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evaluating-code-models

Evaluate code generation models on HumanEval, MBPP, and MultiPL-E using pass@k metrics.

Updated May 2, 2026
Search Tags:#code generation#benchmarking#code execution#multi-language#evaluate#pass@k#human eval
One-click install
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill evaluating-code-models-qcmuu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluating-code-models
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/11-evaluation/bigcode-evaluation-harness
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill evaluating-code-models-qcmuu

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?

This Skill solves the problem of reliably benchmarking code generation models by measuring functional correctness on widely used programming benchmarks.

Core Features & Use Cases

  • Run BigCode Evaluation Harness: Evaluate code models on task suites like HumanEval, MBPP, and MultiPL-E with pass@k metrics.
  • Multi-language coverage: Test translated solutions across 18 languages using MultiPL-E task variants.
  • Safe execution workflow: Use host-only generation and Docker-based execution to avoid risky runtime behavior on your machine.
  • Use cases: Compare competing models, validate instruction-tuned vs base behavior, and quantify coding quality for leaderboards and research reports.

Quick Start

Use the bigcode-evaluation-harness to evaluate your code model on HumanEval with code execution and save the metrics as a JSON file.

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 using pass@k metrics?▼

Benchmark code generation models by evaluating their functional correctness on HumanEval and MBPP suites using the BigCode evaluation harness to calculate pass@k metrics. You need to run task-specific commands with compatible Python packages and enable code execution.

What is the best way to evaluate large language models for multi-language coding quality?▼

Evaluate multi-language coding quality by testing translated solutions across 18 languages using MultiPL-E task variants within the BigCode harness. This approach quantifies how well models generate functionally correct code across diverse programming environments for research benchmarking.

Does the BigCode evaluation harness support HumanEval and MBPP benchmarking?▼

Yes, the BigCode evaluation harness supports benchmarking on HumanEval, MBPP, and MultiPL-E task suites. It measures functional correctness through code execution, allowing you to compare competing models or validate instruction-tuned versus base model behavior.

Can I safely execute generated code during model evaluation without risking my host machine?▼

Safely execute generated code during evaluation by adopting a host-only generation and Docker-based execution workflow. Utilizing the --allow_code_execution flag triggers functional tests in an isolated environment, preventing risky runtime behavior on your local machine.

Do I need specific Python packages to run pass@k evaluation on code models?▼

Yes, running pass@k evaluation requires installing the BigCode evaluation harness alongside compatible Python packages including transformers, accelerate, and datasets. Ensure these dependencies are configured correctly before executing task-specific commands for code generation benchmarking.

Why use functional test execution instead of text similarity for code model benchmarking?▼

Functional test execution is used because it directly measures whether generated code actually runs and produces correct outputs, unlike text similarity. This method provides reliable benchmarking by validating functional correctness on programming tasks like HumanEval and MBPP.

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