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evaluating-llms-harness

Evaluate LLMs on academic benchmarks using Python and lm-eval.

Updated May 8, 2026
Search Tags:#llm#benchmarking#machine learning#model evaluation#large language models
One-click install
npx skills add https://github.com/superfhp/lumi-agent --skill evaluating-llms-harness-superfhp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/superfhp/lumi-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/superfhp/lumi-agent --skill evaluating-llms-harness-superfhp

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 provides a comprehensive framework for benchmarking and evaluating large language models (LLMs) on a wide range of academic and industry-standard benchmarks.

Core Features & Use Cases

  • Benchmarking: Evaluate LLMs across 60+ academic benchmarks including MMLU, GSM8K, HumanEval, and more.
  • Model Comparison: Compare the performance of different models side-by-side.
  • Training Progress Tracking: Monitor training progress and evaluate checkpoints during training.
  • API Support: Evaluate OpenAI, Anthropic, and other API-based models through a unified interface.
  • Use Case: Suppose you are training a new LLM and want to compare its performance against established models like GPT-4 and Claude 3. Use this Skill to run benchmark evaluations and analyze the results.

Quick Start

Use the lm-evaluation-harness skill to evaluate a HuggingFace model on the MMLU benchmark:

lm-evaluation-harness evaluate hf --model_args pretrained=llama2-7b-hf --tasks mmlu

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?▼

You can evaluate large language models on benchmarks like MMLU by running the lm-eval harness with a HuggingFace model, specifying the pretrained model arguments and target tasks to generate performance metrics.

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

Yes, you can evaluate API-based models like OpenAI and Anthropic. The evaluation framework supports API model evaluation through a unified interface, allowing you to compare proprietary models against local HuggingFace or vLLM models.

What is the best way to compare HuggingFace and vLLM models side-by-side?▼

The best way to compare models side-by-side is using an evaluation harness that supports both HuggingFace and vLLM frameworks, running them against identical academic benchmark suites to analyze relative performance results.

Do I need vllm and transformers libraries to benchmark large language models?▼

Yes, you need the vllm and transformers libraries installed in your Python environment to benchmark local large language models effectively, alongside the lm-eval framework to execute the benchmarking scripts.

How does model evaluation help with tracking LLM training progress?▼

Model evaluation helps track LLM training progress by running intermediate model checkpoints through standardized benchmark suites, allowing you to monitor performance improvements and compare results against established baselines.

What benchmarks are available for evaluating large language models?▼

Available benchmarks for evaluating large language models include over 60 academic and industry-standard datasets such as MMLU, GSM8K, and HumanEval, covering diverse tasks to comprehensively measure model capabilities.

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