Fine-Tuning Tutorials | NVIDIA NeMo Platform

Use the tutorials in this section to gain a deeper understanding of how the NVIDIA NeMo Customizer microservice enables fine-tuning tasks.

Tutorials are organized by complexity and typically build on one another. The tutorials reference NMP_BASE_URL, which is the base URL of your NeMo Platform deployment. Refer to the Setup guide for installation, setup, and platform URL guidance.

Getting Started

Understanding Model Entities and Adapters

Learn the fundamentals of how NeMo Customizer works with Model Entities and Adapters, and how to choose the right approach for your project.

model-entities adapters training-types

Dataset Preparation

Format Training Datasets

Learn how to format datasets for different model types.

datasets chat-models completion-models

Customization Jobs

Fine-Tune a Model with Custom Data Using LoRA

Learn how to perform supervised fine-tuning with LoRA adapters using custom data.

nemo-customizer

Fine-Tune a Model with Custom Data Processing All Weights

Learn how to perform supervised fine-tuning using custom data by modifying the all training parameters.

nemo-customizer

Distill a Large Model into a Smaller One with Knowledge Distillation

Learn how to compress a larger teacher model into a smaller student model using knowledge distillation.

nemo-customizer knowledge-distillation

Fine-Tune an Embedding Model With Positive and Negative Samples Using LoRA

Learn how to fine-tune embedding models using LoRA merged training for improved question-answering and retrieval tasks.

embedding-models lora-merged nemo-customizer

Monitoring & Optimization

Check Customization Job Metrics

Learn how to check job metrics using MLflow or Weights & Biases.

nemo-customizer mlflow wandb

Optimize Tokens per GPU

Learn how to optimize the token-per-GPU throughput for a LoRA optimization job.

nemo-customizer wandb sequence-packing