evaluating-llms-harness

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

Evaluate large language models across 60+ academic benchmarks.

20|25|Updated May 30, 2026

Search Tags:#benchmarking #evaluation #model comparison #large language models #language models

Ready to run

npx skills add https://github.com/OpenCoven/coven-cave --skill evaluating-llms-harness-opencoven

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/OpenCoven/coven-cave/tree/main/marketplace/craft-sources/oracles-measure/lm-evaluation-harness
Command: npx skills add https://github.com/OpenCoven/coven-cave --skill evaluating-llms-harness-opencoven

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 helps evaluate the performance of large language models across a comprehensive set of academic benchmarks, enabling users to compare models, track training progress, and report academic results.

Core Features & Use Cases

Benchmarking: Evaluate models on 60+ benchmarks including MMLU, HumanEval, GSM8K, and more.
Model Comparison: Compare different models on multiple tasks to identify strengths and weaknesses.
Training Progress Tracking: Evaluate checkpoints during training to monitor progress.
API Evaluation: Evaluate API-based models from OpenAI, Anthropic, and others.
Custom Tasks: Support for creating custom evaluation tasks tailored to specific domains.

Quick Start

To evaluate the performance of a HuggingFace model on the MMLU benchmark, use the following command:

lm-evaluation-harness --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu

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 large language models on academic benchmarks like MMLU?▼

You can evaluate large language models on academic benchmarks like MMLU using this Skill by specifying the HuggingFace model and target tasks in the lm-evaluation-harness command. It supports evaluating models across 60+ benchmarks to generate performance metrics.

Can I evaluate API-based models from providers like OpenAI and Anthropic?▼

Yes, you can evaluate API-based models from OpenAI and Anthropic. The Skill supports API evaluation alongside local HuggingFace and vLLM models, allowing you to compare different models on multiple tasks to identify their strengths and weaknesses.

What dependencies do I need to run large language model benchmarking?▼

You need lm-eval, transformers, and vllm dependencies installed to run large language model benchmarking. These libraries provide the framework for loading models, executing inference, and evaluating performance across 60+ academic benchmarks.

How does model comparison work across different evaluation tasks?▼

Model comparison works by evaluating different models on multiple tasks to identify their strengths and weaknesses. You can run benchmarks across 60+ academic tasks, track training progress by evaluating checkpoints, and report comprehensive academic results.

Can I create custom evaluation tasks for specific domains?▼

Yes, you can create custom evaluation tasks tailored to specific domains. This Skill supports custom tasks alongside the 60+ built-in academic benchmarks, enabling comprehensive evaluation of large language models for specialized use cases.