Fine-Tuning GPT Models: OpenAI's Managed Fine-Tuning Walkthrough

Description

In this video, we walk through OpenAI's managed fine-tuning service for GPT models. You'll see how to prepare data, launch a fine-tuning job, and evaluate the results—all without managing any infrastructure. You'll learn how to: - Evaluate a base GPT model on a validation set - Convert your dataset to OpenAI's JSONL format - Upload training and validation data to OpenAI - Launch and monitor fine-tuning jobs with custom hyperparameters - Interpret training metrics and loss curves - Evaluate fine-tuned models and understand when fine-tuning helps (or doesn't) Timestamps: 0:00 - Introduction to managed fine-tuning with OpenAI 0:31 - Setting up and evaluating base GPT-4o-mini 1:43 - Understanding baseline performance on validation set 2:30 - Converting GSM8K data to JSONL format 3:38 - Examining formatted training files 4:01 - Uploading data and starting fine-tuning jobs 5:13 - Experimenting with learning rate multipliers 6:01 - Monitoring training in OpenAI console 7:00 - Reviewing completed fine-tuning jobs 8:14 - Evaluating fine-tuned models and analyzing results 9:24 - Key takeaways on when fine-tuning helps This lesson is part of Week 3 of the LLM Engineering and Deployment Certification, where you'll learn both managed and self-hosted fine-tuning approaches. This managed approach offers convenience but comes with trade-offs in cost, control, and transparency. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. ✅ Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI/ML professionals build and evaluate intelligent, goal-driven systems and showcase them through certifications, competitions, and real-world project publications. 🌐 Learn more: https://www.readytensor.ai/ 👍 Like the video? Subscribe and let us know what topics you want us to cover next!