evaluating-code-models

Benchmark code-generation models across datasets using pass@k metrics.

Updated Apr 3, 2026

Search Tags:#benchmarking #code-generation #pass@k #evaluation-harness #humaneval #multipl-e #mbpp

Ready to run

npx skills add https://github.com/handsomelong922/my-codex-skills --skill evaluating-code-models-handsomelong922

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-code-models
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/bigcode-evaluation-harness
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill evaluating-code-models-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bigcode-evaluation-harness, transformers>=4.25.1, accelerate>=0.13.2, datasets>=2.6.1, and includes references (resource) components.

What problem does it solve?

Benchmarking code-generation models against a suite of benchmarks to obtain objective pass@k scores.

Core Features & Use Cases

Multi-benchmark evaluation across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E.
Cross-language support enabling evaluation across 18 languages via MultiPL-E and related tasks.
Leaderboard-ready metrics with pass@k to compare model quality and progress over time.

Quick Start

Run an evaluation to generate solutions and compute pass@k across the supported benchmarks.

Frequently Asked Questions about evaluating-code-models

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

FAQPage Schema

How do I evaluate code-generation models using pass@k metrics?▼

To evaluate code-generation models using pass@k metrics, benchmark them across datasets like HumanEval, MBPP, and MultiPL-E to generate solutions and compute objective scores. It applies the pass@k metric to obtain leaderboard-ready comparisons for model quality.

Can I benchmark code models across multiple programming languages?▼

Yes, you can benchmark code models across multiple programming languages. The evaluation supports cross-language benchmarking across 18 languages via the MultiPL-E dataset and related tasks, enabling comprehensive leaderboard-style comparisons of model performance.

What's the best way to compare code models on HumanEval and MBPP benchmarks?▼

The best way to compare code models on HumanEval and MBPP is to run a multi-benchmark evaluation that generates solutions and computes pass@k scores. This approach covers 15+ benchmarks to provide objective, leaderboard-ready metrics for tracking model progress.

Do I need the bigcode-evaluation-harness to compute pass@k scores?▼

Yes, you need the bigcode-evaluation-harness as a core dependency to compute pass@k scores. Along with transformers and accelerate, it enables deterministic workflows for generating solutions and evaluating code models across supported datasets.

What datasets are supported for code model benchmarking?▼

Supported datasets for code model benchmarking include HumanEval, MBPP, and MultiPL-E, covering 15+ benchmarks total. These datasets enable multi-benchmark evaluation and cross-language support to generate leaderboard-ready pass@k metrics for code-generation models.