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    onboarding

    • Set Up Local Network Access
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    data science

    • Single-cell RNA Sequencing
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    tools

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    fine tuning

    • FLUX.1 Dreambooth LoRA Fine-tuning
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    • Fine-tune with Pytorch
    • Unsloth on DGX Spark

    use case

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    inference

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    Fine-tune with Pytorch

    1 HR

    Use Pytorch to fine-tune models locally

    • DGX
    • Spark
    View on GitHub
    OverviewOverviewInstructionsInstructionsRun on two SparksRun on two SparksTroubleshootingTroubleshooting

    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 DGX 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.
    • Last Updated: 01/15/2025
      • Add two-Spark distributed finetuning example
      • Add detailed instructions to run full SFT, LoRA and qLoRA workflows on Llama3 3B, 8B and 70B models.

    Resources

    • DGX Spark Documentation
    • DGX Spark Forum
    • DGX Spark User Performance Guide
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