> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-ai-studio/services/model-fine-tuning/tutorials/how-to-create-a-pipeline/select-trainer.md). # Select Trainer Select the appropriate trainer - which guides the model you select for training

We offer three trainers to optimize your models:

Trainer	Definition	How it works	Best for	Cover image
SFT (Supervised fine-tuning)	Foundational technique that trains your model on input-output pairs, teaching it to produce desired responses for specific inputs.	- Provide examples of correct responses to prompts to guide the model’s behavior. - Often uses human-generated “ground truth” responses to show the model how it should respond.	- Classification - Nuanced translation - Generating content in a specific format - Correcting instruction-following failures	machine-learning.png
DPO (Direct preference optimization)	Trains models to prefer certain types of responses over others by learning from comparative feedback, without requiring a separate reward model.	- Provide both correct and incorrect example responses for a prompt. - Indicate the correct response to help the model perform better.	- Summarizing text, focusing on the right things - Generating chat messages with the right tone and style	best-practice.png
Pre-training	Initial training phase using large unlabeled data for language understanding.	- Exposes the model to vast amounts of text data to learn grammar, facts, reasoning patterns, and world knowledge. - No labeled examples required.	- Building foundational language understanding - Preparing models for downstream fine-tuning tasks	learning.png

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