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nvidia/pytorch-fine-tune/README.md
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## Overview ## Overview
## Basic Idea ## Basic idea
This playbook guides you through setting up and using Pytorch for fine-tuning large language models on NVIDIA Spark devices. This playbook guides you through setting up and using Pytorch for fine-tuning large language models on NVIDIA Spark devices.
## What you'll accomplish ## 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) 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 ## What to know before starting
## Prerequisites ## Prerequisites
recipes are specifically for DIGITS SPARK. Please make sure that OS and drivers are latest. Recipes are specifically for DIGITS SPARK. Please make sure that OS and drivers are latest.
## Ancillary files ## Ancillary files
ALl files required for finetuning are included. ALl files required for fine-tuning are included.
## Time & risk ## Time & risk
**Time estimate:** 30-45 mins for setup and runing finetuning. Finetuning run time varies depending on model size **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. **Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting.
**Rollback:**
## Instructions ## Instructions
## Step 1. Configure Docker permissions ## Step 1. Configure Docker permissions
@ -78,13 +78,13 @@ nvcr.io/nvidia/pytorch:25.09-py3
nvcr.io/nvidia/pytorch:25.09-py3 nvcr.io/nvidia/pytorch:25.09-py3
``` ```
## Step 4. Install dependencies inside the contianer ## Step 4. Install dependencies inside the container
```bash ```bash
pip install transformers peft datasets "trl==0.19.1" "bitsandbytes==0.48" pip install transformers peft datasets "trl==0.19.1" "bitsandbytes==0.48"
``` ```
## Step 5: authenticate with huggingface ## Step 5: Authenticate with Huggingface
```bash ```bash
huggingface-cli login huggingface-cli login
@ -92,26 +92,26 @@ huggingface-cli login
##<Enter n for git credential> ##<Enter n for git credential>
``` ```
## Step6: Clone the git repo with finetuning recipes ## Step6: Clone the git repo with fine-tuning recipes
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL} git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}
cd ${MODEL}/assets cd ${MODEL}/assets
``` ```
##Step7: Run the finetuning recipes ## Step7: Run the fine-tuning recipes
To run LoRA on Llama3-8B use the following command: To run LoRA on Llama3-8B use the following command:
```bash ```bash
python Llama3_8B_LoRA_finetuning.py python Llama3_8B_LoRA_finetuning.py
``` ```
To run qLoRA finetuning on llama3-70B use the following command: To run qLoRA fine-uning on llama3-70B use the following command:
```bash ```bash
python Llama3_70B_qLoRA_finetuning.py python Llama3_70B_qLoRA_finetuning.py
``` ```
To run full finetuning on llama3-3B use the following command: To run full fine-tuning on llama3-3B use the following command:
```bash ```bash
python Llama3_3B_full_finetuning.py python Llama3_3B_full_finetuning.py
``` ```