Submitting a Fine-Tuning Job: Organising the Workforce

The Six Triple Eight relied on discipline and coordination to execute their mission. We’ll mirror this by creating and submitting a fine-tuning job, allowing the LLM to learn from our curated dataset.

Fine-Tuning with OpenAI

When you create a fine-tuning job via client.fine_tuning.job.create(), you submit your configuration and dataset to OpenAI for training. Below are the key parameters and their purposes.

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1. Parameters Overview

model

• Description: The pre-trained GPT model you wish to fine-tune.
• Examples: "gpt-3.5-turbo", "davinci", "gpt-4-mini" (hypothetical).

training_file

• Description: The file ID of an uploaded JSONL file containing your training data.
• Note: Obtain this ID by uploading your dataset with the Files API and storing the file_id.

hyperparameters

• Description: A dictionary specifying the fine-tuning hyperparameters.
• Key Fields:
  • batch_size: Number of examples per batch (auto by default).
  • learning_rate_multiplier: Scale factor for the learning rate (auto by default).
  • n_epochs: Number of epochs (passes through the entire dataset).

suffix

• Description: A custom string (up to 18 characters) appended to the fine-tuned model name.

seed

• Description: Integer for reproducibility.
• Usage: Ensures the same randomization and consistent training results across runs.

validation_file

• Description: The file ID of a JSONL file containing your validation set.
• Optional: But recommended for tracking overfitting and ensuring a well-generalized model.

integrations

• Description: A list of integrations (e.g., Weights & Biases) you want enabled for the job.
• Fields: Typically includes type and integration-specific configurations.

---

client.fine_tuning.job.create(
    model="gpt-3.5-turbo",
    training_file="train_id",
    hyperparameters={
        "n_epochs": 1
    },
    validation_file="val_id"
)

Managing Fine-Tuning Jobs
Retrieves up to 10 fine-tuning jobs.

client.fine_tuning.jobs.list(limit=10)

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Retrieve a Specific Job

client.fine_tuning.retrieve("job_id")

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List Events for a Job

client.fine_tuning.list_events(
    fine_tuning_job_id="xxxx",
    limit=5
)

Summary

• Model Selection: Choose a suitable GPT model to fine-tune.

• Data Preparation: Upload JSONL files and note their IDs.

• Hyperparameters: Tune batch size, learning rate, and epochs for optimal performance.

• Monitoring: Use validation files, job retrieval, and event logging to ensure your model trains effectively.

• Reproducibility: Set a seed if consistent results are important for your workflow.

• By following these steps, you’ll have a clear path to submitting and managing your fine-tuning jobs in OpenAI, ensuring your model is trained precisely on your custom data.