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DoanNgocCuong
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evaluating-llms-harness

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

Updated Aug 23, 2026
Search Tags:#benchmarking#llm evaluation#model comparison#performance testing#lm-evaluation-harness#academic benchmarks
One-click install
npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill evaluating-llms-harness-doanngoccuong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-llms-harness
Source: https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026/tree/main/.claude/skills/lm-evaluation-harness
Command: npx skills add https://github.com/DoanNgocCuong/continuous-training-pipeline_T3_2026 --skill evaluating-llms-harness-doanngoccuong

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 way to evaluate the performance of Large Language Models (LLMs) across a wide range of academic benchmarks, enabling objective comparison and tracking of model quality.

Core Features & Use Cases

  • Comprehensive Benchmarking: Evaluates LLMs on over 60 academic benchmarks including MMLU, HumanEval, GSM8K, TruthfulQA, and HellaSwag.
  • Model Comparison: Facilitates direct comparison of different LLMs by using industry-standard evaluation methodologies.
  • Training Progress Tracking: Allows users to monitor and visualize model performance improvements during the training process.
  • Use Case: A research team can use this Skill to benchmark their newly trained LLM against established models like Llama 2 and GPT-4 on key reasoning and knowledge tasks, generating a report for publication.

Quick Start

Evaluate the 'meta-llama/Llama-2-7b-hf' model on the MMLU and GSM8K benchmarks.

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 LLMs against academic standards like MMLU and GSM8K?▼

To benchmark LLMs against academic standards, you evaluate models on over 60 benchmarks including MMLU, GSM8K, and HumanEval to assess reasoning, knowledge, and track training progress objectively.

What is the best way to compare different large language models using standardized benchmarks?▼

The best way to compare different large language models is using industry-standard evaluation methodologies across comprehensive academic benchmarks, generating objective reports on model quality and performance differences.

Can I evaluate HuggingFace and vLLM model backends with lm-evaluation-harness?▼

Yes, you can evaluate LLMs using various model backends like HuggingFace and vLLM, facilitating performance testing and quality assessment across different frameworks seamlessly within the same benchmarking suite.

How do I track training progress for a newly trained large language model?▼

You can track training progress by evaluating the newly trained LLM against established models like Llama 2 on key reasoning and knowledge tasks, allowing you to monitor and visualize performance improvements over time.

Does lm-eval support performance testing on specific reasoning tasks like TruthfulQA and HellaSwag?▼

Yes, lm-eval supports performance testing on specific reasoning tasks including TruthfulQA and HellaSwag, providing a comprehensive suite of over 60 academic benchmarks for thorough model quality assessment.

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