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evaluating-llms-harness

Evaluate large language models across 60+ academic and industry benchmarks.

1|Updated May 21, 2026
Search Tags:#benchmarking#model evaluation#large language models#industry standards#academic benchmarks
One-click install
npx skills add https://github.com/blueskies1818/hermesALIone --skill evaluating-llms-harness-blueskies1818
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill evaluating-llms-harness-blueskies1818

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 enables users to evaluate and compare large language models across 60+ academic benchmarks and industry standards, allowing for objective assessment of model performance.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate model performance across tasks like MMLU, GSM8K, and HumanEval.
  • Industry Standard Tasks: Use standardized metrics to compare models and track progress.
  • Quick Evaluation: Execute a simple command to benchmark models across a suite of tasks.
  • Custom Evaluation: Specify particular benchmarks to run based on your research focus.

Quick Start

To evaluate the 'meta-llama/Llama-2-7b-hf' model on MMLU and GSM8K benchmarks, use the following command:

lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k --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 large language models using academic standards?▼

You can benchmark large language models using 60+ academic and industry standard tasks like MMLU, GSM8K, and HumanEval. This allows for objective performance assessment, model comparison, and tracking training progress.

How do I evaluate a Hugging Face model on MMLU and GSM8K benchmarks?▼

To evaluate models on specific benchmarks, execute a command specifying the model, tasks, and parameters. For example, run `lm_eval` with arguments for pretrained model name, tasks like mmlu and gsm8k, few-shot number, and batch size.

Do I need vLLM and transformers to run large language model evaluation?▼

Yes, running large language model evaluation requires `lm-eval`, `transformers`, and `vllm` dependencies. These frameworks facilitate model loading and execution during the academic and industry standard benchmarking process.

Can I run model evaluation and benchmarking on macOS?▼

Yes, model evaluation and benchmarking is compatible with macOS. The framework is designed to run across both Linux and macOS platforms to assess large language model performance.

What is the best way to compare large language model performance across tasks?▼

The best way to compare large language models is using standardized metrics across 60+ academic benchmarks and industry standard tasks. You can specify particular benchmarks to run based on your research focus for precise comparison.

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