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evaluating-llms-harness

Evaluate LLMs against over 60 academic benchmarks like MMLU and GSM8K.

1|Updated Feb 21, 2026

Search Tags:#benchmarking #llm evaluation #performance testing #lm-evaluation-harness #model quality #academic benchmarks

Ready to run

npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill evaluating-llms-harness-tianhao909

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/tianhao909/AI-Research-SKILLs-cn/tree/main/11-evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/tianhao909/AI-Research-SKILLs-cn --skill evaluating-llms-harness-tianhao909

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

Core Features & Use Cases

Comprehensive Benchmarking: Evaluates LLMs on over 60 academic benchmarks, including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag.
Model Comparison: Facilitates direct comparison of different LLMs or different versions of the same LLM.
Training Progress Monitoring: Allows tracking of model performance during training by evaluating checkpoints periodically.
Industry Standard: Widely adopted by major AI labs and organizations for reliable and reproducible LLM evaluation.

Quick Start

Evaluate the 'meta-llama/Llama-2-7b-hf' model on the 'mmlu' and 'gsm8k' tasks using a batch size of 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 LLM performance on academic benchmarks like MMLU and GSM8K?▼

To evaluate LLM performance on academic benchmarks, you can assess models against over 60 standard tasks including MMLU, HumanEval, and GSM8K to measure quality and compare results objectively.

Can I use vLLM and HuggingFace models for LLM evaluation?▼

Yes, you can evaluate LLMs using HuggingFace, vLLM, and API-based models, configuring few-shot settings and device utilization to ensure reliable and reproducible performance testing across various frameworks.

What is the best way to compare the performance of different Large Language Models?▼

The best way to compare Large Language Models is using a standardized benchmarking framework that evaluates them against identical academic tasks, facilitating direct performance comparison and progress tracking.

How do I track LLM training progress by evaluating model checkpoints?▼

You can track LLM training progress by evaluating model checkpoints periodically against academic benchmarks, which allows you to monitor performance improvements and compare different versions of the same model.

Does this LLM evaluation framework support configurable few-shot settings?▼

Yes, this LLM evaluation framework supports configurable few-shot settings and device utilization, allowing you to customize the evaluation parameters when testing HuggingFace, vLLM, or API-based models.