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evaluating-llms-harness

Run lm-evaluation-harness to benchmark LLMs on standardized tasks like MMLU and GSM8K.

19|4|Updated Apr 22, 2026
Search Tags:#benchmarking#llm evaluation#vllm#gsm8k#mmlu#humaneval#model quality
One-click install
npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill evaluating-llms-harness-carterwayneskhizeine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/carterwayneskhizeine/hermes-agent-windows-R/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/carterwayneskhizeine/hermes-agent-windows-R --skill evaluating-llms-harness-carterwayneskhizeine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you benchmark language models against widely used academic and industry-standard tasks, so you can compare model quality objectively and track improvements over time.

Core Features & Use Cases

  • Run standardized LLM evaluations across common benchmark suites like MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, and ARC using the lm-evaluation-harness CLI.
  • Support multiple backends including HuggingFace models, vLLM for faster inference, and API-based models (when configured) with consistent task definitions and metrics.
  • Produce actionable artifacts by saving results to JSON for later analysis, regression testing, and reporting in papers or engineering updates.

Use cases: validating a new model release, comparing checkpoints during training, evaluating reasoning vs code generation quality, and generating consistent metrics for dashboards or release notes.

Quick Start

Run lm-evaluation-harness on core benchmarks for a HuggingFace model with: "lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge --num_fewshot 5 --batch_size 8 --output_path results/llama2-eval.json"

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 on MMLU and GSM8K?▼

You can benchmark LLMs on MMLU and GSM8K by running the lm-evaluation-harness CLI with a HuggingFace or vLLM backend, specifying the model, tasks, few-shot count, and batch size to produce objective quality metrics.

Can I use vLLM for faster LLM evaluation on standardized tasks?▼

Yes, vLLM is supported as a model backend for faster inference during LLM evaluation. The harness supports vLLM alongside HuggingFace and API-based models to run standardized tasks like HellaSwag and ARC with consistent prompts.

What is the best way to compare model quality across different checkpoints during training?▼

The best way to compare model quality across training checkpoints is applying standardized, repeatable evaluations using lm-evaluation-harness, running consistent benchmarks like TruthfulQA and HumanEval to track improvements objectively.

Do I need to configure few-shot count and batch size for LLM benchmarking?▼

Yes, configuring evaluation hyperparameters like few-shot count and batch size is required for LLM benchmarking. These settings ensure consistent prompts and metrics when evaluating models on tasks like MMLU and GSM8K.

How do I save benchmarking results for regression testing and reporting?▼

Save benchmarking results for regression testing by specifying an output path in the CLI command to export JSON artifacts. These files contain the evaluation metrics needed for academic reporting and engineering release notes.

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