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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.

Fine-tuning UI guide

Create and manage fine-tuning jobs through the SeekrFlow web interface.

Fine-tuning SDK guide

Create and manage fine-tuning jobs programmatically with the Python SDK.

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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.

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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.

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Fine-tuning methods

SeekrFlow supports multiple fine-tuning approaches:

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Method comparison

Most fine-tuning projects should:
  • Start with LoRA + instruction fine-tuning as the default approach.
  • Use reinforcement tuning or preference tuning when quality alignment is needed.
  • Consider full fine-tuning only after LoRA experiments show it’s necessary.
These recommendations balance training efficiency with model quality while minimizing upfront investment.

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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, reinforcement tuning, or preference tuning to reduce resource requirements and speed up iteration cycles.

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Vision language tuning

Vision language tuning extends fine-tuning to models that process both images and text. The training workflow is the same as text-only fine-tuning — the difference is in the input data and model selection. Currently, SeekrFlow supports instruction fine-tuning of vision-language models with the same SDK primitives as text-only training.

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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

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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.
Last modified on October 27, 2026