evaluating-llms-harness

Benchmarks language models using standardized academic and code evaluation suites.

Updated May 24, 2026

Search Tags:#benchmarking #llm evaluation #vllm #gsm8k #mmlu #humaneval #distributed evaluation

Ready to run

npx skills add https://github.com/anxiety135790/hermes-skills --skill evaluating-llms-harness-anxiety135790

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/anxiety135790/hermes-skills/tree/main/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/anxiety135790/hermes-skills --skill evaluating-llms-harness-anxiety135790

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you measure and compare language model quality with standardized benchmarks instead of relying on subjective impressions or ad hoc prompts.

Core Features & Use Cases

Academic Benchmarking: Run widely used evaluation suites such as MMLU, GSM8K, HumanEval, HellaSwag, TruthfulQA, ARC, and more.
Training Progress Tracking: Evaluate checkpoints during training to monitor learning curves and model improvement over time.
Model Comparison: Compare multiple models side by side with consistent metrics and reproducible settings.
API and Distributed Evaluation: Assess local HuggingFace models, vLLM deployments, OpenAI and Anthropic APIs, and multi-GPU setups.

Quick Start

Use the lm-evaluation-harness skill to benchmark your model on MMLU, GSM8K, and HellaSwag with a HuggingFace or vLLM backend.

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 LLMs using MMLU and GSM8K?▼

Benchmark LLMs by running standardized academic suites like MMLU and GSM8K to measure model quality with deterministic metrics. This replaces subjective impressions with reproducible evaluation settings for accurate model comparison.

Can I evaluate local HuggingFace models and vLLM deployments together?▼

Evaluate local HuggingFace models and vLLM deployments together using distributed inference. The benchmarking harness supports both backends alongside OpenAI and Anthropic APIs for consistent side-by-side model comparison.

What is the best way to track training progress for language models?▼

Track training progress by evaluating checkpoints against standardized benchmarks to monitor learning curves. This provides consistent metrics to observe model improvement over time instead of relying on ad hoc prompts.

Does LLM evaluation work with multi-GPU setups and API-compatible models?▼

LLM evaluation supports multi-GPU setups and API-compatible models for distributed evaluation. Assess local HuggingFace models, vLLM deployments, and external APIs while maintaining deterministic metrics and reproducible research reporting.

Why use standardized academic benchmarks instead of ad hoc prompts?▼

Standardized academic benchmarks provide reproducible settings and deterministic metrics for reliable model comparison. Relying on ad hoc prompts lacks consistency, whereas suites like HumanEval and TruthfulQA ensure trusted evaluation results.