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evaluating-llms-harness

Evaluate large language models on standardized benchmarks like MMLU and GSM8K.

2|Updated May 22, 2026
Search Tags:#benchmarking#model comparison#performance evaluation#large language models#training progress tracking
One-click install
npx skills add https://github.com/519lab/thoth-agent --skill evaluating-llms-harness-519lab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/519lab/thoth-agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/519lab/thoth-agent --skill evaluating-llms-harness-519lab

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 addresses the need for benchmarking and comparing the performance of large language models across various benchmarks like MMLU, GSM8K, HumanEval, etc. It solves the challenge of assessing the model quality, progress during training, and inter-model comparison without manually executing tasks for each benchmark.

Core Features & Use Cases

  • Automated Benchmarking: Runs evaluations across multiple benchmark datasets (e.g., MMLU, GSM8K) using predefined tasks and metrics.
  • Multi-Model Evaluation: Allows benchmarking against different language models and comparing their performance.
  • Training Progress Tracking: Evaluates model checkpoints during training to monitor progress.
  • Model Comparison: Facilitates model comparison by generating comparison tables based on evaluation results.

Quick Start

Evaluate the LLM model with 'meta-llama/Llama-2-7b-hf' on MMLU and GSM8K benchmarks using:

lm-eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k \
  --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 evaluate large language models on MMLU and GSM8K benchmarks?▼

To evaluate large language models on MMLU and GSM8K benchmarks, run automated evaluations using predefined tasks and metrics. This process assesses model quality and facilitates inter-model comparison without manually executing tasks for each dataset.

Can I track training progress by evaluating model checkpoints?▼

Yes, you can track training progress by evaluating model checkpoints during training. This monitors performance improvements over time and generates comparison tables based on the evaluation results.

What dependencies do I need to run automated benchmarking for LLMs?▼

You need the lm-eval, transformers, and vllm dependencies installed to execute automated benchmarking for large language models. These frameworks provide the execution environment required for running standardized evaluations.

Does this benchmarking approach work with Hugging Face models?▼

Yes, this benchmarking approach works with Hugging Face models by specifying the hf model type and passing the pretrained model arguments. This allows direct evaluation of models like meta-llama/Llama-2-7b-hf.

What is the best way to compare the performance of different large language models?▼

The best way to compare the performance of different large language models is using automated multi-model evaluation. It runs evaluations across multiple benchmark datasets and generates comparison tables based on the results.

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