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Model optimization - OpenAI API Fine-tune a model OpenAI models are already pre-trained to perform across a broad range of subjects and tasks Fine-tuning lets you take an OpenAI base model, provide the kinds of inputs and outputs you expect in your application, and get a model that excels in the tasks you'll use it for
Fine-tuning best practices - OpenAI API Fine-tuning best practices Learn best practices to fine-tune OpenAI models and get better peformance, optimization, and task-specific model behavior
Reinforcement fine-tuning - OpenAI API Fine-tuning jobs take some time to complete, and RFT jobs tend to take longer than SFT or DPO jobs To monitor the progress of your fine-tune job, use the fine-tuning dashboard or the API
Fine-tuning - OpenAI API Creates a fine-tuning job which begins the process of creating a new model from a given dataset Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete
Vision fine-tuning - OpenAI API Vision fine-tuning uses image inputs for supervised fine-tuning to improve the model's understanding of image inputs This guide will take you through this subset of SFT, and outline some of the important considerations for fine-tuning with image inputs
Direct preference optimization - OpenAI API Direct Preference Optimization (DPO) fine-tuning allows you to fine-tune models based on prompts and pairs of responses This approach enables the model to learn from more subjective human preferences, optimizing for outputs that are more likely to be favored
Pricing - OpenAI API Tokens used for model grading in reinforcement fine-tuning are billed at that model's per-token rate Inference discounts are available if you enable data sharing when creating the fine-tune job
Graders - OpenAI API Graders are a way to evaluate your model's performance against reference answers Our graders API is a way to test your graders, experiment with results, and improve your fine-tuning or evaluation framework to get the results you want