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evaluating-llms-harness

Evaluate large language models on 60+ academic benchmarks using lm-eval.

6|3|Updated Jan 29, 2026
Search Tags:#benchmarking#model comparison#language models#llm benchmarking#lm evaluation
One-click install
npx skills add https://github.com/jonnabio/ace-framework --skill evaluating-llms-harness-jonnabio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/jonnabio/ace-framework/tree/main/.ace/packs/ai-research/lm-evaluation-harness
Command: npx skills add https://github.com/jonnabio/ace-framework --skill evaluating-llms-harness-jonnabio

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 provides a standardized framework for benchmarking large language models on 60+ academic benchmarks, allowing for consistent evaluation, model comparison, and training progress tracking.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Supports MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag, and more, covering language understanding, reasoning, and code generation.
  • Model Comparison: Compare model performance across various benchmarks with a single command.
  • Training Progress Tracking: Evaluate checkpoints during training to monitor model progress.

Quick Start

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

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --output_path results/

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 large language models on academic datasets?▼

Benchmark large language models by running standardized evaluations on 60+ academic datasets like MMLU and HumanEval, using the lm_eval command with specified model arguments, tasks, and batch sizes to generate performance results.

Can I use vllm to run LLM evaluations on Hugging Face models?▼

Yes, LLM evaluations support Hugging Face models via the transformers framework and utilize vllm for efficient inference processing during benchmark execution across various academic tasks.

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

Compare LLM performance across benchmarks by executing a single command that evaluates models on multiple academic datasets, generating consistent metrics for language understanding, reasoning, and code generation.

How do I track training progress for language models?▼

Track training progress by evaluating model checkpoints on standard academic benchmarks during the training cycle, allowing teams to monitor performance improvements and compare metrics systematically.

Do I need Python packages to evaluate LLMs on MMLU and GSM8K?▼

Yes, evaluating LLMs requires the Python packages lm-eval, transformers, and vllm to process model inference and compute benchmark scores for datasets like MMLU and GSM8K.

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