skills.rest
AXGZ21
AXGZ21COMMUNITY
Explore all skills by this author →
GitHub

evaluating-llms-harness

Evaluate LLMs against 60+ academic benchmarks like MMLU and GSM8K.

Updated Aug 23, 2026
Search Tags:#benchmarking#llm evaluation#performance metrics#lm-evaluation-harness#model quality#academic benchmarks
One-click install
npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill evaluating-llms-harness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/AXGZ21/hermes-agent-railway/tree/main/skills/mlops/lm-evaluation-harness
Command: npx skills add https://github.com/AXGZ21/hermes-agent-railway --skill evaluating-llms-harness

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the evaluation of Large Language Models (LLMs) against a comprehensive suite of academic benchmarks, providing standardized metrics for model quality and performance.

Core Features & Use Cases

  • Comprehensive Benchmarking: Evaluates LLMs across 60+ academic benchmarks including MMLU, HumanEval, GSM8K, and TruthfulQA.
  • Model Comparison: Facilitates direct comparison of different LLMs or different versions of the same LLM.
  • Training Progress Tracking: Enables monitoring of model performance during training cycles.
  • Industry Standard: Utilizes the widely adopted lm-evaluation-harness framework used by major AI labs.
  • Use Case: A researcher wants to compare the reasoning capabilities of two new LLMs. They can use this Skill to run both models through MMLU and GSM8K benchmarks and get comparable accuracy scores.

Quick Start

Use the evaluating-llms-harness skill to evaluate the 'meta-llama/Llama-2-7b-hf' model on the 'mmlu' and 'gsm8k' tasks.

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 against academic benchmarks like MMLU and GSM8K?▼

This Skill evaluates Large Language Models against 60+ academic benchmarks, including MMLU, GSM8K, and HumanEval, to provide standardized performance metrics for model quality assessment and comparison.

What is the best way to compare the performance of different LLMs?▼

The best way to compare LLM performance is by running models through the lm-evaluation-harness framework. This Skill facilitates direct comparison of different LLMs by evaluating them against identical academic benchmark suites.

Can I use vLLM and HuggingFace models for LLM benchmarking?▼

Yes, this LLM evaluation harness supports HuggingFace, vLLM, and API-based models. It allows you to benchmark various model architectures against academic standards to assess quality and track training progress.

How does the lm-evaluation-harness framework work for tracking LLM training progress?▼

The lm-evaluation-harness framework tracks LLM training progress by running models through standardized test sets. This Skill enables monitoring of model performance during training cycles by providing consistent metrics across 60+ academic benchmarks.

Do I need vLLM or transformers installed to run LLM evaluation tasks?▼

Yes, you need lm-eval, transformers, and vllm dependencies installed to run LLM evaluation tasks. These libraries provide the necessary backend to load, execute, and benchmark models locally or via API connections.

Related Skills

Explore More Education & Research Skills→
Brilly-BohyunBrilly-Bohyun

evaluating-llms-harness

Evaluates and benchmarks LLMs on 60+ academic scenarios with standardized metrics.

Community
Advanced
pmcdowallpmcdowall

evaluating-llms-harness

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

Community
Advanced
projectedanxprojectedanx
1

evaluating-llms-harness

Benchmark Large Language Models across academic and reasoning datasets.

Community
Advanced
robertbr123robertbr123

evaluating-llms-harness

Evaluate LLMs against 60+ academic benchmarks using lm-eval.

Community
Advanced