> ## 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.

# Facet AI

> Democratizing LLM fine-tuning for everyone

## What is Facet AI?

Facet AI is a **no-code** platform that makes fine-tuning Gemma models accessible to everyone. Whether you're a developer or business user, you can create custom language models tailored to your specific needs with our streamlined platform and user-friendly interface.

## Key Features

Fine-tuned small language models (SLMs) are often more **cost-effective and performant** for specific use cases compared to generic LLMs. Learn more with [this article from Unsloth](https://docs.unsloth.ai/get-started/beginner-start-here/faq-+-is-fine-tuning-right-for-me). Facet AI simplifies this process with:

<CardGroup cols={2}>
  <Card title="Easy Dataset Preparation" icon="database">
    Upload your data or connect to Hugging Face Hub. We handle formatting for language modeling, preference tuning, and multimodal tasks.
  </Card>

  <Card title="Multiple Model Sizes" icon="computer">
    Fine-tune Gemma 3 models from 270M to 27B parameters, including Gemma 3n variants, using SFT, DPO, and GRPO.
  </Card>

  <Card title="Built-in Evaluation" icon="chart-line">
    Test your models with comprehensive inference and evaluation suites to measure performance before deployment.
  </Card>

  <Card title="Flexible Export" icon="download">
    Export models in multiple formats (adapter, merged, GGUF) with various quantizations for deployment anywhere.
  </Card>
</CardGroup>

## Quick Demo!

<iframe className="w-full aspect-video rounded-xl" src="https://www.youtube.com/embed/JGnzoLSZReI" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowFullScreen />

<Info>
  Interested in more detailed walkthroughs? Check out our [video tutorials](/tutorials) covering common use cases and advanced techniques!
</Info>

## Getting Started

Choose your path based on your needs:

<CardGroup cols={3}>
  <Card title="Quick Start" icon="rocket" href="/quickstart">
    Get up and running in minutes using our managed platform. Perfect for trying out Facet AI.
  </Card>

  <Card title="Self-Deploy" icon="server" href="/deployment">
    Deploy Facet AI on your own cloud infrastructure for full control and customization.
  </Card>

  <Card title="Developer Guide" icon="code" href="/developer">
    Contribute to the open-source project or customize for your organization.
  </Card>
</CardGroup>

<Note>
  For production and enterprise use, deploy Facet AI to your own cloud for full control, security, and autoscaling. Our platform provides 100% Infrastructure-as-Code (IaC) for easy, automated setup. We have limited compute capacity on the managed platform to ensure fair usage for all users.
</Note>

## What We Support

<AccordionGroup>
  <Accordion title="Dataset Management">
    * Custom data uploads and Hugging Face Hub integration
    * Automatic dataset augmentation and preparation
    * Support for language modeling, preference tuning, and multimodal tasks
  </Accordion>

  <Accordion title="Model Training">
    * Gemma 3 family: 270M, 1B, 4B, 12B, 27B parameters
    * Gemma 3n variants: E2B and E4B
    * Training methods: SFT, DPO, and GRPO
  </Accordion>

  <Accordion title="Evaluation & Deployment">
    * Built-in inference and evaluation tools
    * Multiple export formats and quantizations
    * Easy deployment to Google Cloud Run and other providers
  </Accordion>
</AccordionGroup>

## Limitations

<Warning>
  **What Facet AI doesn't do:**

  * Web scraping for data collection
  * Hosting deployed models (you deploy them yourself)
  * Custom model architectures beyond Gemma variants
</Warning>

## Feature Requests

If you want us to expand on any documentation or any specific video tutorials, please open an issue on our GitHub repository! This project is maintained by two undergraduate students who need to balance academic and internship too, so please be patient with response times.

import { Callout } from "mint-ui";

<Callout title="Need help?" type="info">
  Check out our [quickstart guide](/quickstart) to get started in minutes, or explore our [comprehensive tutorials](/tutorials) for detailed workflows.
</Callout>