Evaluating Nemotron Fine-Tunes with HumanEval and MBPP

Training Without Evaluation Is Guessing

The Nemotron coding assistant example includes explicit benchmark targets in config:

• HumanEval
• MBPP
• custom coding tasks

That alone is a strong design signal. It frames fine-tuning as a measurable engineering process, not only a qualitative prompt exercise.

Metrics That Matter

The evaluation section in examples/nemotron-coding-assistant/config.yaml tracks:

• pass@k
• syntax correctness
• code style
• execution success
• BLEU and CodeBLEU

This mixed metric set is practical. It balances functional correctness and output quality.

Why Multiple Benchmarks

HumanEval and MBPP expose different failure modes:

• one stresses synthesis under strict functional checks
• the other catches more practical task variability

Using both reduces overfitting to one benchmark style and gives better confidence before deployment.

The Production Habit

I strongly recommend pairing every training run with:

1. config snapshot
2. benchmark results
3. promoted model tag only after threshold checks

The example’s structure (training and evaluation modules plus experiment scripts) supports that workflow cleanly.

Practical Takeaway

If your coding model cannot pass a stable local benchmark loop, it is not ready for broader rollout. Benchmark discipline is what turns model tuning from exploration into production engineering.

Related Reading

• /posts/nemotron-training-config-playbook/
• /posts/custom-llm-training/
• /posts/rag-eval-playground-practical-loop/