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# Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages

**[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 16 min read · 2 upvotes · 0 comments

## Summary

NVIDIA shows how to post-train its open Nemotron models with locale-grounded persona datasets so agents follow local rules, not just local language. The tutorial fine-tunes Nemotron 3.5 Lightning for a French public-service assistant using the Nemotron-Personas-France dataset, built with Pleias as part of a persona data program covering 12 countries. Steps cover defining a behavior contract, generating synthetic persona-based conversations with vLLM, fine-tuning with LoRA via NVIDIA NeMo Automodel, merging the adapter, and evaluating against the base model. In one example, the base model suggests a postal passport application for a resident who can no longer travel, while the fine-tuned model describes a home-visit option requiring a medical certificate less than three months old.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://developer.nvidia.com/blog/fine-tuning-nvidia-nemotron-to-know-local-rules-not-just-local-languages>

## Questions this post answers

### How do I fine-tune NVIDIA Nemotron 3.5 Lightning for a local-language customer service agent?

Use the NVIDIA Nemotron repo's workflow: define a behavior contract, generate synthetic persona-based conversations with the sdg/data_designer step using a locale-grounded persona dataset, then fine-tune with LoRA via NVIDIA NeMo Automodel. The example uses nvidia/Nemotron-Personas-France, samples 2,000 personas, serves Nemotron 3.5 Lightning locally with vLLM, and merges the LoRA adapter before evaluating against the base model.

_Developers adapting LLMs to local markets can track emerging fine-tuning workflows like this one on daily.dev._

### Why does the French passport stamp fee differ between mainland France and French Guiana?

The passport tax stamp costs €43 for a resident of French Guiana compared to €86 in mainland France, a regional difference that a generic foundation model may not know without locale-specific grounding data such as persona-based training sets tied to official procedure text.

_Teams building region-aware AI agents can follow localization fine-tuning approaches like this on daily.dev._

### What setting do I need for Nemotron 3.5 Lightning to stop it from generating reasoning traces during synthetic data generation?

Set enable_thinking: false under providers → extra_body → chat_template_kwargs in the sdg_data_designer.yaml config. Nemotron 3.5 Lightning produces reasoning traces by default; placing this setting under inference_parameters instead causes the run to fail, and omitting it leaves reasoning text in the training data.

_Developers debugging LLM config quirks can keep up with practical fixes like this on daily.dev._

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---

Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#lora](https://daily.dev/tags/lora)

[View this post on daily.dev](https://daily.dev/posts/fine-tuning-nvidia-nemotron-to-know-local-rules-not-just-local-languages-01kseszkv)

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