evaluating-code-models

Evaluates code generation models using standardized benchmarks like HumanEval and MBPP.

11.5k|842|Updated Nov 3, 2025

Search Tags:#code generation #benchmarking #evaluation #pass@k #human-eval #bigcode

Ready to run

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evaluating-code-models
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/11-evaluation/bigcode-evaluation-harness
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the evaluation of code generation models across a wide array of benchmarks, providing standardized metrics for performance comparison.

Core Features & Use Cases

Multi-Benchmark Evaluation: Supports HumanEval, MBPP, MultiPL-E, and over 15 other benchmarks.
Pass@k Metrics: Measures functional correctness with industry-standard pass@k metrics.
Multi-Language Support: Evaluates models across 18 different programming languages.
Use Case: When comparing two new code generation models, use this Skill to run them both against HumanEval and MBPP to objectively determine which performs better.

Quick Start

Evaluate the 'bigcode/starcoder2-7b' model on the HumanEval benchmark.

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 HumanEval and MBPP?▼

To benchmark code generation models using HumanEval and MBPP, this Skill automates the evaluation process and measures functional correctness with pass@k metrics. It provides objective performance comparisons across these standardized benchmarks.

What is the pass@k metric in code generation evaluation?▼

The pass@k metric in code generation evaluation measures functional correctness by calculating the probability that at least one of k generated samples passes the tests. This Skill utilizes it as an industry standard for benchmarking.

Can I evaluate multi-language code generation across different programming languages?▼

Yes, you can evaluate multi-language code generation across 18 different programming languages using the MultiPL-E benchmark. This Skill facilitates testing multi-language support to measure code generation quality comprehensively.

Does the BigCode Evaluation Harness support custom code generation benchmarks?▼

The BigCode Evaluation Harness supports over 15 standardized benchmarks including HumanEval, MBPP, and MultiPL-E. While it provides extensive multi-benchmark evaluation, custom benchmark integration depends on the harness's configuration capabilities.

What dependencies do I need to run code generation benchmarks?▼

To run code generation benchmarks, you need the bigcode-evaluation-harness along with transformers, accelerate, and datasets libraries. These dependencies provide the environment for loading models and executing standardized benchmark evaluations.