Nemotron Training Config Playbook

Why The Config Is The Product

examples/nemotron-coding-assistant/config.yaml is one of the most useful files in the repo because it captures the full lifecycle in one place:

• model source
• dataset curation
• training method
• evaluation targets
• serving and logging behavior

This is exactly what most local fine-tuning projects miss.

Model And Serving Defaults

The Nemotron section uses:

• base model: nvidia/Llama-3.1-Nemotron-Nano-8B-v1
• serving preference: vllm with ollama fallback
• conservative generation defaults for coding (low temperature, high max tokens)

The fallback story is critical in local environments where GPU or service availability changes across machines.

Dataset Pipeline Decisions

The datasets block combines multiple source types (huggingface, local) and quality controls:

• deduplication strategy
• min/max length
• language filters
• train/val/test split ratios

That means data quality is treated as configuration, not hidden code behavior.

Training Method Controls

The config supports SFT, LoRA, QLoRA, DPO, and full fine-tune profiles.

For most local hardware, QLoRA is the practical baseline. The profile includes:

• 4-bit quantization settings
• LoRA rank/alpha/dropout
• target module lists

This keeps memory usage realistic while preserving adaptation quality.

Practical Takeaway

If you want reproducible local model work, invest in a single composable config surface like this. It reduces rework, improves team handoff, and makes evaluation comparisons more trustworthy.

Related Reading

• /posts/custom-llm-training/
• /posts/nemotron-evaluation-humaneval-mbpp/
• /posts/mlops-mlflow-opentelemetry/