evaluating-code-models

This Skill evaluates code generation models against HumanEval, MBPP, and MultiPL-E benchmarks using pass@k metrics. It calculates functional correctness to provide detailed performance analysis for comparing models like StarCoder2 and CodeLlama.

You can benchmark LLM code generation across 18 programming languages using the MultiPL-E benchmark. This Skill provides multi-language code assessment to deliver detailed performance analysis for various code generation models.

Comparing your custom LLM against StarCoder2 and CodeLlama requires running this Skill on standard benchmarks like HumanEval. It generates objective pass@k metrics to identify specific areas where your model needs improvement.

You need the bigcode-evaluation-harness library to measure pass@k functional correctness. This Skill requires it for execution, alongside dependencies like transformers, accelerate, and datasets for comprehensive code model benchmarking.

Supported benchmarks for evaluating code generation performance include HumanEval, MBPP, MultiPL-E, and over 15 others. This Skill measures functional correctness using pass@k metrics across these diverse industry-standard datasets.