evaluating-llms-harness

Name: evaluating-llms-harness
Availability: InStock
Author: tangzheng202202

Benchmark LLMs across 60+ tasks using the lm-evaluation-harness backend.

1|Updated Apr 13, 2026

Search Tags:#llm #benchmarking #metrics #evaluation #benchmarks #lm_eval

Ready to run

npx skills add https://github.com/tangzheng202202/hermes-skills --skill evaluating-llms-harness-tangzheng202202

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/tangzheng202202/hermes-skills/tree/main/03-mlops/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/tangzheng202202/hermes-skills --skill evaluating-llms-harness-tangzheng202202

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a consistent, scalable way to benchmark large language models across 60+ academic benchmarks, enabling objective comparison and progress tracking for researchers and engineers.

Core Features & Use Cases

Evaluates 60+ tasks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag to surface a model's capabilities across reasoning, coding, and knowledge domains.
Supports multiple backends and integrations (HuggingFace, vLLM, and API-based models) to fit diverse deployment environments and research needs.
Use cases include comparing model versions, tracking progress over time for academic publications, and benchmarking new architectures for publication-ready results.

Quick Start

Run a full evaluation using the lm-evaluation-harness harness against your desired task set to establish a baseline.

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 multiple academic tasks like MMLU and HumanEval?▼

To benchmark LLMs across tasks like MMLU and HumanEval, you can run the lm-evaluation-harness backend against a configured task list to generate standardized metrics quantifying model quality and progression.

What is the best way to evaluate and compare model versions for academic publications?▼

Evaluating and comparing model versions for academic publications is best achieved by running standardized benchmarks across 60+ tasks, which tracks model progression over time and generates objective, publication-ready results.

Does the lm-evaluation-harness support API-based models and vLLM backends?▼

Yes, the lm-evaluation-harness supports evaluating API-based models and vLLM backends alongside HuggingFace deployments, allowing you to fit diverse research environments and compare model quality consistently.

Can I use a single harness to evaluate reasoning, coding, and knowledge domains?▼

Yes, you can use a single harness to evaluate reasoning, coding, and knowledge domains by configuring tasks like GSM8K, TruthfulQA, and HellaSwag to surface a model's comprehensive capabilities.

Do I need the lm-evaluation-harness backend to quantify model quality?▼

Yes, you need the lm-evaluation-harness backend to quantify model quality, as it provides the framework to execute the 60+ benchmark tasks and output the standardized metrics required for evaluation.