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AI & ML · Concept

Epoch

Fine-tuning hyperparameters control how a model learns from examples. Start with defaults, then adjust epochs, learning rate multiplier, or batch size after evaluation.

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Syntax

client.fineTuning.jobs.create({ training_file, model, method: { type: "supervised", supervised: { hyperparameters: { n_epochs } } } })

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  • Fine-tuning
  • Supervised fine-tuning
  • Evals
  • Training dataset
  • Test dataset
  • Learning rate multiplier
  • Batch size

Source: Fine-tuning best practices | OpenAI API. Checked 2026-11-03.