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evaluating-llms-harness

Evaluate LLMs on academic benchmarks using lm-eval and HuggingFace models.

Updated Jul 3, 2026
Search Tags:#benchmarking#LLM#model evaluation#large language models#academic benchmarks
One-click install
npx skills add https://github.com/LynxLabVN/office-agent --skill evaluating-llms-harness-lynxlabvn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/LynxLabVN/office-agent --skill evaluating-llms-harness-lynxlabvn

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 allows users to benchmark and evaluate the performance of Large Language Models (LLMs) across a wide range of academic benchmarks, enabling comparison and tracking of model quality and training progress.

Core Features & Use Cases

  • Benchmarking LLMs: Evaluate LLMs across 60+ academic benchmarks such as MMLU, GSM8K, and HumanEval.
  • Standardized Evaluation: Provides standardized prompts and metrics for fair and reproducible evaluations.
  • Custom Checkpoint Evaluation: Supports evaluation of custom model checkpoints and configurations.
  • Quick Start: Use the skill to evaluate any HuggingFace model on a selection of benchmarks with a simple command.

Quick Start

Use the lm-evaluation-harness skill to evaluate a HuggingFace model on MMLU with 5-shot evaluation:

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --device cuda:0

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 a Large Language Model on academic benchmarks?▼

To evaluate an LLM on academic benchmarks, you can use this Skill to run standardized tests across 60+ tasks like MMLU and GSM8K. It uses lm-eval to provide reproducible metrics for comparing model quality and tracking training progress.

Can I benchmark a custom HuggingFace model checkpoint with vllm?▼

Yes, you can benchmark custom HuggingFace model checkpoints using this Skill. It supports evaluating custom configurations and requires libraries like transformers and vllm to execute the standardized academic benchmark tests.

What's the best way to run a 5-shot MMLU evaluation on a HuggingFace model?▼

The best way to run a 5-shot MMLU evaluation is using the lm_eval command with the hf model type. Specify pretrained model arguments, set num_fewshot to 5, and define the batch size and device to execute the benchmark.

Does this LLM evaluation harness support mathematical reasoning and code generation tasks?▼

Yes, this LLM evaluation harness supports mathematical reasoning and code generation tasks. It evaluates performance across comprehensive academic benchmarks, including GSM8K for math and HumanEval for code generation.

Why do I need transformers and vllm to benchmark LLMs?▼

You need transformers and vllm to benchmark LLMs because they handle model loading and optimized inference execution. These dependencies ensure the evaluation harness can efficiently process tasks across standardized academic benchmarks.

Are there limitations when evaluating LLMs with academic benchmarks?▼

Evaluating LLMs with academic benchmarks requires a Python environment and specific libraries like lm-eval. This Skill is designed for researchers needing standardized reproducible evaluations, so custom non-academic prompts may be limited.

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