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JamesFincher
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evaluating-llms-harness

Benchmarks LLMs on academic tasks like MMLU and GSM8K via lm-eval.

Updated May 4, 2026
Search Tags:#benchmarking#llm evaluation#human evaluation#vllm#gsm8k#mmlu#lm-eval-harness
One-click install
npx skills add https://github.com/JamesFincher/gengar --skill evaluating-llms-harness-jamesfincher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/JamesFincher/gengar/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/JamesFincher/gengar --skill evaluating-llms-harness-jamesfincher

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you benchmark language models consistently across widely used academic tasks so you can compare model quality and track progress over time.

Core Features & Use Cases

  • Run standardized evaluations across benchmarks like MMLU, GSM8K, HumanEval, TruthfulQA, and HellaSwag with the lm-eval-harness tooling.
  • Support multiple backends including Hugging Face models, vLLM, and API-driven models for flexible benchmarking setups.
  • Analyze and operationalize results by saving outputs, logging samples, and repeating evaluations for training checkpoints and model comparisons.

Use Case: Evaluate a newly trained 7B model before publishing by running MMLU/GSM8K/HellaSwag, then use the saved JSON results to generate a comparison table against your previous baseline.

Quick Start

Ask the agent to run lm-evaluation-harness on your Hugging Face model for MMLU, GSM8K, and HellaSwag and save the JSON results to a results folder using your preferred batch size.

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 Hugging Face model on MMLU and GSM8K?▼

You can benchmark Hugging Face models on MMLU and GSM8K by configuring the lm-eval-harness with your model and preferred batch size to run standardized academic tasks and save reproducible JSON results.

Can I use vLLM for LLM evaluation instead of local inference?▼

Yes, vLLM is supported as a backend for LLM evaluation. You can configure the harness to use vLLM-backed inference alongside local Hugging Face or API-driven models for flexible benchmarking setups.

What standardized tasks are available for benchmarking language models?▼

Available standardized tasks for benchmarking language models include MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, and ARC, providing objective quality comparisons across academic metrics.

Do I need to install lm-eval to run benchmarks on my models?▼

Yes, you need to install the lm-eval package along with transformers and vllm dependencies to execute standardized evaluations and generate reproducible result artifacts for regression tracking.

How do I track model quality progress over time?▼

You can track model quality progress over time by running standardized evaluations on training checkpoints, saving the JSON outputs, and comparing the reproducible result artifacts against previous baselines.

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