Select Trainer
Select the appropriate trainer - which guides the model you select for training
We offer three trainers to optimize your models:
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
Last updated