evaluating-llms-harness

Evaluate language models on 60+ academic benchmarks via HuggingFace and vLLM.

Updated May 8, 2026

Search Tags:#benchmarking #model comparison #language models #evaluation harness #training tracking

Ready to run

npx skills add https://github.com/gztcode/my-skills --skill evaluating-llms-harness-gztcode

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/gztcode/my-skills/tree/main/evaluating-llms-harness
Command: npx skills add https://github.com/gztcode/my-skills --skill evaluating-llms-harness-gztcode

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 enables you to evaluate and compare language models on over 60+ academic benchmarks, supporting popular APIs like HuggingFace, vLLM, and more.

Core Features & Use Cases

Benchmarking: Evaluate models across standardized benchmarks like MMLU, HumanEval, GSM8K, and more.
Model Comparison: Compare different models side-by-side on the same benchmarks.
Training Progress Tracking: Track training progress and analyze results over time.
API Support: Integrate with popular APIs and local language model servers.
Use Case: Use this Skill to determine which model is best suited for your application by comparing their performance across a range of tasks.

Quick Start

Evaluate the Llama-2-7B model on the MMLU benchmark:

lm-evaluation-harness --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 evaluate language models on academic benchmarks like MMLU?▼

You can evaluate language models on academic benchmarks like MMLU by running the evaluation harness with your specified model arguments. This Skill supports over 60 standardized benchmarks and provides comparable metrics for analysis.

Can I use vLLM and HuggingFace transformers for model comparison?▼

Yes, you can use vLLM and HuggingFace transformers for model comparison. The Skill integrates with these frameworks and local servers to evaluate and compare different models side-by-side on the same benchmarks.

What is the best way to track training progress for language models?▼

The best way to track training progress is to evaluate the model at intervals using standardized benchmarks. This Skill provides consistent metrics across tasks like HumanEval and GSM8K to analyze results over time.

What benchmarks are available for evaluating language models?▼

Available benchmarks for evaluating language models include MMLU, HumanEval, and GSM8K, among over 60 academic tasks. These standardized benchmarks provide metrics for model comparison and training progress tracking.

Do I need lm-eval to run the evaluation harness?▼

Yes, you need the lm-eval package, along with transformers and vllm, to run the evaluation harness. These dependencies are required to evaluate models and generate standardized metrics across benchmarks.