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evaluating-llms-harness

Benchmark LLMs on academic and industry benchmarks using lm-eval.

1|Updated Feb 17, 2026
Search Tags:#benchmarking#machine learning#evaluation#LLM#large language model
One-click install
npx skills add https://github.com/brittaniebuffiecsu/zerogravityclaw --skill evaluating-llms-harness-brittaniebuffiecsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/brittaniebuffiecsu/zerogravityclaw/tree/main/src/hermes-core/skills/mlops/evaluation/lm-evaluation-harness
Command: npx skills add https://github.com/brittaniebuffiecsu/zerogravityclaw --skill evaluating-llms-harness-brittaniebuffiecsu

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 platform for benchmarking and evaluating the performance of large language models (LLMs) on a wide range of academic and industry benchmarks.

Core Features & Use Cases

  • Benchmarking: Evaluate LLMs on 60+ academic benchmarks including MMLU, GSM8K, HumanEval, and more.
  • Comparison: Compare the performance of different LLMs side-by-side.
  • Training Progress Tracking: Monitor the training progress of LLMs by evaluating checkpoints during training.
  • API Evaluation: Evaluate API-based LLMs through a unified TemplateAPI interface.
  • Custom Tasks: Create and evaluate custom tasks for domain-specific evaluation.
  • Distributed Evaluation: Run evaluations across multiple GPUs for faster performance.

Quick Start

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

lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size 8 \
  --device cuda:0

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 a HuggingFace model on the MMLU dataset?▼

You can evaluate LLMs on 60+ academic benchmarks including MMLU, GSM8K, and HumanEval. The platform supports comparing different models side-by-side, monitoring training progress via checkpoints, and evaluating API-based models through a unified TemplateAPI interface.

Can I evaluate API-based large language models without local weights?▼

Yes, API-based LLMs can be evaluated through a unified TemplateAPI interface. This allows you to benchmark remote models against academic and industry benchmarks without downloading or hosting the model weights locally.

Does LLM evaluation support distributed evaluation across multiple GPUs?▼

Yes, LLM evaluation supports distributed evaluation across multiple GPUs. This feature allows you to run large language model benchmarks faster by distributing the evaluation workload across several graphics processing units simultaneously.

How do I create custom tasks for domain-specific LLM evaluation?▼

You can create custom tasks for domain-specific LLM evaluation by defining your own benchmark parameters within the framework. This enables you to evaluate large language models on specialized datasets tailored to your specific industry or academic requirements.

What Python libraries are required to run large language model benchmarks?▼

Running large language model benchmarks requires the lm-eval, transformers, and vllm Python libraries. These dependencies facilitate HuggingFace model loading, high-throughput inference, and the execution of the benchmark evaluation scripts.

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