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LM Evaluation Harness

Benchmark LLM performance across 60+ academic benchmarks with reproducible scoring.

577|62|Updated May 15, 2026
Search Tags:#integration#mlops#benchmarking#tasks#reproducibility#model-evaluation#llm-evaluation
One-click install
npx skills add https://github.com/agentic-in/elephant-agent --skill lm-evaluation-harness-agentic-in
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LM Evaluation Harness
Source: https://github.com/agentic-in/elephant-agent/tree/main/packages/skills/builtin_packages/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/agentic-in/elephant-agent --skill lm-evaluation-harness-agentic-in

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Benchmarking and evaluating LLMs across 60+ academic benchmarks to provide standardized, reproducible performance measures for model comparison, progress tracking, and scholarly reporting.

Core Features & Use Cases

  • 60+ task benchmarks across language understanding, code, reasoning, and multilingual tasks for comprehensive evaluation.
  • Cross-model compatibility with HuggingFace, vLLM, and API-based endpoints for flexible benchmarking setups.
  • Reproducible results with deterministic prompts, scoring, and easy integration into research pipelines.

Quick Start

Run a full LM evaluation on your current model using the built-in 60+ benchmarks.

Frequently Asked Questions about LM Evaluation Harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark LLM performance across multiple academic tasks?▼

Yes, you can evaluate API-based endpoints alongside HuggingFace and vLLM deployments. This cross-model compatibility allows you to flexibly benchmark different LLM setups using the same standardized task suite and reproducible scoring mechanisms.

How do I ensure reproducibility when evaluating language models?▼

Yes, you can evaluate API-based endpoints alongside HuggingFace and vLLM deployments. This cross-model compatibility allows you to flexibly benchmark different LLM setups using the same standardized task suite and reproducible scoring mechanisms.

How do I ensure reproducibility when evaluating language models?▼

To ensure reproducibility during language model evaluation, the process uses deterministic prompts and standardized scoring mechanisms. This combination provides consistent measures for tracking training progress and reporting academic results.

Can I use this benchmarking suite with vLLM and HuggingFace models?▼

Yes, you can evaluate API-based endpoints alongside HuggingFace and vLLM deployments. This cross-model compatibility allows you to flexibly benchmark different LLM setups using the same standardized task suite and reproducible scoring mechanisms.

What is the best way to compare LLM performance using standardized tasks?▼

The best way to compare LLM performance is using a built-in suite of 60+ academic benchmarks. This provides reproducible measures across language, code, and reasoning tasks, requiring minimal setup to integrate into your research pipelines.

Does LLM evaluation require extensive setup for academic benchmarking?▼

No, academic benchmarking requires minimal setup to run. You can quickly start a full evaluation on your current model using the built-in 60+ task suite to generate standardized, reproducible scoring results.

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