Fine-Tuning

When I first started exploring AI, I was eager to use my own data with large language models (LLMs). However, I faced a dilemma: should I fine-tune a model with my data or use Retrieval-Augmented Generation (RAG)? After diving into research, I discovered the strengths and challenges of each approach. Here’s what I learned: Fine-Tuning Fine-tuning involves retraining a pre-trained model on a specific dataset to adapt it to a particular domain or task. For example: ...

Overview This exercise walks you through fine-tuning a GPT-4o model using Azure AI Foundry to create a custom chat application. The goal is to refine the model’s responses to ensure a consistent conversational tone tailored for a travel assistant. Steps & Configuration Details 1. Deploy a Base Model in Azure AI Foundry Open Azure AI Foundry portal (https://ai.azure.com) and sign in. Search for gpt-4o and select Use this model. Configuration Items: Azure AI Foundry Resource: A valid name. Subscription: Your Azure subscription. Resource Group: Select or create a resource group. Region: Choose from: East US 2 North Central US Sweden Central (regions supporting fine-tuning). Deployment Name: gpt-4o (default). 2. Fine-Tune the Model Download Training Data: https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel-finetune-hotel.jsonl Save the file as JSONL (ensure it’s not saved as .txt). Navigate to Fine-tuning under Build and customize. Select Add fine-tune model → Choose gpt-4o → Click Next. Fine-Tuning Configuration: ...