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evaluating-llms-harness

Run standardized LLM benchmarks on HuggingFace and vLLM backends with lm-eval.

1|Updated May 16, 2026
Search Tags:#benchmarking#llm evaluation#vllm#gsm8k#mmlu#human-eval#lm-eval
One-click install
npx skills add https://github.com/devMoez/titan --skill evaluating-llms-harness-devmoez
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/devMoez/titan/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/devMoez/titan --skill evaluating-llms-harness-devmoez

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of evaluating and comparing LLM quality in a standardized, reproducible way across well-known benchmarks such as MMLU, GSM8K, HumanEval, and TruthfulQA.

Core Features & Use Cases

  • Run standardized LLM benchmarks: Execute 60+ academic and code-generation tasks (e.g., MMLU, GSM8K, HellaSwag, ARC, HumanEval) to produce comparable metrics across models.
  • Support multiple inference backends: Evaluate HuggingFace models and vLLM backends (including faster inference paths) while keeping the task interface consistent.
  • Workflow for analysis and iteration: Track results for model comparison, automate checkpoint evaluation during training, and troubleshoot common evaluation issues like OOM and mismatched task settings.

Use Case: You’re releasing a new model checkpoint and need to report consistent benchmark scores (accuracy/exact match/F1-style metrics depending on the task) before deciding whether it’s ready for deployment.

Quick Start

Run lm-evaluation-harness to benchmark a HuggingFace model on MMLU, GSM8K, and HellaSwag with: lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k,hellaswag --device cuda:0 --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 a HuggingFace model on MMLU and GSM8K?▼

To benchmark a HuggingFace model on MMLU and GSM8K, run the lm-evaluation-harness with the `hf` backend, specifying the pretrained model name, target tasks, and CUDA device for reproducible academic evaluation metrics.

Can I use vLLM for faster LLM evaluation on standard academic tasks?▼

Yes, you can use the vLLM backend for faster LLM evaluation on standard academic tasks. The evaluation harness supports vLLM-backed models to accelerate inference while maintaining a consistent task interface across benchmarks like MMLU and HumanEval.

What is the best way to compare LLM quality across different model checkpoints?▼

The best way to compare LLM quality across checkpoints is running standardized academic tasks like MMLU, HellaSwag, and TruthfulQA using lm-eval. This produces comparable metrics such as accuracy and exact match scores for reliable reproducible evaluation.

Does lm-eval support running multiple academic benchmarks like HumanEval and ARC in one command?▼

Yes, lm-eval supports running multiple academic benchmarks like HumanEval and ARC in one command. You can specify a comma-separated list of tasks such as `mmlu,gsm8k,hellaswag` in the execution arguments to generate combined evaluation results.

Why does my LLM evaluation run out of memory during benchmark testing?▼

LLM evaluation runs out of memory during benchmark testing when batch sizes exceed available GPU memory. Troubleshoot common evaluation issues like OOM by configuring appropriate batch sizes and verifying task settings when evaluating large HuggingFace models.

When do I need to run offline LLM evaluations with standardized tasks?▼

You need to run offline LLM evaluations with standardized tasks when releasing a new model checkpoint and requiring consistent benchmark scores. This reproducible evaluation confirms model readiness for deployment across reasoning and code generation domains.

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