AdvancedИнженерия

OpenAI Fine-tuning: From Data to DPO

Official OpenAI fine-tuning examples: data preparation, chat model fine-tuning, Direct Preference Optimization, model distillation, and Reinforcement Fine-Tuning. Real code from the OpenAI team.

3modules

5lessons

175 mintotal time

ML engineers and developers customizing OpenAI modelsaudience

Module 1

Data Preparation and Basic Fine-Tuning

Loading, validating, and counting tokens for a chat model fine-tuning dataset; running a complete fine-tuning cycle on an ingredient extraction example.

Data Preparation for Chat Model Fine-Tuning

Complete pipeline for checking and analyzing a JSONL dataset before fine-tuning: format errors, token statistics, and training cost estimation.

35 min

Fine-Tuning Chat Models: End-to-End

End-to-end walkthrough: preparing JSONL, uploading files via the Files API, launching a fine-tuning job, monitoring progress, and running inference on the fine-tuned model.

35 min

Module 2

DPO and Preferences

Direct Preference Optimization technique: when and why SFT is not enough, the preferred/rejected dataset format, and a full DPO job cycle via the API.

Direct Preference Optimization: Learning from Preferences

DPO aligns a model to subjective criteria (tone, style, brand voice) using preferred/rejected response pairs — no reward model or RL required.

35 min

Module 3

Distillation and Reinforcement Fine-Tuning

Distilling a large model's knowledge into a smaller one via the Store API and Structured Outputs; Reinforcement Fine-Tuning with verifiable graders on medical data.

Model Distillation: gpt-4o → gpt-4o-mini

Distillation uses store=True in Chat Completions to capture large-model outputs, then fine-tunes a smaller model on that data — reducing cost and latency without sacrificing accuracy.

35 min

Reinforcement Fine-Tuning: Training with a Verifiable Reward

RFT uses an RL loop with a grader to optimize a model's reasoning on tasks with measurable quality: medicine, law, finance, and code.

35 min