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View All Playbooks
View All Playbooks

onboarding

  • Set Up Local Network Access
  • Open WebUI with Ollama

data-science

  • Optimized JAX
  • Text to Knowledge Graph

tools

  • Comfy UI
  • DGX Dashboard
  • VS Code
  • RAG application in AI Workbench
  • Set up Tailscale on your Spark

fine-tuning

  • FLUX.1 Dreambooth LoRA Fine-tuning
  • LLaMA Factory
  • Fine-tune with NeMo
  • Fine tune with Pytorch
  • Unsloth on DGX Spark
  • Vision-Language Model Fine-tuning

use-case

  • Build and Deploy a Multi-Agent Chatbot
  • NCCL for Two Sparks
  • Connect Two Sparks
  • Video Search and Summarization

inference

  • Multi-modal Inference
  • NIM on Spark
  • NVFP4 Quantization
  • Speculative Decoding
  • TRT LLM for Inference
  • Install and Use vLLM for Inference

Fine tune with Pytorch

1 HR

Use Pytorch to fine-tune models locally

View on GitHub

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.

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.

Resources

  • DGX Spark Documentation
  • DGX Spark Forum