> ## 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.

# Fine-tuning

[**Data Lab**](https://tokenfactory.nebius.com/datalab) offers tools for preparing and managing datasets for fine-tuning workflows. For more information on fine-tuning, see the [Post-training documentation](https://docs.tokenfactory.nebius.com/post-training/overview).

### Dataset preparation

You can create fine-tuning datasets by:

* Importing and filtering inference logs (chat completions)
* Uploading and filtering structured datasets

Prepared datasets are stored in Data Lab and can be reused across fine-tuning jobs.

### Reproducibility

By keeping datasets versioned and centralized, Data Lab enables:

* Consistent training inputs across experiments
* Easier comparison of fine-tuning results
* Safer iteration without accidental data changes

### Data location and compliance

* Fine-tuning jobs run in **EU or US data centers**, for more information, see our [Legal Quick Guide](https://docs.tokenfactory.nebius.com/legal/legal-quick-guide#4-data-location:-where-your-data-is-processed) (Data Location part)
* All datasets are stored centrally in the eu-north1 (Finland) data center.

This design balances compliance requirements with operational simplicity.

### Typical use cases

* **Adapting models** to internal domain-specific data
* **Improving response quality** for specific tasks
* Training student models for **distillation pipelines**