> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tokenfactory.nebius.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

**Fine-tuning in Nebius Token Factory** lets you train models to perform better on your specific tasks and data. By teaching a model with your own examples, it can deliver more accurate results and reduce the chances of AI “hallucinations.”

Unlike regular prompting, fine-tuning doesn’t limit how many examples you can use. You can train the model on a much larger dataset, which means less manual prompt engineering later on. This not only improves accuracy but also helps lower costs and speeds up responses.

For more information about how to fine-tune a model, see the following:

<Columns cols={2}>
  <Card title="How to Fine-tune" icon="layer-group" href="/fine-tuning/how-to-fine-tune">
    You can fine-tune a generic model to adjust it to domain-specific tasks.
  </Card>

  <Card title="Models" icon="list" href="/fine-tuning/models">
    There are different models for fine-tuning and inference.
  </Card>

  <Card title="Datasets" icon="table" href="/fine-tuning/datasets">
    You can create datasets for training a model and validating the results of the training.
  </Card>

  <Card title="Deploy Custom LoRA" icon="layer-plus" href="/fine-tuning/deploy-custom-model">
    Deploy serverless LoRA adapter models in Nebius Token Factory with per-token billing.
  </Card>

  <Card title="Fine-tuning LLMs with Nebius Token Factory" icon="newspaper" href="https://nebius.com/blog/posts/fine-tuning-llms-with-nebius-ai-studio">
    In this blog post, we’ll demonstrate how to fine-tune an LLM using Nebius Token Factory with a function-calling task as our running example.
  </Card>
</Columns>