Fine-tuning
Adapt pre-trained models to specific domains through automated dataset creation and training.
Fine-tuning adapts pre-trained models to specific domains or tasks through specialized training. SeekrFlow automates dataset creation, manages training workflows, and deploys fine-tuned models as custom endpoints.
How fine-tuning works
The fine-tuning process adjusts model parameters by training on structured question-and-answer pairs. Models learn from examples that demonstrate desired behaviors, domain knowledge, and specific output patterns. The training produces specialized models with deeper expertise while retaining general capabilities from the base model.
Fine-tuning methods
SeekrFlow supports multiple fine-tuning approaches:
| Method | Description | Best for |
|---|---|---|
| Instruction fine-tuning | Standard approach that trains models on question-and-answer pairs aligned to task-specific instructions. Embeds domain knowledge directly into model parameters. | Embedding proprietary knowledge, customizing behavior and tone, optimizing for demonstrated tasks |
| Context-grounded fine-tuning | Training approach that teaches models to access and retrieve information from external knowledge bases during inference. Maintains accuracy with frequently changing information. | Dynamic information that requires real-time updates, maintaining current data without retraining |
| Group relative policy optimization (GRPO) | Teaches the model to judge its own outputs using a reward function that scores generated responses against reference answers, rather than directly imitating target responses. | Improving response quality, aligning outputs with brand voice, reducing unwanted behaviors |
Learn more: Choosing a fine-tuning method
Low-rank adaptation (LoRA)
Low-rank adaptation (LoRA) is a parameter-efficient optimization technique that can be applied to any of the fine-tuning methods above. Rather than updating all model weights during training, LoRA trains small adapter modules, enabling faster training with lower compute costs while preserving base model knowledge. LoRA can be used with instruction fine-tuning, context-grounded fine-tuning, or GRPO to reduce resource requirements and speed up iteration cycles.
Dataset preparation
Fine-tuning requires structured training datasets with question-and-answer pairs. SeekrFlow's data engine automates the creation of training-ready datasets from raw source files, generating examples that demonstrate desired model behaviors and domain knowledge.
Learn more: AI-ready data
When to use fine-tuning
Fine-tuning provides value when:
- Working with proprietary or sensitive information not in base model training data.
- Requiring specific output formats, styles, or tones.
- Optimizing for tasks more easily demonstrated than described.
- Improving accuracy on domain-specific terminology or concepts.
- Reducing costs by using smaller specialized models.
Model deployment
Fine-tuned models are deployed as custom model endpoints. Once active, fine-tuned models can be used in agents, inference workflows, or any application requiring specialized model behavior.
Updated 8 days ago