Description
Post-train NVIDIA Nemotron 3.5 Lightning for your users’ language and local context. This five-minute tutorial walks through a French support example using Nemotron-Personas datasets, synthetic conversations, and LoRA fine-tuning with NeMo AutoModel.
Learn how to define target behavior, generate and check training data, fine-tune your model, and compare its responses with the base model—then adapt the workflow to other languages.
Nemotron code and recipes: https://github.com/NVIDIA-NeMo/Nemotron
French persona dataset: https://huggingface.co/datasets/nvidia/Nemotron-Personas-France
NeMo AutoModel: https://github.com/NVIDIA-NeMo/Automodel
Tech blog and exact model download: https://developer.nvidia.com/blog/fine-tuning-nvidia-nemotron-to-know-local-rules-not-just-local-languages
Chapters (from supplied transcript; confirm against final video):
00:00 Post-Training for Language and Local Context
00:37 French Support: Base vs. Post-Trained Model
01:13 Personas, Synthetic Data, and LoRA
01:44 Define Target Behavior
01:57 Generate Synthetic Conversations
02:25 Audit and Split Training Data
02:39 Fine-Tune With LoRA
03:05 Serve and Evaluate Your Model
03:48 Further Training and Other Languages
04:17 Get Started