> ## 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.

# Video Tutorials

> End-to-end walkthrough for real-world use cases

New to Facet AI? This is the best place to start! Our videos walk you through common real-world examples sourced from cookbooks, tutorials, and industry use cases.

<Note>
  We might not train the models with full dataset or very long epochs in these
  videos since the purpose is to demonstrate platform usage. If you wish to
  share a use case, feel free to post an issue [on our
  GitHub!](https://github.com/gemma-facet/docs).
</Note>

## Language Modeling with SFT

Videos in this section cover supervised fine-tuning (SFT), the most common method for adapting large language models to specific tasks using labeled datasets.

### Improving Gemma 270M by 50% at emotion classification

This example uses [emotions dataset from Hugging Face Hub](https://huggingface.co/datasets/boltuix/emotions-dataset) to fine-tune Gemma 270M for emotion classification.
In just 20 minutes of training, evaluation performance improved significantly and we are able to quickly try it out locally with Ollama.

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### Coming Soon

* Deploying models to cloud with vLLM
* LoRA on 4B models with vision datasets
* Fine tune 1B model on datasets with different language
* Using local datasets and dataset augmentation / synthesis
* Quickly benchmark Gemma models on common benchmark datasets from Hub

## Reinforcement Learning with GRPO

* Coming soon with reasoning examples...

## Preference Tuning with DPO / ORPO

* Coming soon...