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evaluating-llms-harness

Evaluate language models on academic benchmarks using lm-eval, transformers, and vllm.

Updated Jun 4, 2026
Search Tags:#llm#benchmarking#evaluation#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/kngender5/hermes --skill evaluating-llms-harness-kngender5
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/kngender5/hermes/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/kngender5/hermes --skill evaluating-llms-harness-kngender5

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 language models (LLMs) across various tasks and metrics, providing a standardized approach to evaluate and compare model performance.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate LLMs on a wide range of tasks including MMLU, GSM8K, HumanEval, and more.
  • Industry Standard: Used by leading organizations like EleutherAI and HuggingFace.
  • Quick Start: Evaluate a HuggingFace model with a single command: 
    lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag \
  --device cuda:0 \
  --batch_size 8
  • Custom Evaluation: Run custom evaluations for specific tasks or metrics.

Quick Start

Evaluate the Llama-2-7b model on MMLU, GSM8K, and HellaSwag benchmarks.

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?▼

You can benchmark LLMs on MMLU and GSM8K by running the lm_eval command with a HuggingFace model, specifying the target tasks like mmlu and gsm8k, and configuring the device and batch size for execution.

What is the best way to evaluate language models on academic benchmarks?▼

Evaluating language models on academic benchmarks is best done using a standardized framework that applies metrics across 60+ tasks, including MMLU, HumanEval, and HellaSwag, enabling direct performance comparison.

Do I need vllm and transformers to run custom LLM evaluations?▼

Yes, you need transformers and vllm installed as dependencies to execute custom LLM evaluations, alongside lm-eval, to properly load models and run the benchmarking suite.

Can I run custom evaluations for specific LLM tasks?▼

Yes, you can run custom evaluations for specific tasks or metrics, allowing you to tailor the benchmarking process to your unique research or development requirements beyond the standard suite.

How does lm-eval compare to other language model benchmarking tools?▼

The lm-eval framework provides an industry-standard approach used by leading organizations like EleutherAI and HuggingFace, distinguishing itself by offering a wide range of 60+ academic benchmarks for comprehensive model comparison.

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