skills.rest
liumeixin
liumeixinCOMMUNITY
Explore all skills by this author →
GitHub

evaluating-llms-harness

Evaluate large language models across 60+ academic benchmarks with standardized prompts and metrics.

4|Updated May 14, 2026
Search Tags:#benchmarking#HuggingFace#academic research#LLM evaluation#model quality
One-click install
npx skills add https://github.com/liumeixin/hermes-skills --skill evaluating-llms-harness-liumeixin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/liumeixin/hermes-skills/tree/main/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/liumeixin/hermes-skills --skill evaluating-llms-harness-liumeixin

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 provides a comprehensive framework to evaluate large language models across a wide range of academic benchmarks, helping users compare model quality, track training progress, and report results consistently.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Assess models on tasks like MMLU, HumanEval, GSM8K, and more.
  • Standardized Metrics: Aligns with industry standards used by EleutherAI, HuggingFace, and major labs.
  • HuggingFace and vLLM Support: Evaluate models using HuggingFace and vLLM backends for fast inference.

Quick Start

To evaluate a HuggingFace model using this harness, run the following 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 evaluate a HuggingFace model on academic benchmarks like MMLU and GSM8K?▼

To evaluate a HuggingFace model, use the lm_eval command with the hf model backend, specify pretrained model arguments, select tasks like mmlu or gsm8k, and assign a CUDA device with a batch size for inference.

Can I use vLLM for fast LLM evaluation across multiple academic tasks?▼

Yes, vLLM is supported as a backend for fast inference during LLM evaluation, allowing you to assess models on over 60 academic benchmarks using standardized prompts and industry-aligned metrics.

What is the best way to compare LLM quality using standardized metrics?▼

The best way to compare LLM quality is using a standardized evaluation harness that tests models across 60+ academic benchmarks with metrics aligned to EleutherAI, HuggingFace, and major labs for consistent reporting.

Does this LLM evaluation harness support tracking training progress over time?▼

Yes, the LLM evaluation harness supports tracking training progress by providing consistent metrics across standardized academic benchmarks, enabling you to compare model quality snapshots at different training stages.

Do I need transformers and lm-eval libraries to run model evaluation benchmarks?▼

Yes, you need the lm-eval, transformers, and vllm libraries to run model evaluation benchmarks, as these dependencies provide the necessary framework for loading models and executing standardized academic tests.

What academic benchmarks are available for evaluating large language models?▼

Available academic benchmarks for evaluating large language models include MMLU, HumanEval, GSM8K, HellaSwag, and over 60 other standardized tasks for comprehensive quality comparison and academic reporting.

Related Skills

unclehowellunclehowell

evaluating-llms-harness

Benchmark LLMs across 60+ academic benchmarks using the lm-eval harness.

Community
Advanced
moltis-orgmoltis-org
2.8k

evaluating-llms-harness

Evaluate LLM performance across 60+ academic benchmarks with multiple backends.

Official
Advanced
ovachieverovachiever
52

lm-evaluation-harness

Evaluate language models across 60+ academic benchmarks with lm-evaluation-harness.

Community
Advanced
VincentChong123VincentChong123

add-emacs

Configure a local HTTP bridge between NanoClaw and Emacs.

Community
Intermediate