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evaluating-llms-harness

Benchmark large language models on academic and reasoning tasks with standardized prompts and metrics.

Updated Jun 17, 2026
Search Tags:#llm#benchmarking#evaluation#gsm8k#mmlu#model quality
One-click install
npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill evaluating-llms-harness-cxnaive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/skills/mlops/evaluation/evaluating-llms-harness
Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill evaluating-llms-harness-cxnaive

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the difficulty of objectively measuring and comparing the performance of Large Language Models across diverse academic and reasoning benchmarks.

Core Features & Use Cases

  • Standardized Benchmarking: Evaluate models on 60+ benchmarks including MMLU, GSM8K, and HumanEval using consistent prompts and metrics.
  • Training Progress Tracking: Automate periodic evaluations during model training to visualize learning curves and performance improvements.
  • Use Case: A developer training a custom Llama-3 fine-tune can use this Skill to run a standardized MMLU evaluation after every 500 training steps to ensure the model is gaining knowledge without regressing on reasoning tasks.

Quick Start

Run the evaluating-llms-harness skill to benchmark the model meta-llama/Llama-2-7b-hf on the mmlu and gsm8k tasks using a batch size of 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 tasks?▼

You can benchmark LLMs on MMLU and GSM8K by running this Skill with lm-eval, transformers, and vllm to execute standardized evaluation workflows using consistent prompts and metrics across local or remote hardware.

Can I evaluate API-based models and open-source LLMs using the same metrics?▼

Yes, you can evaluate both API-based models and open-source LLMs using the same standardized metrics. The Skill facilitates model comparison and quality assurance across 60+ academic and reasoning benchmarks.

How do I track LLM training progress with automated MMLU evaluations?▼

You can automate periodic MMLU evaluations during model training to visualize learning curves and ensure the model is gaining knowledge without regressing on reasoning tasks after specific training steps.

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

Yes, you need to integrate with lm-eval, transformers, and vllm to execute the evaluation workflows and benchmark Large Language Models on local or remote hardware effectively.

What is the best way to compare LLM performance across diverse academic benchmarks?▼

The best way to compare LLM performance is using standardized prompts and metrics across 60+ benchmarks. This Skill facilitates objective measurement for training progress monitoring and quality assurance.

How many benchmarks are supported for evaluating model quality?▼

The Skill supports evaluating model quality across 60+ benchmarks, including MMLU, GSM8K, and HumanEval, using consistent prompts and metrics to objectively measure and compare LLM performance.

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