Blog
9 blog available in the ChatGPT directory
Preparing and Analyzing Chat Datasets for Effective Model Fine-Tuning
This notebook is your essential toolkit for preprocessing and analyzing chat datasets before fine-tuning chat models. It detects format errors, delivers key statistics, and estimates token counts to predict costs accurately. Designed for the latest gpt-3.5-turbo fine-tuning method—see legacy docs for babbage-002 and davinci-002.
Fine-Tuning OpenAI Models for RAG with Qdrant: A Hands-On Guide
Explore a complete walkthrough on fine-tuning OpenAI models for Retrieval Augmented Generation (RAG), enhanced by Qdrant and few-shot learning to minimize hallucinations. Using gpt-3.5-turbo on SQuAD, this practical tutorial is ideal for ML practitioners and AI engineers mastering tailored workflows.
Fine-tuningStep-by-Step Guide to Fine-Tuning GPT-4o Mini Chat Models
Boost your model's capabilities with fine-tuning on massive datasets beyond prompt limits for top results across tasks. This hands-on notebook demos entity extraction from recipes using RecipeNLG—a go-to NER dataset—with GPT-4o mini. Get started today and deploy your custom model!
Harnessing Model Distillation for Superior Fine-Tuning
OpenAI's Distillation empowers you to train compact models like gpt-4o-mini using gpt-4o outputs, slashing costs and latency. This guide distills a dataset for top results, explores Structured Outputs for classification, and proves compatibility across models.
Boosting Conversational Reasoning with OpenAI's Reinforcement Fine-Tuning
Unlock advanced conversational reasoning in your models using OpenAI's Reinforcement Fine-Tuning (RFT). This guide uses a HealthBench-inspired dataset to evaluate GPT-4.1, craft custom graders, and refine behaviors via reinforcement learning for smarter dialogues.
Mastering Model Graders for Reinforcement Fine-Tuning
Discover how to apply reinforcement fine-tuning (RFT) to OpenAI's o4-mini model using a medical dataset for outcome prediction from transcripts. This guide equips experienced developers with step-by-step instructions to benchmark, grade, train, and deploy fine-tuned reasoning models for research purposes. Unlock sharper decision-making and enhanced context interpretation.
Fine-Tuning Mastery: Selecting SFT, DPO, RFT, or Vision – Featuring a DPO Deep Dive
Discover OpenAI's fine-tuning methods to boost your models for specific tasks. Learn when to use Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Reinforcement Fine-Tuning (RFT), or Vision Fine-Tuning, with an in-depth DPO guide to get you started.
Fine-tuningMastering Multilingual Reasoning: Fine-Tuning gpt-oss-20b with Hugging Face Transformers
Discover how to fine-tune OpenAI's gpt-oss-20b for chain-of-thought reasoning in multiple languages using Hugging Face's TRL library. From setup to inference, build a model that reasons in English, Spanish, French, Italian, German—or even mixes them for precise, interpretable responses. Perfect for underrepresented languages!
Fine-Tune gpt-oss for Superior Korean Performance: News and Chat Styles
Unlock peak Korean proficiency in OpenAI's gpt-oss open-weight models by fine-tuning with Korean news style and contemporary chat tones. This bilingual Korean/English guide details efficient BF16/QLoRA workflows, MXFP4 quantization paths, and MoE-aware LoRA setups for optimal results.