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evaluating-llms-harness

Evaluate large language models on academic benchmarks using lm-eval.

1|1|Updated May 9, 2026
Search Tags:#benchmarking#evaluation#large language models#model quality#academic benchmarks
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill evaluating-llms-harness-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill evaluating-llms-harness-ldzhhxx

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of benchmarking large language models across a diverse set of academic-grade tasks and industry-standard benchmarks, providing a comprehensive evaluation of model quality and performance.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate LLMs on tasks like MMLU, GSM8K, HellaSwag, TruthfulQA, and more.
  • Standardized Prompts and Metrics: Ensures fair and consistent evaluations across different models.
  • Industry-Grade Suite: Used by major research organizations like EleutherAI and HuggingFace.

Quick Start

To benchmark a HuggingFace model, install lm-eval and run the following command:

lm-eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag \
  --device cuda:0 \
  --batch_size 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 evaluate large language models on academic benchmarks like MMLU and GSM8K?▼

To evaluate large language models on academic benchmarks, you can use this Skill to automate standardized prompts and metrics across tasks like MMLU and GSM8K. It leverages the `lm-eval` library to ensure fair, consistent evaluations for any HuggingFace model.

What is the best way to benchmark a HuggingFace model using lm-eval?▼

The best way to benchmark a HuggingFace model is using the `lm-eval` library with standardized tasks. Simply specify the model arguments, such as `pretrained=meta-llama/Llama-2-7b-hf`, select your tasks, and run the evaluation on your preferred device.

How does standardized prompt evaluation ensure fair model quality assessment?▼

Standardized prompt evaluation ensures fair model quality assessment by applying consistent prompts and metrics across all tested models. This automation eliminates prompt variance, providing accurate, academic-grade performance comparisons on benchmarks like TruthfulQA and HellaSwag.

Do I need a specific Python environment to run academic-grade LLM evaluations?▼

Yes, you need a Python environment with the `lm-eval` library installed, along with `transformers` and `vllm` dependencies. Access to models via HuggingFace is also required to execute the standardized academic benchmark tasks.

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