> ## 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.

# Preprocessing Workflow

> Prepare and format your datasets for fine-tuning Gemma models with Facet's preprocessing service

Turn raw text or multimodal data into training‑ready conversations in a few steps. You can upload files (CSV, JSON/JSONL, Parquet, Excel) or import from Hugging Face, and optionally include images for vision‑language tasks. Choose a processing mode that matches your objective (language modeling, prompt‑only, or preference tuning) and produce a clean, consistent dataset for training.

<Steps>
  <Step title="Pick your data source">
    Upload a file or import a Hugging Face dataset by repository name.

    <Note>
      Supports text and image data for multimodal training.
    </Note>
  </Step>

  <Step title="Map fields to roles">
    Point columns/keys to conversation roles (system, user, assistant) or to chosen/rejected pairs for preference data.
  </Step>

  <Step title="Choose processing mode">
    Select Language Modeling, Prompt‑only, or Preference Tuning to shape the output format.
  </Step>

  <Step title="Preview and validate">
    Inspect a sample of the processed dataset and fix any mapping issues.

    <Check>
      Confirm examples look correct before running the full job.
    </Check>
  </Step>

  <Step title="Process and version">
    Run the job to generate a training‑ready dataset you can reuse across experiments.
  </Step>
</Steps>

Next: open the [Datasets page](/dataset/datasets.mdx) to create your first processed dataset.