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 |