Ajuste fino
El Ajuste Fino (Fine Tuning) le permite adaptar los modelos de NovaAI a su propio dominio, estilo de escritura o requisitos comerciales mediante el entrenamiento con conjuntos de datos personalizados. Esto ayuda a mejorar la coherencia de las respuestas, la precisión y el rendimiento en tareas específicas.
Overview
While base models perform well across a wide range of tasks, fine-tuning enables them to learn patterns specific to your application. Common use cases include customer support, legal assistants, medical documentation, and brand-specific content generation.
Fine-Tuning Workflow
Project Structure
Keeping training and validation datasets organized makes experiments easier to reproduce and maintain.
Training Dataset Guidelines
Requirement | Recommendation |
|---|---|
Format | JSONL |
Example Quality | High-quality, accurate responses |
Dataset Size | Hundreds to thousands of examples |
Consistency | Use the same writing style throughout |
Validation Set | Keep separate from training data |
Example Training Record
Each training example should represent the ideal response you expect from the model.
Before You Train
Clean and validate your dataset.
Remove duplicate or conflicting examples.
Use consistent formatting across all records.
Test prompts with the base model first.
Keep training and validation data separate.
Training Job Status
Status | Description |
Queued | Waiting to begin training. |
Running | Model is currently training. |
Completed | Training finished successfully. |
Failed | Training stopped due to an error. |
Cancelled | Training was manually cancelled. |
Best Practices
Start with a small dataset before scaling.
Continuously evaluate model performance.
Retrain only when your data changes significantly.
Version your datasets and trained models.
Monitor production results after deployment.
Next Step
Once your model has been trained and evaluated, continue to Batch Requests to learn how to efficiently process large volumes of requests in a single operation.