evaluating-llms-harness

Name: evaluating-llms-harness
Availability: InStock
Author: brilly-bohyun

Evaluates and benchmarks LLMs on 60+ academic scenarios with standardized metrics.

Updated Jun 17, 2026

Search Tags:#llm #benchmarking #model evaluation #gsm8k #mmlu #human eval

Ready to run

npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill evaluating-llms-harness-brilly-bohyun

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/Brilly-Bohyun/skill-repository/tree/main/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/Brilly-Bohyun/skill-repository --skill evaluating-llms-harness-brilly-bohyun

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 benchmark and evaluate Large Language Models (LLMs) on 60+ academic benchmarks, enabling model comparison, quality tracking, and academic research.

Core Features & Use Cases

Benchmarking: Evaluate LLMs across 60+ academic benchmarks such as MMLU, GSM8K, HumanEval, and more.
Model Comparison: Compare models side-by-side and generate comparison tables.
Training Progress Tracking: Evaluate checkpoints during training to track progress and identify areas for improvement.
Custom Tasks: Evaluate custom evaluation tasks using the harness.

Quick Start

Evaluate a HuggingFace model on MMLU:

lm-evaluation-harness --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu --num_fewshot 5 --batch_size 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 benchmark a HuggingFace model on MMLU?▼

To benchmark a HuggingFace model on MMLU, run the harness with the `hf` model type, specify the pretrained checkpoint name, set `mmlu` as the task, and configure few-shot and batch size parameters to execute evaluation and generate metrics.

What academic benchmarks can I use for evaluating Large Language Models?▼

Evaluating Large Language Models can be performed across 60+ academic benchmarks including MMLU, GSM8K, and HumanEval, providing standardized prompts and metrics for accurate model comparison and academic research.

Does this LLM evaluation harness support vLLM and custom API endpoints?▼

Yes, this LLM evaluation harness supports vLLM and custom API endpoints in addition to HuggingFace, requiring the `lm-eval`, `transformers`, and `vllm` dependencies to evaluate and benchmark diverse model deployments.

Can I track training progress by evaluating LLM checkpoints?▼

You can track training progress by evaluating LLM checkpoints during training to measure benchmark performance over time, identify areas for improvement, and generate side-by-side comparison tables.

What's the best way to compare LLMs side-by-side on academic benchmarks?▼

The best way to compare LLMs side-by-side on academic benchmarks is to evaluate multiple models using the same standardized prompts and metrics harness, which generates comparison tables for accurate performance tracking.

How do I evaluate custom tasks using the lm-eval harness?▼

You can evaluate custom tasks using the lm-eval harness by configuring the standardized evaluation framework to support custom evaluation criteria, enabling benchmarking for specialized use cases beyond the 60+ built-in academic datasets.