysuhas/hermes_cli --skill evaluating-llms-harness-heysuhas

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 diverse academic and reasoning benchmarks, ensuring reproducible and standardized evaluation results.

Core Features & Use Cases

  • Standardized Benchmarking: Evaluate models on over 60 academic datasets including MMLU, GSM8K, and HumanEval.
  • Flexible Backend Support: Run evaluations using HuggingFace, vLLM, or various API-based models.
  • Training Progress Tracking: Monitor model quality improvements during training by running periodic evaluations on specific checkpoints.

Quick Start

Execute the lm-evaluation-harness to evaluate a specified HuggingFace model on the MMLU benchmark using a single GPU.

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 MMLU and GSM8K using HuggingFace?▼

To benchmark LLMs on MMLU and GSM8K, use this Skill to evaluate HuggingFace models across standardized academic datasets, quantifying performance with reproducible metrics.

Can I use vLLM to accelerate inference during LLM evaluation?▼

Yes, you can use vLLM for accelerated inference during LLM evaluation, alongside support for local HuggingFace models and various API-based language models.

What is the best way to track LLM quality improvements during training?▼

The best way to track LLM quality improvements is by running periodic evaluations on specific model checkpoints across academic benchmarks to quantify performance changes.

Does this LLM evaluation harness support API-based models?▼

Yes, the LLM evaluation harness supports various external API-based language models, allowing you to benchmark them alongside local HuggingFace and vLLM backends.

How many academic datasets are supported for LLM benchmarking?▼

The LLM benchmarking harness supports evaluation on over 60 academic datasets, including industry-standard metrics like MMLU, GSM8K, and HumanEval for reproducible research.

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