Fine-tuning Models

Adapt OpinioAI's pre-trained models to better suit your specific research requirements and improve their performance on specialized tasks or datasets.

Overview

Fine-tuning allows you to tailor AI models using your own data. Choose the approach based on your needs:

• Quick Adaptation: For simple customization with smaller datasets (uses Gemini 2 Flash).
• Deep Learning: For complex tasks requiring extensive datasets and parameter tuning (uses Claude 3.7 Sonnet).
• Technical Specialization: For adapting models to specific technical domains or code-related tasks (uses Mistral Large).

Process

1. Prepare Data: Create a dataset of input/output examples relevant to your task (text, JSON, CSV formats). Ensure data quality and balance.
2. Configure Training: Select the base model (Gemini, Claude, Mistral) and set training parameters like learning rate and epochs.
3. Run Fine-tuning: Initiate the training process. OpinioAI handles the underlying infrastructure.
4. Evaluate & Deploy: Monitor training metrics, evaluate the fine-tuned model's performance on a test set, and deploy it for use in features like Ask Away or Analyze.

Use Cases

• Domain Adaptation: Improve model understanding of industry-specific jargon or technical terms.
• Task Specialization: Train models to generate specific output formats or follow custom instructions.
• Performance Optimization: Enhance accuracy, speed, or efficiency for your particular use case.