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
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 |