Models & Fine-tuning

Manage your AI models and fine-tune them for specific research needs within OpinioAI.

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.