---
title: "Fine-Tuning Tutorials"
description: ""
---
Use the tutorials in this section to gain a deeper understanding of how the NVIDIA NeMo Customizer microservice enables fine-tuning tasks.

<Tip>

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](/documentation/get-started) for installation, setup, and platform URL guidance.

</Tip>

---

## Getting Started

<Cards>

<Card title="Understanding Model Entities and Adapters" href="/documentation/customizer-reference/tutorials/understanding-models-and-training">

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

</Card>

</Cards>

## Dataset Preparation

<Cards>

<Card title="Format Training Datasets" href="/documentation/customizer-reference/tutorials/format-training-dataset">

Learn how to format datasets for different model types.

datasets chat-models completion-models

</Card>

</Cards>

## Customization Jobs

<Cards>

<Card title="Fine-Tune a Model with Custom Data Using LoRA" href="/documentation/customizer-reference/tutorials/lora-customization-job">

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

nemo-customizer

</Card>
<Card title="Fine-Tune a Model with Custom Data Processing All Weights" href="/documentation/customizer-reference/tutorials/sft-customization-job">

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

nemo-customizer

</Card>
<Card title="Distill a Large Model into a Smaller One with Knowledge Distillation" href="/documentation/customizer-reference/tutorials/distillation-customization-job">

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

nemo-customizer knowledge-distillation

</Card>
<Card title="Fine-Tune an Embedding Model With Positive and Negative Samples Using LoRA" href="/documentation/customizer-reference/tutorials/embedding-customization-job">

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

embedding-models lora-merged nemo-customizer

</Card>

</Cards>

## Monitoring & Optimization

<Cards>

<Card title="Check Customization Job Metrics" href="/documentation/customizer-reference/tutorials/metrics">

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

nemo-customizer mlflow wandb

</Card>
<Card title="Optimize Tokens per GPU" href="/documentation/customizer-reference/tutorials/optimize-throughput">

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

nemo-customizer wandb sequence-packing

</Card>

</Cards>
