Fine-tuning
Customize and enhance AI models to better align with your specific use cases
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.
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
- Browse base models that support fine-tuning
- Compare model capabilities and specifications
- Review fine-tuning requirements
- Check pricing and resource needs
- Compare performance characteristics
- Review size and resource requirements
- Assess fine-tuning suitability
- Evaluate cost implications
- Recommendations based on use case
- Compatibility with existing systems
- Performance benchmarks
- Community feedback and ratings
Dataset Management
The Dataset Management section allows you to create and manage training data:
Dataset Creation
Create new datasets from various sources
Data Preparation
Clean, format, and structure your data
Data Validation
Verify dataset quality and format
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
- Accuracy and precision
- Recall and F1 score
- Response quality assessment
- Task-specific metrics
- Side-by-side comparison with base model
- A/B testing with real users
- Benchmark against previous versions
- Comparison with competitors
- Token usage efficiency
- Response time improvements
- Resource consumption
- ROI calculation
- Go/no-go recommendation
- Deployment strategy
- Rollback planning
- Monitoring setup
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:
Define Objectives
Clearly articulate what you want to improve
Collect Data
Gather high-quality examples for training
Prepare Dataset
Format and validate your training data
Configure Training
Set up the fine-tuning job parameters
Run Training
Execute the fine-tuning process
Evaluate Results
Assess the performance of the fine-tuned model
Deploy Model
Integrate the improved model into your agents
Monitor Performance
Track ongoing performance and results