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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:

How to Fine-tune

You can fine-tune a generic model to adjust it to domain-specific tasks.

Models

There are different models for fine-tuning and inference.

Datasets

You can create datasets for training a model and validating the results of the training.

Deploy Custom LoRA

Deploy serverless LoRA adapter models in Nebius Token Factory with per-token billing.

Fine-tuning LLMs with Nebius Token Factory

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.