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Chris-Chai-Minjae
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evaluating-llms-harness

Benchmark LLMs across 60+ standardized tasks with automated evaluation pipelines.

Updated Apr 23, 2026
Search Tags:#benchmarking#llm-evaluation#evaluation-harness#gsm8k#humaneval#hellaswag#standard-tasks
One-click install
npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill evaluating-llms-harness-chris-chai-minjae
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/Chris-Chai-Minjae/hermes-agent-r1-bridge --skill evaluating-llms-harness-chris-chai-minjae

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables researchers and engineers to benchmark and compare large language models across 60+ established benchmarks, producing reproducible performance reports for development, publication, and vendor evaluations.

Core Features & Use Cases

  • 60+ benchmarks including MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag
  • Supports HuggingFace, vLLM, and API-based models
  • Automated evaluation pipelines with score aggregation and model comparisons
  • Use cases: benchmarking, model selection, progress tracking

Quick Start

Run a standard benchmark across core tasks to generate a reproducible performance snapshot.

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 GSM8K and HumanEval?▼

Yes, you can evaluate HuggingFace, vLLM, and API-based large language models. The harness runs automated evaluation pipelines across these deployments to generate reproducible performance snapshots for direct model comparison.

What is the best way to compare LLM performance for model selection?▼

Yes, you can evaluate HuggingFace, vLLM, and API-based large language models. The harness runs automated evaluation pipelines across these deployments to generate reproducible performance snapshots for direct model comparison.

Does this LLM evaluation harness support API-based models and vLLM?▼

The best way to compare LLM performance is using an evaluation harness with automated score aggregation across 60+ benchmarks. This generates reproducible reports tracking model progress for selection and publication.

Can I use standard benchmarks like MMLU and HellaSwag for reproducible LLM evaluation?▼

To benchmark LLMs, run standardized tasks like GSM8K and HumanEval to quantify and compare model performance, producing reproducible reports for development and vendor comparisons. It supports 60+ benchmarks including MMLU and TruthfulQA.

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