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SriRamkunamsetty
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evaluating-llms-harness

Run standardized language model benchmarks and report metrics across multiple backends.

Updated May 20, 2026
Search Tags:#metrics#nlp#benchmark#llm-evaluation#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill evaluating-llms-harness-sriramkunamsetty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent/tree/main/hermes-agent/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/SriRamkunamsetty/SITA2.0-HermesAgent --skill evaluating-llms-harness-sriramkunamsetty

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Benchmarking and comparing language models across standardized tasks to produce reproducible performance metrics.

Core Features & Use Cases

  • Evaluates 60+ benchmarks (MMLU, GSM8K, HumanEval, ARC, and more) to surface robust model quality signals.
  • Supports multiple backends (HuggingFace, vLLM, and local/API adapters) for flexible deployment and benchmarking.
  • Produces consolidated metrics, dashboards, and comparative reports to inform model selection and deployment decisions.

Quick Start

Install lm-evaluation-harness and run a basic evaluation on a chosen model across a subset of tasks to obtain reproducible 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 standardized tasks like MMLU and HumanEval?▼

You can benchmark LLMs using standardized evaluation tasks configured via YAML. This process generates consolidated metrics and reproducible reports, covering 60+ benchmarks like MMLU and HumanEval to inform model selection.

What is the best way to evaluate language models with vLLM or HuggingFace backends?▼

Evaluating language models with vLLM or HuggingFace backends involves using supported model adapters. This enables flexible deployment and benchmarking across diverse scenarios, yielding reproducible performance metrics for robust model quality signals.

Can I use custom APIs to run NLP benchmarks and generate comparative reports?▼

Yes, you can use custom APIs to run NLP benchmarks. The system supports local and API adapters, enabling standardized evaluation tasks and generating comparative dashboards and reports to inform deployment decisions.

Does this benchmarking approach support reproducible evaluations for 60+ benchmarks?▼

Yes, this benchmarking approach supports reproducible evaluations for over 60 benchmarks. It utilizes YAML task configurations, result parsing, and standardized reporting to ensure end-to-end benchmarking workflows yield consistent metrics.

How do I parse results from GSM8K and ARC benchmarks to surface model quality signals?▼

You parse results from GSM8K and ARC benchmarks using the built-in result parsing mechanism. This processes evaluation task outputs, producing consolidated metrics and reports that surface robust model quality signals.

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