evaluating-llms-harness

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

Evaluate LLMs across 60+ academic benchmarks using lm-evaluation-harness.

Updated Jan 12, 2026

Search Tags:#benchmarking #llm evaluation #performance testing #lm-evaluation-harness #model quality #academic benchmarks

Ready to run

npx skills add https://github.com/MesferAli/XCircle --skill evaluating-llms-harness-mesferali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/lm-evaluation-harness
Command: npx skills add https://github.com/MesferAli/XCircle --skill evaluating-llms-harness-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lm-eval, transformers, vllm, and includes references (resource) components.

What problem does it solve?

This Skill provides a standardized framework for evaluating the quality and capabilities of Large Language Models (LLMs) across a wide range of academic benchmarks.

Core Features & Use Cases

Comprehensive Benchmarking: Evaluate LLMs on over 60 academic benchmarks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag.
Model Comparison: Easily compare the performance of different LLMs or track the progress of a single model during training.
Industry Standard: Utilizes the widely adopted lm-evaluation-harness used by major AI labs.
Flexible Backend Support: Works with HuggingFace models, vLLM, and various APIs.

Quick Start

Evaluate the 'meta-llama/Llama-2-7b-hf' model on the MMLU, GSM8K, and HellaSwag tasks using the command lm_eval --model hf --model_args pretrained=meta-llama/Llama-2-7b-hf --tasks mmlu,gsm8k,hellaswag --device cuda:0 --batch_size 8.

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 LLM performance across academic tasks like MMLU and GSM8K?▼

Yes, you can evaluate vLLM models. The framework supports flexible backend execution for HuggingFace models, vLLM, and various API-based models to ensure standardized benchmarking across different platforms.

What is the best way to compare different LLMs on academic benchmarks?▼

You can execute LLM evaluation by running the lm_eval command with specified model arguments, tasks like mmlu or hellaswag, target device such as cuda:0, and batch size to evaluate HuggingFace models on academic benchmarks.

Does the lm-evaluation-harness framework support API-based models?▼

Yes, the lm-evaluation-harness framework supports API-based models. It provides flexible backend support to evaluate HuggingFace models, vLLM, and various APIs for comprehensive model quality assessment.

How do I run LLM evaluation on HuggingFace models using lm-eval?▼

You run LLM evaluation by executing the lm_eval command with specified model arguments, tasks like mmlu or hellaswag, target device such as cuda:0, and batch size to evaluate HuggingFace models on academic benchmarks.