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evaluating-code-models

Evaluate code generation models with pass@k metrics across multiple benchmarks.

3|Updated Feb 21, 2026
Search Tags:#code generation#benchmarking#evaluation#multi-language#pass@k#humaneval
One-click install
npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill evaluating-code-models-ihatesea69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-code-models
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/11-evaluation/bigcode-evaluation-harness
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill evaluating-code-models-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized framework for evaluating the performance of code generation models across a wide array of benchmarks and programming languages.

Core Features & Use Cases

  • Multi-Benchmark Support: Evaluate models on HumanEval, MBPP, MultiPL-E, and over 15 other industry-standard benchmarks.
  • Pass@k Metrics: Quantify model performance using precise pass@k metrics, crucial for comparing coding abilities.
  • Multi-Language Evaluation: Assess code generation capabilities across 18 different programming languages.
  • Use Case: You are developing a new code generation model and need to compare its performance against existing state-of-the-art models like StarCoder2 and CodeLlama on standard benchmarks like HumanEval.

Quick Start

Use the evaluating-code-models skill to benchmark the 'bigcode/starcoder2-7b' model on the 'humaneval' task.

Frequently Asked Questions about evaluating-code-models

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

FAQPage Schema
How do I benchmark code generation models on HumanEval and MBPP?▼

You can evaluate code generation models using the BigCode Evaluation Harness to run benchmarks like HumanEval and MBPP, which reports pass@k metrics to quantify and compare model performance across standardized programming tasks.

What is pass@k metric evaluation for code models?▼

Pass@k metrics evaluate code generation models by calculating the probability that at least one of k generated samples passes the unit tests, providing a standardized measure for comparing coding capabilities.

Can I evaluate instruction-tuned code models across multiple programming languages?▼

Yes, the evaluation framework supports multi-language evaluation for up to 18 programming languages and includes specific features for assessing instruction-tuned code generation models.

What's the best way to compare a custom code model against StarCoder2 and CodeLlama?▼

You can compare your custom code generation model against state-of-the-art models like StarCoder2 and CodeLlama by running standardized benchmark evaluations on tasks like HumanEval to generate comparable pass@k metrics.

Does the BigCode Evaluation Harness support custom task implementation?▼

Yes, the evaluation framework supports custom task implementation, allowing you to define and run custom benchmarks alongside the over 15 industry-standard benchmarks included by default.

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