evaluating-llms-harness Skill

Source

• Creator: NousResearch
• Repository: NousResearch/hermes-agent
• Skill file: skills/mlops/evaluation/lm-evaluation-harness/SKILL.md

What it does

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

Skill instructions

lm-evaluation-harness - LLM Benchmarking What's inside Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs. Quick start lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics. Installation: bash pip install lm-eval Evaluate any HuggingFace model: bash lmeval --model hf \ --modelargs pretrained=meta-llama/Llama-2-7b-hf \ --tasks mmlu,gsm8k,hellaswag \ --device cuda:0 \ --batchsize 8 View available tasks: bash lmeval --tasks list Common workflows Workflow 1: Standard benchmark evaluation Evaluate model on core benchmarks (MMLU, GSM8K, HumanEval). Copy this checklist: Benchmark Evaluation: - [ ] Step 1: Choose benchmark suite - [ ] Step 2: Configure model - [ ] Step 3: Run evaluation -