> ## 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.

# Quickstart

> Get started with Facet AI in minutes - fine-tune your first Gemma model

## Welcome to Facet AI

This quickstart guide will walk you through creating your first fine-tuned model using Facet AI. You'll go from data upload to model deployment in just a few steps.

<Note>
  **Prerequisites:** You'll need a Facet AI account. If you don't have one,
  [sign up here](https://gemma-facet.vercel.app) first.
</Note>

## Step 1: Create Your Account and Access the Platform

<Steps>
  <Step title="Sign up for Facet AI">
    1. Visit [Facet AI](https://gemma-facet.vercel.app)
    2. Click "Get Started" and create your account
    3. Verify your email address if required

    <Check>
      You should see the Facet AI dashboard after successful signup.
    </Check>
  </Step>

  <Step title="Navigate to the Dashboard">
    Once logged in, you'll see the main dashboard with sections for:

    * **Datasets**: Manage your training data
    * **Training**: Create and monitor fine-tuning jobs
    * **Models**: View your trained models
    * **Exports**: Download models in various formats

    <Tip>
      Bookmark the dashboard URL for easy access to your projects.
    </Tip>
  </Step>
</Steps>

## Step 2: Prepare Your Dataset

<Steps>
  <Step title="Upload Your Data">
    You have two options for getting your training data:

    <Tab title="Connect Hugging Face">
      1. Go to **Datasets** → **Create Dataset**
      2. Choose "Import from Hugging Face"
      3. Enter the dataset name (e.g., `huggingface/datasets`)
      4. Select the specific split you want to use

      <Tip>
        Popular datasets for fine-tuning: `wikitext`, `openwebtext`, `alpaca`
      </Tip>
    </Tab>

    <Tabs>
      <Tab title="Upload Custom Data">
        1. Go to **Datasets** → **Create Dataset**
        2. Choose "Upload from file"
        3. Upload your data file (CSV, JSON, or TXT)
        4. Give your dataset a descriptive name

        <Warning>
          Supported formats: CSV, JSON, TXT. Maximum file size: 100MB per upload.
        </Warning>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Configure Dataset Processing">
    1. After upload, configure your dataset:
       * **Task Type**: Choose from Language Modeling, Preference Tuning, or Multimodal
       * **Format**: Convert your dataset into conversational format for training
       * **Augmentation**: Setup data augmentation if desired
    2. Click "Process Dataset"

    <Check>
      Processing typically takes 1-5 minutes depending on dataset size.
    </Check>
  </Step>
</Steps>

## Step 3: Start Your First Training Job

<Steps>
  <Step title="Create Training Configuration">
    1. Go to **Training** → **New Job**
    2. Select your processed dataset from the dropdown
    3. Choose your model:
       * **Gemma 3 270M**: Fastest, good for experimentation
       * **Gemma 3 1B**: Balanced performance and speed
       * **Gemma 3 4B**: Better quality, longer training time
       * **Gemma 3 12B**: High quality, requires more resources

    <Info>
      For your first model, we recommend starting with Gemma 3 270M/1B to get quick results.
    </Info>
  </Step>

  <Step title="Configure Training Parameters">
    Set your training parameters (or use defaults):

    * **Learning Rate**: Start with default (0.0001)
    * **Batch Size**: Use default (4) for most cases
    * **Epochs**: Generally 1-3 epochs suffice, for testing limit to 100-500 training steps
    * **Training Method**: Select between SFT, DPO, or GRPO based on your task

    <Tip>
      You can adjust these parameters later as you become more experienced with fine-tuning.
    </Tip>
  </Step>

  <Step title="Launch Training">
    1. Review your configuration
    2. Give your training job a descriptive name
    3. Click "Start Training"

    <Check>
      Training will begin immediately. You can monitor progress in the Training section.
    </Check>
  </Step>
</Steps>

## Step 4: Monitor and Evaluate Your Model

<Steps>
  <Step title="Track Training Progress">
    1. Go to **Training** to see your active jobs
    2. Click on your training job to view detailed progress
    3. Monitor metrics like loss, learning rate, and training time

    <Note>
      Training time varies: 270M models train in \~30 minutes, while 12B models can take several hours.
    </Note>
  </Step>

  <Step title="Test Your Model">
    Once training completes:

    1. Go to **Models** section
    2. Find your newly trained model
    3. Click "Test Model" to run inference
    4. Try different prompts to evaluate performance

    <Tip>
      Test with various prompts to ensure your model performs well across different scenarios.
    </Tip>
  </Step>
</Steps>

## Step 5: Export and Deploy Your Model

<Steps>
  <Step title="Export Your Model">
    1. Go to **Exports** → **Create Export**
    2. Select your trained model
    3. Choose export format:
       * **GGUF**: For local deployment with llama.cpp
       * **Adapter**: For Hugging Face transformers
       * **Merged**: Complete model ready for deployment
    4. Select quantization level (4-bit, 8-bit, or 16-bit)
    5. Click "Create Export"

    <Check>
      Export typically takes 5-15 minutes depending on model size and format.
    </Check>
  </Step>

  <Step title="Download Your Model">
    1. Once export completes, click "Download"
    2. Save the model file to your local machine
    3. Your model is now ready for deployment!

    <Warning>
      Keep your model files secure and don't share them publicly unless intended.
    </Warning>
  </Step>
</Steps>

## Next Steps

Congratulations! You've successfully fine-tuned your first Gemma model. Here's what to explore next:

<CardGroup cols={2}>
  <Card title="Advanced Training" icon="cogs" href="/fine-tuning/training">
    Learn about DPO, GRPO, and advanced training techniques for better model performance.
  </Card>

  <Card title="Model Deployment" icon="rocket" href="/deployment/deployment">
    Deploy your models to production using Google Cloud Run or other platforms.
  </Card>

  <Card title="Evaluation Techniques" icon="chart-line" href="/evaluation/inference_export">
    Learn comprehensive evaluation methods to assess your model's quality.
  </Card>

  <Card title="Dataset Best Practices" icon="database" href="/dataset/datasets">
    Master dataset preparation for optimal fine-tuning results.
  </Card>
</CardGroup>

## Troubleshooting

<AccordionGroup>
  <Accordion title="Training fails to start">
    * Check your dataset is properly processed
    * Ensure you have sufficient credits/quota
    * Verify your training parameters are valid
  </Accordion>

  <Accordion title="Poor model performance">
    * Try a larger model size (1B → 4B → 12B) - Increase training steps - Check
      your dataset quality and size - Consider data augmentation
  </Accordion>

  <Accordion title="Export issues">
    * Ensure training completed successfully
    * Try a different export format
    * Check your available storage quota
  </Accordion>
</AccordionGroup>

<Note>
  **Need more help?** Check out our [comprehensive tutorials](/tutorials) or
  contact support at [facet.gemma@gmail.com](mailto:facet.gemma@gmail.com).
</Note>