> ## Documentation Index
> Fetch the complete documentation index at: https://facetai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-Tuning Workflow

> Learn how to fine-tune Gemma models for your specific tasks using Facet's training service

Fine‑tune a Gemma model on your domain data with a simple, repeatable flow. You’ll pick a base model, choose a training method (SFT, DPO/ORPO, or GRPO), and run efficiently with Full, LoRA, or QLoRA depending on resources. Vision variants support text+image.

<Steps>
  <Step title="Prepare your dataset">
    Process data into conversation or preference format using the [dataset preprocessing guide](/dataset/introduction.mdx).

    <Check>
      Verify a small sample looks correct before training.
    </Check>
  </Step>

  <Step title="Select base model and method">
    Pick a Gemma size that fits your budget, then choose SFT (supervised), DPO/ORPO (preference), or GRPO (reasoning with rewards).
  </Step>

  <Step title="Enable PEFT and quantization if needed">
    Start with QLoRA for strong results on modest hardware; use Full finetune only when you need maximal capacity.
  </Step>

  <Step title="Launch and monitor">
    Start the job and watch training/validation signals to catch issues early.
  </Step>

  <Step title="Evaluate and iterate">
    Test the model, compare to the baseline, and iterate on data or settings.
  </Step>
</Steps>

Next: open the [Training guide](/fine-tuning/training) for step‑by‑step configuration details.