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evaluating-llms-harness

Evaluate language models on academic benchmarks like MMLU and GSM8K.

Updated May 25, 2026
Search Tags:#benchmarking#machine learning#evaluation#model comparison#language model
One-click install
npx skills add https://github.com/webdevtodayjason/subctl-rust --skill evaluating-llms-harness-webdevtodayjason
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/webdevtodayjason/subctl-rust/tree/main/skills/lm-evaluation-harness
Command: npx skills add https://github.com/webdevtodayjason/subctl-rust --skill evaluating-llms-harness-webdevtodayjason

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill allows users to evaluate the quality and capabilities of various language models (LLMs) through standardized benchmarks and tests.

Core Features & Use Cases

  • Standardized Benchmarks: Access a suite of 60+ academic benchmarks (MMLU, GSM8K, HumanEval, etc.) to evaluate LLM performance.
  • Custom Evaluation: Create custom benchmarks for domain-specific evaluation needs.
  • Model Comparison: Compare the performance of different LLMs on a unified set of metrics.
  • Use Case: For a researcher comparing two LLMs, this Skill can run both models on the same set of benchmarks and generate comparison tables and graphs to visualize the results.

Quick Start

To evaluate a HuggingFace model on the MMLU benchmark, use the following command:

lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu --num_fewshot 5 --batch_size 8

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 and evaluate language models using standardized tests?▼

You can evaluate language models by running them against 60+ academic benchmarks like MMLU, GSM8K, and HumanEval. This Skill executes standardized tests using HuggingFace models to generate metrics for quality assessment and comparison.

Can I compare the performance of different LLMs on a unified set of metrics?▼

Yes, you can compare different language models by running them on the same benchmarks. This Skill generates comparison tables and graphs to visualize the results, helping you evaluate relative performance across a unified set of metrics.

How do I create custom benchmarks for domain-specific language model evaluation?▼

You can create custom benchmarks for domain-specific evaluation by defining tailored tests within the framework. This allows you to assess language model performance on specialized tasks beyond the standard academic benchmarks provided.

Do I need lm-eval, transformers, and vllm to run model comparison benchmarks?▼

Yes, evaluating language models with this Skill requires lm-eval, transformers, and vllm dependencies. You also need access to HuggingFace models and APIs to execute the benchmarks and generate performance comparisons.

What's the best way to evaluate a HuggingFace model on the MMLU benchmark?▼

The best way to evaluate a HuggingFace model on the MMLU benchmark is using the lm_eval command with the hf model argument. You can specify the pretrained model and configure few-shot examples and batch size for the test run.

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