evaluating-llms-harness

Evaluate large language models across 60+ academic benchmarks.

Updated Apr 29, 2026

Search Tags:#benchmarking #model comparison #academic research #LLM evaluation #benchmark suite

Ready to run

npx skills add https://github.com/fikriaf/agentos --skill evaluating-llms-harness-fikriaf

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/fikriaf/agentos/tree/main/src/agentos/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/fikriaf/agentos --skill evaluating-llms-harness-fikriaf

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows users to evaluate large language models (LLMs) across 60+ academic benchmarks, tracking training progress and comparing model quality. It provides a standardized method for benchmarking LLMs and is used by leading AI labs.

Core Features & Use Cases

Benchmarking: Evaluate LLMs using 60+ academic benchmarks like MMLU, HumanEval, GSM8K, etc.
Progress Tracking: Monitor model performance over time with periodic evaluations.
Model Comparison: Compare different LLMs side-by-side across various benchmarks.
Use Case: A researcher wants to compare the performance of two LLMs (GPT-3 and Llama-2) on a set of benchmarks. They can use this Skill to run evaluations and generate comparison tables.

Quick Start

Run the lm-evaluation-harness to evaluate the GPT-4 model on the MMLU benchmark and save the results to a file.

lm-evaluation-harness --model hf --model_args pretrained=gpt4 --tasks mmlu --output_path results/

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 academic benchmarks by running the lm-evaluation-harness script, specifying the model source like HuggingFace or vLLM, and targeting tasks such as MMLU to generate standardized performance results.

Can I compare custom HuggingFace models side-by-side with API-based models?▼

You can compare custom HuggingFace models side-by-side with API-based models. The evaluation harness supports HuggingFace, vLLM, and API-based models, allowing you to generate direct comparison tables across 60+ academic benchmarks.

What's the best way to track LLM training progress over time?▼

The best way to track LLM training progress over time is by running periodic evaluations using this benchmark suite. It monitors model performance across standardized academic benchmarks, allowing you to measure quality improvements throughout the training lifecycle.

Do I need vLLM and transformers installed to run LLM benchmarking tasks?▼

You need vLLM and transformers installed to run LLM benchmarking tasks. The Skill specifically requires Python libraries including lm-eval, transformers, and vllm to evaluate models and execute the academic benchmark suite successfully.

How does model comparison across 60+ academic benchmarks work for research?▼

Model comparison for academic research works by evaluating different LLMs against a standardized suite of 60+ academic benchmarks. This provides a consistent method for assessing model quality, tracking progress, and generating comparison tables for analytical review.