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evaluating-llms-harness

Benchmark language models across 60+ evaluation tasks with YAML configuration.

Updated Jun 17, 2026
Search Tags:#benchmarking#llm-evaluation#multitask#evaluation-harness#gsm8k#model-benchmark
One-click install
npx skills add https://github.com/anilcan-kara/nozich-agent --skill evaluating-llms-harness-anilcan-kara
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/anilcan-kara/nozich-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/anilcan-kara/nozich-agent --skill evaluating-llms-harness-anilcan-kara

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Benchmark language models across 60+ evaluation tasks using a unified harness. It is used by researchers and engineers to benchmark model quality, compare models, and track progress across standard benchmarks such as MMLU, GSM8K, HumanEval, and ARC.

Core Features & Use Cases

  • Unified task suite: run diverse benchmarks (MMLU, GSM8K, HumanEval, ARC, etc.) with consistent prompts and metrics.
  • Reproducible evaluation: YAML-based task configuration, deterministic seeds, and centralized result reporting for research papers and internal validation.
  • Extensibility: supports multiple backends (HF, vLLM, local APIs) and easy integration with custom tasks or API models.

Quick Start

Run a quick evaluation of a language model by selecting a task set and executing the evaluation harness to obtain baseline metrics.

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 across standard tasks like MMLU and GSM8K?▼

To benchmark LLMs across standard tasks, you use a unified evaluation harness to run over 60 tasks like MMLU and GSM8K with consistent prompts and deterministic seeds for reproducible results.

What is the best way to ensure reproducible LLM evaluation results?▼

Reproducible LLM evaluation is achieved through YAML-based task configuration and deterministic seeds, which standardize prompts and metrics for research papers and internal validation.

Can I evaluate local models or APIs using this harness?▼

Yes, you can evaluate local models or APIs because the harness supports multiple backends including Hugging Face, vLLM, and local APIs for flexible model benchmarking.

How do I add custom tasks to an LLM evaluation harness?▼

You can add custom tasks to the LLM evaluation harness using YAML-based configuration and optional references or assets directories, allowing easy integration and extensibility for specific benchmarks.

Does the harness support benchmarking for code generation tasks like HumanEval?▼

Yes, the harness supports benchmarking for code generation tasks like HumanEval, running diverse evaluations with consistent prompts and centralized result reporting across over 60 task sets.

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