Main Concept
Supervised fine-tuning is the process of taking a pre-trained foundation model and further training it on a smaller, specialized dataset with labeled examples to adapt it to a specific task or domain.
Context
It allows you to use an existing well pre-trained model and train it even better on a specialized field or with an special behavior, this is very useful for business and industries.
Key Aspects
- You obtain a model better trained for a particular field or area of knowledge.
- The labeled examples are input-output pairs, for instance:
- Supervised Fine-Tunning is usually cheaper because requires less computational power and less data is usually required.
- However it requires experienced ML engineers to run the task
- You must prepare the data, do fine-tuning and then evaluate the model.
Applications
- Customer service
- Health
- Legal services
- Banking
- etc.
Examples
Example 1: Guatemalan Banking Chatbot
Without Fine-tuning:
User: "How much does a SINPE transfer cost?"
Generic Titan: "I don't have specific information about SINPE transfers. Could you clarify what SINPE is?"
❌ Doesn't know regional payment systems With Fine-tuning:
Training data includes 1,000+ examples of Guatemala banking queries
User: "How much does a SINPE transfer cost?"
Fine-tuned Titan: "SINPE transfers in Guatemala cost Q5.00 for amounts up to Q5,000 and Q15.00 for larger amounts. Processing time is 1-2 hours on business days.
✅ Knows regional systems, pricing, local context