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Rachasumanth
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eval-bench

Run model evaluations across NLP, safety, code, and RAG benchmarks.

Updated Feb 17, 2026
Search Tags:#code generation#benchmarking#evaluation#RAG#safety#model testing
One-click install
npx skills add https://github.com/Rachasumanth/text2llm001 --skill eval-bench
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-bench
Source: https://github.com/Rachasumanth/text2llm001/tree/main/workspace/skills/eval-bench
Command: npx skills add https://github.com/Rachasumanth/text2llm001 --skill eval-bench

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the rigorous evaluation of AI models, ensuring their performance, safety, and reliability before deployment.

Core Features & Use Cases

  • Model Evaluation: Run comprehensive benchmarks using lm-evaluation-harness covering common NLP tasks (MMLU, HellaSwag, ARC).
  • Safety & Bias Testing: Assess models for truthfulness, toxicity, bias, and stereotyping with benchmarks like TruthfulQA, ToxiGen, BBQ, and CrowS-Pairs.
  • Code Generation Evaluation: For code models, evaluate performance on HumanEval, MBPP, and MultiPL-E.
  • RAG & Perplexity: Evaluate Retrieval-Augmented Generation quality with Ragas and measure perplexity on held-out datasets.
  • Reporting: Generate human-readable reports summarizing findings and providing release readiness recommendations.

Quick Start

Run a full model evaluation using the eval-bench skill with default settings.

Frequently Asked Questions about eval-bench

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run comprehensive model evaluations for release readiness?▼

Run comprehensive model evaluations by executing diverse benchmark suites covering NLP tasks, safety, bias, and code generation to generate readable reports for release readiness.

How do I benchmark code generation capabilities for AI models?▼

Benchmark code generation capabilities by evaluating model performance on HumanEval, MBPP, and MultiPL-E datasets to determine coding accuracy and reliability.

Can I evaluate Retrieval-Augmented Generation quality and measure perplexity?▼

Evaluate Retrieval-Augmented Generation quality and measure perplexity using Ragas on held-out datasets to ensure deterministic and comparable RAG performance results.

How do I assess AI models for truthfulness, toxicity, and stereotyping bias?▼

Assess AI models for truthfulness, toxicity, and stereotyping bias by running dedicated safety benchmarks like TruthfulQA, ToxiGen, BBQ, and CrowS-Pairs.

Do I need specific harnesses and tools to get deterministic benchmarking results?▼

You need lm-evaluation-harness, Ragas, and specific safety or bias benchmark tools to execute evaluations and ensure deterministic, comparable results across diverse suites.

Does model evaluation testing work for common NLP tasks like MMLU and HellaSwag?▼

Model evaluation testing works for common NLP tasks by leveraging lm-evaluation-harness to cover standard benchmarks including MMLU, HellaSwag, and ARC.

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