evaluating-llms-harness

Benchmark language models on standardized and custom tasks.

1|Updated Jun 9, 2026

Search Tags:#gsm8k #mmlu #llm benchmarking #humanEval #benchmarking suite

Ready to run

npx skills add https://github.com/lxt070409/hermes-skills --skill evaluating-llms-harness-lxt070409

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/lxt070409/hermes-skills/tree/main/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/lxt070409/hermes-skills --skill evaluating-llms-harness-lxt070409

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 provides a comprehensive benchmarking solution for evaluating the performance of language models across various tasks and metrics, enabling users to compare and track the progress of their models.

Core Features & Use Cases

Standardized Benchmarks: Evaluates LLMs on 60+ academic benchmarks including MMLU, GSM8K, HumanEval, etc.
Customizable: Supports custom benchmarks and task configurations.
Multi-Model Comparison: Enables comparison of multiple models side-by-side.
Industry Standard: Used by EleutherAI, HuggingFace, and major research labs.
Use Case: A researcher wants to compare the performance of two different LLMs on multiple benchmarks to choose the best one for their project.

Quick Start

Load the skill and evaluate a model on MMLU:

skill_view(name='evaluating-llms-harness')
lm-evaluation-harness evaluate --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu

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 on standardized tasks like MMLU and GSM8K?▼

To benchmark LLMs on standardized tasks, you can evaluate models on 60+ academic benchmarks including MMLU and GSM8K using the lm-evaluation-harness, tracking performance metrics across various tasks.

Can I compare multiple language models side-by-side using lm-eval and vllm?▼

Yes, you can compare multiple language models side-by-side using lm-eval and vllm to evaluate and track performance differences across various standardized benchmarks for model selection.

Does this LLM benchmarking suite support custom task configurations?▼

Yes, this LLM benchmarking suite supports custom benchmarks and task configurations, allowing you to evaluate language models on tailored datasets beyond the 60+ standard academic benchmarks.

What libraries do I need to evaluate language models with this benchmarking harness?▼

You need the lm-eval, transformers, and vllm libraries installed to evaluate language models with this benchmarking harness, enabling comprehensive performance tracking and multi-model comparison.

How do I evaluate a HuggingFace model on the HumanEval benchmark?▼

To evaluate a HuggingFace model on the HumanEval benchmark, load the skill and run the lm-evaluation-harness command with the hf model type, specifying the pretrained model arguments and the humaneval task.

When should I use a standardized benchmarking suite for language model evaluation?▼

You should use a standardized benchmarking suite for language model evaluation during academic research, model selection, and performance tracking to ensure consistent, industry-standard metrics across 60+ benchmarks.