The Fine-tuning section allows you to customize and enhance AI models to better align with your specific use cases, terminology, and requirements, improving performance and reducing costs.
Xenovia Fine-tuning Interface

​
Fine-tuning Overview

The fine-tuning process consists of several key components:

Model Selection

Choose which models to customize

Dataset Management

Create and manage training data

Training Jobs

Configure and monitor fine-tuning processes

Model Evaluation

Assess the performance of fine-tuned models

​
Model Selection

The Model Selection section helps you choose which models to customize:
  • Browse base models that support fine-tuning
  • Compare model capabilities and specifications
  • Review fine-tuning requirements
  • Check pricing and resource needs

​
Dataset Management

The Dataset Management section allows you to create and manage training data:
1

Dataset Creation

Create new datasets from various sources
2

Data Preparation

Clean, format, and structure your data
3

Data Validation

Verify dataset quality and format
4

Dataset Versioning

Track changes and iterations
Dataset features include:
  • Upload existing datasets in various formats
  • Create datasets from conversation history
  • Generate synthetic training data
  • Import from external sources
  • Collaborative dataset editing
  • Quality scoring and improvement suggestions

​
Training Jobs

The Training Jobs section allows you to configure and monitor fine-tuning processes:

Job Configuration

Set training parameters and options

Job Monitoring

Track progress and performance

Resource Management

Allocate and optimize computing resources

Job History

Review past training jobs
Configuration options include:
  • Learning rate and epochs
  • Batch size and steps
  • Early stopping criteria
  • Validation split
  • Hyperparameter optimization
  • Training notifications

​
Model Evaluation

The Model Evaluation section helps you assess the performance of fine-tuned models:
  • Accuracy and precision
  • Recall and F1 score
  • Response quality assessment
  • Task-specific metrics

​
Fine-tuning Approaches

Xenovia supports different fine-tuning approaches to address various needs:

Task-specific Tuning

Optimize for particular use cases

Domain Adaptation

Specialize for industry terminology

Style Alignment

Adjust tone and communication style

Knowledge Integration

Incorporate specific knowledge

Behavior Modification

Change response patterns

Efficiency Optimization

Improve performance and reduce costs

​
Fine-tuning Workflow

The typical fine-tuning workflow in Xenovia follows these steps:
1

Define Objectives

Clearly articulate what you want to improve
2

Collect Data

Gather high-quality examples for training
3

Prepare Dataset

Format and validate your training data
4

Configure Training

Set up the fine-tuning job parameters
5

Run Training

Execute the fine-tuning process
6

Evaluate Results

Assess the performance of the fine-tuned model
7

Deploy Model

Integrate the improved model into your agents
8

Monitor Performance

Track ongoing performance and results

Testing

Evaluate model performance

Analytics

Track performance improvements

Deployment

Release fine-tuned models