skills.rest
unclehowell
unclehowellCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

evaluating-llms-harness

Benchmark LLMs across 60+ academic benchmarks using the lm-eval harness.

Updated Sep 1, 2021
Search Tags:#api#llm#benchmarking#evaluation#benchmark#huggingface#vllm
One-click install
npx skills add https://github.com/unclehowell/unclehowell --skill evaluating-llms-harness-unclehowell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/unclehowell/unclehowell --skill evaluating-llms-harness-unclehowell

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The lm-evaluation-harness simplifies rigorous benchmarking of language models by providing a unified, open framework to run 60+ academic benchmarks and generate reproducible results.

Core Features & Use Cases

  • Standardized evaluation across a broad task suite (MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, etc.).
  • Supports multiple backends (HuggingFace, vLLM, and OpenAI/Anthropic/Open APIs) for flexible deployment and fair comparisons.
  • Use cases include model quality assessment, model comparison studies, and progress tracking during development or research.

Quick Start

Install the lm-eval harness and run a benchmark suite against your model to generate a standardized report.

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 LLMs across academic tasks like MMLU and GSM8K?▼

Benchmarking LLMs across academic tasks requires a unified evaluation harness to execute standardized tests like MMLU and GSM8K, generating reproducible model comparison results.

Can I evaluate API-based models and HuggingFace backends using the same harness?▼

Yes, the same harness supports evaluating API-based models alongside HuggingFace and vLLM backends, ensuring fair comparisons and flexible deployment across providers.

What is the best way to compare model quality between different LLM providers?▼

The best way to compare model quality between LLM providers is applying a standardized evaluation harness to run a broad task suite uniformly across API, HuggingFace, and vLLM backends.

Do I need Python and lm-eval installed to run LLM benchmarking?▼

Yes, running LLM benchmarking requires Python and the lm-eval harness installed, along with compatible backends such as transformers and vllm to load models and execute benchmarks.

How many academic benchmarks are available for evaluating language models?▼

There are over 60 academic benchmarks available for evaluating language models, including standardized tasks like TruthfulQA, HumanEval, and HellaSwag for comprehensive assessment.

Related Skills

Explore More Software Engineering Skills→
photonics-dhlphotonics-dhl

evaluating-llms-harness

Benchmark language models across 60+ academic benchmarks using a unified evaluation harness.

Community
Advanced
guccangguccang
3

evaluating-llms-harness

Evaluate large language models against standardized benchmarks using lm-evaluation-harness.

Community
Advanced
projectedanxprojectedanx
1

evaluating-llms-harness

Benchmark Large Language Models across academic and reasoning datasets.

Community
Advanced
HarriesHarries

evaluating-llms-harness

Run standardized LLM evaluations across 60+ academic benchmarks.

Community
Advanced