evaluating-llms-harness

Evaluate language models across standardized benchmarks like MMLU and GSM8K.

5|2|Updated May 26, 2026

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

Ready to run

npx skills add https://github.com/nyxoraAI/Nyxora --skill evaluating-llms-harness-nyxoraai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/nyxoraAI/Nyxora/tree/main/packages/core/playbooks/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/nyxoraAI/Nyxora --skill evaluating-llms-harness-nyxoraai

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 LLMs, enabling users to evaluate and compare the performance of various models across a wide range of academic benchmarks.

Core Features & Use Cases

Standardized Benchmarks: Evaluate LLMs using 60+ standardized benchmarks (MMLU, GSM8K, HumanEval, etc.).
Model Comparison: Compare the performance of different models side-by-side.
Training Progress Tracking: Monitor the progress of model training with periodic evaluations.
Quick Start: Run a basic evaluation with a single command.

Quick Start

Run the following command to evaluate the model 'meta-llama/Llama-2-7b-hf' on the MMLU benchmark:

lm-evaluation-harness --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 LLMs on MMLU and GSM8K using standardized metrics?▼

To benchmark LLMs on MMLU and GSM8K, you can run a single command specifying the model, task, and batch size to evaluate performance across 60+ standardized benchmarks for understanding, reasoning, and factual knowledge.

What is the best way to compare language models side-by-side for research publication?▼

The best way to compare language models side-by-side for research publication is using a benchmarking suite that evaluates models across multiple academic benchmarks like MMLU and HumanEval, providing standardized metrics for accurate comparison.

Can I use vllm and transformers to track LLM training progress with periodic evaluations?▼

Yes, you can use vllm and transformers to track LLM training progress by running periodic evaluations across standardized benchmarks, monitoring improvements in understanding, reasoning, and factual knowledge over time.

Do I need lm-eval to evaluate language models across multiple benchmarks?▼

Yes, you need the lm-eval Python package to evaluate language models across multiple benchmarks, along with transformers and vllm, to perform standardized benchmarking operations for model comparison and research.

Does evaluating LLMs with this benchmark suite support few-shot evaluation?▼

Yes, evaluating LLMs with this benchmark suite supports few-shot evaluation, allowing you to specify parameters like num_fewshot in the command line to test model performance under different prompting conditions.