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evaluating-llms-harness

Evaluate LLM performance across 60+ academic benchmarks with multiple backends.

2.8k|332|Updated Jan 29, 2026
Search Tags:#llm#metrics#evaluation#benchmark#harness#backends
One-click install
npx skills add https://github.com/moltis-org/moltis --skill evaluating-llms-harness-moltis-org
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/moltis-org/moltis/tree/main/crates/skills/src/assets/mlops/evaluation/evaluating-llms-harness
Command: npx skills add https://github.com/moltis-org/moltis --skill evaluating-llms-harness-moltis-org

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Evaluate LLM performance across 60+ academic benchmarks to generate standardized quality metrics.

Core Features & Use Cases

  • Benchmark across 60+ tasks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag.
  • Supports multiple backends (HuggingFace, vLLM, API-based providers) and standardized evaluation pipelines.
  • Use cases include model comparison, academic reporting, and progress tracking for model development.

Quick Start

Run an initial evaluation with your model using the harness to generate baseline benchmark scores.

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 LLM performance across academic tasks like MMLU and HumanEval?▼

To benchmark LLM performance, you can evaluate models across 60+ academic tasks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag to generate standardized quality metrics for comparison.

Can I use vLLM and HuggingFace backends for LLM evaluation?▼

Yes, LLM evaluation supports multiple backends including HuggingFace, vLLM, and API-based providers, ensuring a unified evaluation workflow that produces reproducible results across different environments.

What is the best way to compare LLM models using standardized metrics?▼

The best way to compare LLM models is running a consistent benchmark suite across 60+ tasks, which applies a unified evaluation workflow to generate reproducible, standardized quality metrics for direct model comparison.

How do I run an initial LLM evaluation to generate baseline benchmark scores?▼

You run an initial LLM evaluation by executing the harness with your model to generate baseline benchmark scores, applying a standardized pipeline across the 60+ academic task suite.

Does LLM benchmarking support API-based providers for generating evaluation metrics?▼

Yes, LLM benchmarking supports API-based providers as a backend, allowing you to evaluate remote models within the standardized pipeline to generate reproducible quality metrics.

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