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
- Prepare Data: Create a quality dataset (question-answer pairs, conversations, instructions, etc.).
- Select Base Model: Choose an appropriate pre-trained model (e.g., Gemini, Claude, Mistral).
- Configure & Train: Set parameters (learning rate, epochs) and start the fine-tuning job.
- 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.