Select Trainer
Select Trainer
Updated on 23 Sep 2025

Select the appropriate trainer - which guides the model you select for training.

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We offer three trainers to optimize your models:

Trainer Definition How it works Best for
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
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
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