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evaluating-llms-harness

Evaluate LLMs against 60+ academic benchmarks using lm-evaluation-harness.

3|Updated Feb 21, 2026
Search Tags:#benchmarking#llm evaluation#performance testing#lm-evaluation-harness#model quality#academic benchmarks
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill evaluating-llms-harness-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/11-evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill evaluating-llms-harness-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm, human-eval, pytorch-lightning, scikit-learn, pandas, matplotlib, sentence-transformers, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a standardized framework for evaluating the performance of Large Language Models (LLMs) across a wide range of academic benchmarks, enabling objective comparison and progress tracking.

Core Features & Use Cases

  • Comprehensive Benchmarking: Evaluates LLMs on 60+ academic benchmarks including MMLU, HumanEval, GSM8K, and TruthfulQA.
  • Model Comparison: Facilitates direct comparison of different LLMs based on standardized metrics.
  • Training Progress Tracking: Monitors and visualizes model performance during the training lifecycle.
  • Use Case: A researcher wants to compare the reasoning capabilities of two newly trained LLMs. They use this Skill to run both models against the MMLU and GSM8K benchmarks and analyze the resulting accuracy scores.

Quick Start

Use the lm-evaluation-harness skill to evaluate the 'meta-llama/Llama-2-7b-hf' model on the 'mmlu' and 'gsm8k' tasks.

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 LLMs against academic benchmarks like MMLU and GSM8K?▼

You can benchmark LLM performance on academic tasks by running models against standardized evaluation suites like MMLU and GSM8K, which generates accuracy scores for objective comparison and tracking.

What is the best way to compare the reasoning capabilities of different LLMs?▼

The best way to compare LLM reasoning capabilities is to run multiple models through a standardized evaluation suite, generating direct accuracy score contrasts across academic benchmarks to determine relative performance.

Does the lm-evaluation-harness framework support evaluating vLLM and HuggingFace models?▼

Yes, the lm-evaluation-harness framework supports evaluating HuggingFace, vLLM, and API-based models, providing flexible integration for testing various LLM architectures across academic benchmarks.

Can I track LLM training progress using academic performance testing?▼

Yes, you can track LLM training progress by periodically running the model through academic performance testing benchmarks, which monitors and visualizes accuracy changes during the training lifecycle.

What domains does LLM evaluation cover using the lm-evaluation-harness?▼

LLM evaluation using the lm-evaluation-harness covers reasoning, coding, and knowledge domains, testing model quality across 60+ academic benchmarks including TruthfulQA and HumanEval.

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