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evaluating-llms-harness

Evaluate LLMs against academic benchmarks with reproducible metrics.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Benchmarking large language models is hard to do consistently across tasks, backends, and runs, and this Skill makes evaluation reproducible and comparable with standardized prompts and metrics.

Core Features & Use Cases

  • Evaluates models across major academic benchmarks such as MMLU, GSM8K, HellaSwag, TruthfulQA, HumanEval, ARC, and more.
  • Supports HuggingFace models, vLLM inference, OpenAI-compatible APIs, Anthropic-style APIs, and custom local endpoints.
  • Helps with model release reporting, training checkpoint tracking, distributed multi-GPU evaluation, and custom domain-specific benchmark creation.

Quick Start

Ask for an evaluation plan for your model and benchmarks, including the backend you want to use, the few-shot setting, and whether you need single-GPU or distributed execution.

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 against standardized academic tasks like MMLU and GSM8K?▼

To benchmark LLMs against standardized academic tasks like MMLU and GSM8K, you select your model, backend, specific tasks, few-shot settings, and batching configuration to generate reproducible performance metrics.

Can I evaluate models using vLLM inference and OpenAI-compatible APIs?▼

Yes, you can evaluate models using vLLM inference, OpenAI-compatible APIs, Anthropic-style APIs, HuggingFace models, and custom local endpoints by explicitly selecting your preferred backend for the benchmark run.

What is the best way to run distributed LLM evaluation across multiple GPUs?▼

The best way to run distributed LLM evaluation across multiple GPUs is to configure your benchmark suite for multi-GPU execution, enabling consistent and reproducible performance tracking across large model checkpoints.

Does LLM evaluation support custom domain-specific tasks and code execution?▼

Yes, LLM evaluation supports creating custom domain-specific benchmarks and includes optional code-execution support for tasks like HumanEval, allowing you to measure model performance beyond standard academic suites.

How do I track training checkpoints during model benchmarking?▼

You track training checkpoints during model benchmarking by running standardized evaluations at different stages, ensuring explicit backend and task selection to compare reproducible performance metrics across model iterations.

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