evaluating-code-models

To calculate pass@k metrics for HumanEval and MBPP benchmarks, run code generation models with recommended sampling settings like n_samples 200. The evaluation produces pass@1, pass@10, and pass@100 scores and exports results to JSON for functional correctness comparison.

Yes, you can evaluate HuggingFace code generation models on MultiPL-E for multiple languages. The evaluation supports Docker-based execution for non-Python languages to safely measure functional correctness and produce comparison tables for model selection.

The best way to benchmark local code models for functional correctness is using standardized unit-test suites like HumanEval, MBPP, and MultiPL-E. This process computes pass@k metrics and supports options for quantized or private models to ensure reproducible model comparison.

Yes, benchmarking code models with pass@k supports generation-only workflows. You can separate the code generation phase from the evaluation phase, allowing flexible sampling and HuggingFace model integration before executing Docker-safe unit tests.

To run a HumanEval benchmark on a model like bigcode/starcoder2-7b, specify the model, set n_samples to 200, allow code execution, and save the resulting pass@k metrics to a JSON file such as results/starcoder2-humaneval.json.