Managing & Fine-tuning Models

Customize AI models for your specific research needs by fine-tuning them with your own data, improving accuracy and relevance for your domain.

Overview

Fine-tuning adapts pre-trained models. This section covers both managing your models and the fine-tuning process itself.

• Customize: Adapt models for specific domains or tasks.
• Improve Accuracy: Enhance performance on specialized terminology or datasets.
• Manage: Track model versions, monitor performance, and manage deployment.

Fine-tuning Process

1. Prepare Data: Create a quality dataset (question-answer pairs, conversations, instructions, etc.).
2. Select Base Model: Choose an appropriate pre-trained model (e.g., Gemini, Claude, Mistral).
3. Configure & Train: Set parameters (learning rate, epochs) and start the fine-tuning job.
4. Evaluate & Deploy: Monitor training, validate performance, and deploy the model for use.

Model Management

• Version Control: Keep track of different fine-tuned model versions.
• Performance Monitoring: Monitor accuracy, response time, and cost.
• Deployment: Manage where and how your fine-tuned models are used.