evaluating-llms-harness

Name: evaluating-llms-harness
Availability: InStock
Author: Automate

Evaluate LLMs across academic benchmarks with configurable backends and distributed GPU inference.

1|Updated Apr 24, 2026

Search Tags:#llm evaluation #model comparison #mmlu #humaneval #academic benchmarks #lm-eval #model benchmarking

Ready to run

npx skills add https://github.com/automatedigital/spark --skill evaluating-llms-harness-automatedigital

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/automatedigital/spark/tree/main/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/automatedigital/spark --skill evaluating-llms-harness-automatedigital

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the inconsistent, time-consuming process of manually benchmarking large language models, ensuring you use standardized prompts and metrics to get fair, reproducible results for model comparison, research reporting, or training progress tracking.

Core Features & Use Cases

Standardized Benchmarking: Evaluate LLMs across 60+ academic benchmarks including MMLU, HumanEval, GSM8K, and TruthfulQA, using the same prompts and metrics as EleutherAI, HuggingFace, and major AI labs.
Multi-Backend Support: Test HuggingFace models, vLLM-optimized deployments, and API-based models from OpenAI, Anthropic, and other providers.
Use Case: Use this Skill to compare the performance of your fine-tuned custom model against leading open and closed-source LLMs, or track how your model's accuracy on core reasoning benchmarks improves over the course of training.

Quick Start

Use the lm-evaluation-harness skill to run a 5-shot evaluation of the meta-llama/Llama-2-7b-hf model on the MMLU, GSM8K, and HellaSwag benchmarks and save the results to a local JSON file.

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 LLMs using standardized academic benchmarks?▼

To evaluate LLMs using standardized academic benchmarks, this Skill runs models across 60+ tests like MMLU and HumanEval, applying consistent prompts and metrics to ensure fair, reproducible results for model comparison and research reporting.

Can I benchmark API-based models from OpenAI and Anthropic against HuggingFace models?▼

Yes, you can benchmark API-based models against HuggingFace models. This Skill supports multi-backend evaluation, allowing you to test HuggingFace Transformers, vLLM deployments, and API-based models from OpenAI and Anthropic using the same standardized metrics.

How do I track my fine-tuned model's accuracy during training?▼

You can track fine-tuned model accuracy during training by running this Skill periodically to measure performance on core reasoning benchmarks like GSM8K, generating standardized reports to visualize accuracy improvements over the training course.

Does this LLM evaluation tool support distributed evaluation across multiple GPUs?▼

Yes, this LLM evaluation tool supports distributed evaluation across multiple GPUs. It handles large model inference by applying configurable few-shot settings and batch processing to optimize benchmarking workflows for extensive open and closed-source LLMs.

What is the best way to compare a custom model against leading LLMs?▼

The best way to compare a custom model against leading LLMs is using this Skill to run standardized 60+ academic benchmark tests, ensuring you apply identical prompts and metrics for fair cross-model comparison and reproducible evaluation results.