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evaluating-llms-harness

Evaluate LLMs on academic benchmarks like MMLU and GSM8K.

Updated Mar 8, 2026
Search Tags:#benchmarking#llm evaluation#performance testing#lm-evaluation-harness#model quality#academic benchmarks
One-click install
npx skills add https://github.com/helix4u/hermes-agent-private --skill evaluating-llms-harness-helix4u
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/helix4u/hermes-agent-private/tree/main/skills/mlops/lm-evaluation-harness
Command: npx skills add https://github.com/helix4u/hermes-agent-private --skill evaluating-llms-harness-helix4u

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the rigorous evaluation of Large Language Models (LLMs) against established academic benchmarks, providing objective measures of their capabilities.

Core Features & Use Cases

  • Comprehensive Benchmarking: Evaluates LLMs across 60+ academic benchmarks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag.
  • Model Comparison: Facilitates direct comparison of different LLMs or different versions of the same LLM.
  • Training Progress Tracking: Monitors and visualizes model performance during training to identify improvements or regressions.
  • Use Case: A research team can use this Skill to benchmark their newly trained LLM against state-of-the-art models like GPT-4 and Llama 2, generating a report with key metrics for academic publication.

Quick Start

Evaluate the 'meta-llama/Llama-2-7b-hf' model on the MMLU and GSM8K benchmarks.

Frequently Asked Questions about evaluating-llms-harness

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

FAQPage Schema
How do I evaluate LLM performance on academic benchmarks like MMLU and GSM8K?▼

You can evaluate LLMs by running them against over 60 academic benchmarks like MMLU, GSM8K, and HumanEval. This skill automates the rigorous evaluation process to provide objective measures of model capabilities and generate key metrics for analysis.

Can I use vLLM and HuggingFace transformers for LLM benchmarking?▼

Yes, LLM benchmarking supports various model backends including HuggingFace, vLLM, and APIs. This allows you to comprehensively analyze performance, compare different models, and track training progress using your preferred framework.

What is the best way to compare different LLM models using academic benchmarks?▼

The best way to compare LLMs is to evaluate them across a wide array of established academic benchmarks. This facilitates direct comparison of different models or different versions of the same model to identify performance improvements or regressions.

How do I track LLM training progress and performance regressions?▼

You can track LLM training progress by evaluating the model against academic benchmarks during training. This monitors and visualizes performance over time, helping you identify improvements or regressions in capabilities like reasoning or code generation.

Does lm-eval support evaluating models for academic publication?▼

Yes, lm-eval supports evaluating models for academic publication by generating reports with key metrics. Research teams can benchmark newly trained LLMs against state-of-the-art models like GPT-4 and Llama 2 to provide objective performance measures.

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