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lm-evaluation-harness

Run standardized LLM benchmarks across 60+ academic tasks.

247|22|Updated Dec 11, 2024
Search Tags:#benchmarking#llm evaluation#model comparison#vllm#mmlu#humaneval#academic benchmarks
One-click install
npx skills add https://github.com/graniet/kheish --skill lm-evaluation-harness-graniet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lm-evaluation-harness
Source: https://github.com/graniet/kheish/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/graniet/kheish --skill lm-evaluation-harness-graniet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the manual effort of building and maintaining custom LLM evaluation pipelines, letting you quickly measure model quality against widely accepted academic benchmarks without repetitive setup work.

Core Features & Use Cases

  • Standardized Benchmarking: Evaluate models across 60+ academic benchmarks including MMLU, GSM8K, HumanEval, and TruthfulQA using industry-standard prompts and metrics for reproducible results.
  • Multi-Backend Support: Test HuggingFace open models, vLLM-optimized deployments, and closed API models (OpenAI, Anthropic) in a single unified workflow.
  • Use Case: ML researchers can use this Skill to compare new model checkpoints against state-of-the-art baselines, track training progress across iterations, and report standardized metrics for academic publications.

Quick Start

Use the lm-evaluation-harness skill to evaluate the meta-llama/Llama-2-7b-hf model on the MMLU, GSM8K, and HellaSwag benchmarks with 5-shot prompting, then save the full results to a local JSON file for analysis.

Frequently Asked Questions about lm-evaluation-harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark LLMs on academic benchmarks like MMLU and HumanEval?▼

Benchmarking LLMs on MMLU and HumanEval uses standardized evaluation pipelines to apply industry-standard prompts and metrics, generating reproducible model quality comparisons without manual setup.

Can I evaluate models deployed with vLLM alongside OpenAI and Anthropic APIs?▼

Evaluating models deployed with vLLM alongside OpenAI and Anthropic APIs is supported through a unified multi-backend workflow that tests HuggingFace, vLLM, and closed API models together.

What is the best way to compare new model checkpoints against state-of-the-art baselines?▼

Comparing new model checkpoints against state-of-the-art baselines requires standardized academic benchmarks with configurable few-shot settings and batch sizes to track training progress and report reproducible metrics.

Does LLM evaluation harness support custom task creation for domain-specific testing?▼

LLM evaluation harnesses support custom task creation, allowing you to define domain-specific testing needs alongside standard benchmarks like GSM8K and TruthfulQA.

How do I configure few-shot prompting and distributed inference for model comparison?▼

Configuring few-shot prompting and distributed inference involves adjusting batch sizes and inference settings in the evaluation pipeline to support reproducible model comparisons across large workloads.

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