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evaluating-llms-harness

Evaluate LLMs against 60+ academic benchmarks using lm-eval.

Updated May 9, 2026
Search Tags:#benchmarking#LLM#academic research#model evaluation#language models
One-click install
npx skills add https://github.com/robertbr123/Linket-Agent --skill evaluating-llms-harness-robertbr123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/robertbr123/Linket-Agent/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/robertbr123/Linket-Agent --skill evaluating-llms-harness-robertbr123

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 benchmarking tool for Large Language Models (LLMs), allowing users to evaluate their models against a wide range of academic benchmarks, compare models, and track training progress.

Core Features & Use Cases

  • Benchmarking 60+ Academic Benchmarks: Evaluate models on tasks like MMLU, GSM8K, HumanEval, and TruthfulQA.
  • Standardized Prompts and Metrics: Use standardized prompts and metrics for reproducible evaluations.
  • Quick Start: Evaluate any HuggingFace model using the command lm_eval --model hf ....

Quick Start

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

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k \
  --batch_size auto

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 language model on academic tasks?▼

Benchmark LLMs by running `lm_eval --model hf --model_args pretrained=[model_name] --tasks [task_list]` to evaluate models using standardized prompts and metrics across 60+ academic benchmarks.

What academic benchmarks are available for evaluating LLMs?▼

Academic benchmarks available for evaluating LLMs include MMLU, GSM8K, HumanEval, and TruthfulQA. The tool evaluates models against 60+ standardized academic benchmarks to assess model quality and track training progress.

Do I need vllm and transformers to evaluate large language models?▼

Yes, you need vllm and transformers to evaluate large language models with this tool. These Python libraries, along with lm-eval, are required dependencies for running model evaluations and benchmarking tasks.

Can I evaluate LLMs on multiple benchmarks simultaneously?▼

Yes, you can evaluate LLMs on multiple benchmarks simultaneously by passing a comma-separated list to the `--tasks` argument, such as `--tasks mmlu,gsm8k`, allowing you to assess model quality across several academic benchmarks in one run.

What is the best way to ensure reproducible LLM evaluation results?▼

The best way to ensure reproducible LLM evaluation results is to use standardized prompts and metrics provided by the lm-eval framework. This guarantees consistent evaluations across 60+ academic benchmarks for accurate model comparison.

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