# Fine tune with Pytorch

> Use Pytorch to fine-tune models locally

## Table of Contents

- [Overview](#overview)
- [Instructions](#instructions)
- [Troubleshooting](#troubleshooting)

---

## Overview

## Basic idea

This playbook guides you through setting up and using Pytorch for fine-tuning large language models on NVIDIA Spark devices.

## What you'll accomplish

You'll establish a complete fine-tuning environment for large language models (1-70B parameters) on your NVIDIA Spark device. 
By the end, you'll have a working installation that supports parameter-efficient fine-tuning (PEFT) and supervised fine-tuning (SFT).

## What to know before starting

- Previous experience with fine-tuning in Pytorch
- Working with Docker



## Prerequisites
Recipes are specifically for DIGITS SPARK. Please make sure that OS and drivers are latest.


## Ancillary files

ALl files required for fine-tuning are included in the folder in [the GitHub repository here](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}).

## Time & risk

* **Time estimate:** 30-45 mins for setup and runing fine-tuning. Fine-tuning run time varies depending on model size 
* **Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting.

## Instructions

## Step 1. Configure Docker permissions

To easily manage containers without sudo, you must be in the `docker` group. If you choose to skip this step, you will need to run Docker commands with sudo.

Open a new terminal and test Docker access. In the terminal, run:

```bash
docker ps
```

If you see a permission denied error (something like `permission denied while trying to connect to the Docker daemon socket`), add your user to the docker group:

```bash
sudo usermod -aG docker $USER
```

> **Warning**: After running usermod, you must log out and log back in to start a new
> session with updated group permissions.

## Step 2.  Pull the latest Pytorch container

```bash
docker pull nvcr.io/nvidia/pytorch:25.09-py3
```

## Step 3. Launch Docker

```bash
docker run --gpus all -it --rm --ipc=host \
-v $HOME/.cache/huggingface:/root/.cache/huggingface \
-v ${PWD}:/workspace -w /workspace \
nvcr.io/nvidia/pytorch:25.09-py3
```

## Step 4. Install dependencies inside the container

```bash
pip install transformers peft datasets "trl==0.19.1" "bitsandbytes==0.48"
```

## Step 5: Authenticate with Huggingface

```bash
huggingface-cli login
##<input your huggingface token.
##<Enter n for git credential>
```

## Step 6:  Clone the git repo with fine-tuning recipes

```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets
cd ${MODEL}/assets
```

## Step7: Run the fine-tuning recipes

To run LoRA on Llama3-8B use the following command:
```bash
python Llama3_8B_LoRA_finetuning.py
```

To run qLoRA fine-tuning on llama3-70B use the following command:
```bash
python Llama3_70B_qLoRA_finetuning.py
```

To run full fine-tuning on llama3-3B use the following command:
```bash
python Llama3_3B_full_finetuning.py
```

## Troubleshooting

> **Note:** DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. 
> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within 
> the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:
```bash
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
```
