Gconv Prune

Custom Models

Build and integrate custom AI models with Gconv Prune

Overview

Learn how to develop, train, and integrate custom AI models into your Gconv Prune implementation. This guide covers model architecture, training processes, and deployment strategies.

Model Architecture

Key components of a custom model:

  • Model Definition: Structure and layers
  • Input Processing: Data preprocessing and tokenization
  • Output Handling: Response generation and formatting
  • Integration Points: API endpoints and callbacks

Implementation Example

Example of implementing a custom model:

from gconv.models import CustomModel

class MyCustomModel(CustomModel):
    def __init__(self):
        super().__init__()
        self.model = YourModelArchitecture()

    def train(self, data):
        # Training implementation
        pass

    def predict(self, input):
        # Prediction logic
        return self.model.generate(input)

Training Process

Steps for training your custom model:

  1. Prepare and preprocess your training data
  2. Configure training parameters
  3. Implement training loop with validation
  4. Monitor training metrics
  5. Save and version your models

Deployment

Best practices for deploying custom models:

  • Version control your model artifacts
  • Implement A/B testing capabilities
  • Set up monitoring and logging
  • Configure auto-scaling based on load

Need Help?

For custom model development help, check our troubleshooting guide or contact our support team.