Fine-tuning

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.

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.

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.

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	Deprecated (sunset July 17, 2026). 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
Reinforcement tuning (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
Preference tuning (DPO)	Learns directly from comparisons between preferred and dispreferred responses, without requiring reference answers or reward functions.	Aligning outputs with subjective quality criteria, organizational standards, human feedback

Method comparison

	Best for	Knowledge location	Update mechanism	Retrieval required	Training data	Training cost	Iteration speed	Artifact size
Instruction	Static domain knowledge	Model parameters	Retrain model	No	QA pairs	High (full)	Slow	Large (full model)
Context-grounded	Dynamic information	External knowledge base	Update knowledge base	Yes	QA pairs + knowledge base	High (full)	Slow	Large (full model)
Reinforcement tuning	Quality alignment (verifiable)	Model parameters	Retrain model	No	Prompts + reference answers	High (full)	Slow	Large (full model)
Preference tuning	Quality alignment (subjective)	Model parameters	Retrain model	No	Preferred/rejected response pairs	High (full)	Slow	Large (full model)

Recommended approach

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.

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

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.

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

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.

SeekrFlow Overview

SeekrFlow App

SeekrFlow MCP Server

Fine-tuning UI guide

Fine-tuning SDK guide

How fine-tuning works

When to use fine-tuning

Fine-tuning methods

Method comparison

Recommended approach

Low-rank adaptation (LoRA)

Vision language tuning

Dataset preparation

Model deployment

Fine-tuning UI guide

Fine-tuning SDK guide

​How fine-tuning works

​When to use fine-tuning

​Fine-tuning methods

​Method comparison

​Recommended approach

​Low-rank adaptation (LoRA)

​Vision language tuning

​Dataset preparation

​Model deployment

How fine-tuning works

When to use fine-tuning

Fine-tuning methods

Method comparison

Recommended approach

Low-rank adaptation (LoRA)

Vision language tuning

Dataset preparation

Model deployment