evaluating-llms-harness

Evaluate large language models across 60+ academic benchmarks.

Updated May 11, 2026

Search Tags:#llm #benchmarking #academic research #model evaluation #large language models

Ready to run

npx skills add https://github.com/richardnguyen0715/keep-it-real --skill evaluating-llms-harness-richardnguyen0715

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/richardnguyen0715/keep-it-real/tree/main/refer-projects/hermes-agent/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/richardnguyen0715/keep-it-real --skill evaluating-llms-harness-richardnguyen0715

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 problem of evaluating large language models (LLMs) across a wide range of academic benchmarks, making it easier to compare models, track training progress, and report academic results.

Core Features & Use Cases

Benchmarking LLMs: Evaluate LLMs on 60+ academic benchmarks, including MMLU, GSM8K, HumanEval, and more.
Industry Standards: Supports benchmarks used by major labs and industry leaders like EleutherAI and HuggingFace.
Tracking Training Progress: Evaluate checkpoints during training to monitor performance over time.
Model Comparison: Compare multiple models using a standardized benchmark suite.

Quick Start

Use the lm-evaluation-harness skill to evaluate a HuggingFace model with MMLU benchmark. Run: lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-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 benchmark large language models using academic benchmarks?▼

You can benchmark LLMs by running evaluation tasks like MMLU or GSM8K against HuggingFace models. This process requires the lm-eval, transformers, and vllm libraries to execute the standardized academic test suites.

Can I evaluate HuggingFace models on MMLU and GSM8K benchmarks?▼

Yes, you can evaluate HuggingFace models on MMLU and GSM8K benchmarks. The Skill supports 60+ academic benchmarks including MMLU and GSM8K, allowing direct evaluation of HuggingFace pretrained models via the transformers library.

What libraries do I need to run LLM evaluation harnesses?▼

You need the lm-eval, transformers, and vllm libraries installed to execute LLM evaluation harnesses. These dependencies facilitate model loading, inference acceleration, and standardized task execution across Linux and macOS platforms.

How do I track LLM training progress across multiple checkpoints?▼

To track LLM training progress, you evaluate model checkpoints against a standardized benchmark suite. By running periodic evaluations during training, you can monitor performance changes over time and compare multiple checkpoints directly.

Does the lm-eval benchmarking harness work on macOS?▼

Yes, the lm-eval benchmarking harness supports macOS. The Skill facilitates running 60+ academic benchmarks and evaluating large language models on both Linux and macOS platforms using the required vllm and transformers dependencies.

What is the best way to compare multiple LLMs using industry standards?▼

The best way to compare multiple LLMs is evaluating them against a standardized benchmark suite. By running models through 60+ academic benchmarks backed by industry standards from EleutherAI and HuggingFace, you generate consistent metrics for direct comparison.