skills.rest
zaiyemeiyou404
zaiyemeiyou404COMMUNITY
Explore all skills by this author →
GitHub

evaluating-llms-harness

Evaluate LLM performance across benchmarks using lm-eval, transformers, and vllm.

Updated May 25, 2026
Search Tags:#llm#benchmarking#ai research#gsm8k#mmlu#humaneval
One-click install
npx skills add https://github.com/zaiyemeiyou404/Hermes --skill evaluating-llms-harness-zaiyemeiyou404
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/zaiyemeiyou404/Hermes/tree/main/backup/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/zaiyemeiyou404/Hermes --skill evaluating-llms-harness-zaiyemeiyou404

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 benchmarking solution for evaluating Large Language Models (LLMs) across a wide range of tasks and metrics, significantly speeding up the model evaluation process and allowing for more detailed comparisons.

Core Features & Use Cases

  • 60+ Academic Benchmarks: Evaluate LLMs on tasks like MMLU, GSM8K, and HumanEval.
  • Industry Standard: Used by leading AI research organizations and companies.
  • Fast Evaluation: Utilizes optimized evaluation frameworks for efficient benchmarking.
  • Use Case: Use this Skill to evaluate the performance of a new LLM on a specific task, such as mathematical reasoning or code generation, and compare it against established models.

Quick Start

Run the following command to evaluate the MMLU benchmark on the model 'gpt2':

lm_eval --model hf \
  --model_args pretrained=gpt2 \
  --tasks mmlu \
  --num_fewshot 5 \
  --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 an LLM on academic tasks like MMLU and GSM8K?▼

Run the lm_eval command specifying the model, task name like mmlu or gsm8k, few-shot examples, and batch size to evaluate LLM performance across standardized academic benchmarks.

What benchmarks are available for evaluating large language models?▼

Evaluating large language models supports over 60 academic benchmarks including MMLU, GSM8K, and HumanEval, covering mathematical reasoning, code generation, and comprehensive knowledge testing.

Does this LLM evaluation framework require vllm and transformers?▼

Yes, executing LLM evaluation requires vllm and transformers dependencies alongside lm-eval, providing an optimized framework for fast and efficient model benchmarking across various tasks.

Can I use Hugging Face models to evaluate code generation performance?▼

Yes, you can evaluate code generation performance using Hugging Face models by passing pretrained model arguments to the evaluation framework and selecting the HumanEval benchmark task.

What is the best way to compare a new LLM against established models?▼

The best way to compare a new LLM against established models is using a standardized benchmarking framework applying industry-standard metrics across a wide range of tasks for detailed performance comparison.

Related Skills

Explore More Software Engineering Skills→
gqf2008gqf2008

evaluating-llms-harness

Automate language model evaluation across 60+ benchmarks using lm-evaluation-harness.

Community
Advanced
moltis-orgmoltis-org
2.8k

evaluating-llms-harness

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

Official
Advanced
pmcdowallpmcdowall

serving-llms-vllm

Configure vLLM deployments with OpenAI-compatible APIs and performance tuning.

Community
Advanced
DaddyElonMusk69DaddyElonMusk69

evaluating-llms-harness

Evaluate language models across 60+ academic benchmarks with multi-backend support.

Community
Advanced