Principle:Openai Openai python Fine Tuning Job Creation

Knowledge Sources	OpenAI Fine-tuning Guide openai-python
Domains	Fine_Tuning, Model_Training
Last Updated	2026-02-15 00:00 GMT

Overview

A job submission pattern that initiates a fine-tuning run on a base model using uploaded training data with configurable hyperparameters.

Description

Fine-tuning job creation submits a training request to OpenAI's infrastructure. It specifies the base model, training file, optional validation file, hyperparameters (epochs, batch size, learning rate), and a custom suffix for the resulting model name. The job runs asynchronously on OpenAI's infrastructure with checkpoint saving.

Usage

Use this principle after uploading training data. Select a base model (e.g., "gpt-4o-mini-2024-07-18"), provide the training file ID, and optionally configure hyperparameters and validation data.

Theoretical Basis

# Job creation
job = create_fine_tuning_job(
    model=base_model,
    training_file=file_id,
    hyperparameters={
        "n_epochs": "auto",                  # Number of training epochs
        "batch_size": "auto",                # Training batch size
        "learning_rate_multiplier": "auto",  # Learning rate scaling
    },
    validation_file=val_file_id,  # Optional validation data
    suffix="my-custom-model",     # Model name suffix
    seed=42,                      # Reproducibility
)
# Returns job with .id, .status = "validating_files"

Related Pages

Implemented By

• Implementation:Openai_Openai_python_Fine_Tuning_Jobs_Create