Fine Tuning Models | Advanced Concepts

Introduction

Fine-tuning models allows you to customize pretrained models from OpenAI, adapting them to specific tasks or domains. This tutorial covers the concept of fine-tuning, how to perform it using the OpenAI API, and provides examples in JavaScript and Python.

What is Fine-Tuning?

Fine-tuning involves taking a pretrained model and updating its parameters to better fit a specific dataset or task. It leverages the knowledge learned from a large, general dataset to improve performance on a more specific dataset or task.

Using the OpenAI API for Fine-Tuning

API Request

To fine-tune a model, you typically use a sequence of API requests to upload your dataset and configuration, initiate the fine-tuning process, and retrieve the fine-tuned model.

POST /v1/fine-tune HTTP/1.1
Host: api.openai.com
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

{
    "model": "gpt-3.5-turbo",
    "examples": [
        {"prompt": "Question: What is the capital of France?\nAnswer: Paris\n"},
        {"prompt": "Question: What is the capital of Germany?\nAnswer: Berlin\n"}
    ],
    "train_steps": 1000
}

Replace YOUR_API_KEY with your actual API key. In this example, the examples array contains prompts and expected answers for training the model, and train_steps specifies the number of training steps.

API Response

The API responds with the status of the fine-tuning job and, upon completion, provides access to the fine-tuned model.

HTTP/1.1 200 OK
Content-Type: application/json

{
    "model": "gpt-3.5-turbo",
    "fine_tuned_model_id": "ftm-1234567890"
}

This response includes the ID of the fine-tuned model, which you can use to access it for inference tasks.

Parameters

Here are some common parameters used in fine-tuning:

model: The base model to fine-tune, such as "gpt-3.5-turbo".
examples: Training examples containing prompts and expected outputs.
train_steps: Number of training steps or epochs to fine-tune the model.

Examples in JavaScript

Here's how you can initiate fine-tuning using the OpenAI API in JavaScript:

const axios = require('axios');

const apiKey = 'YOUR_API_KEY';
const endpoint = 'https://api.openai.com/v1/fine-tune';

async function fineTuneModel(examples, trainSteps) {
    try {
        const response = await axios.post(endpoint, {
            model: 'gpt-3.5-turbo',
            examples: examples,
            train_steps: trainSteps
        }, {
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`
            }
        });

        console.log('Fine-tuning job initiated:', response.data);
        return response.data;
    } catch (error) {
        console.error('Error:', error);
    }
}

const examples = [
    {"prompt": "Question: What is the capital of France?\nAnswer: Paris\n"},
    {"prompt": "Question: What is the capital of Germany?\nAnswer: Berlin\n"}
];
const trainSteps = 1000;

fineTuneModel(examples, trainSteps);

Examples in Python

Here's how you can initiate fine-tuning using the OpenAI API in Python:

import openai

api_key = 'YOUR_API_KEY'
openai.api_key = api_key

def fine_tune_model(examples, train_steps):
    response = openai.FineTune.create(
        model="gpt-3.5-turbo",
        examples=examples,
        train_steps=train_steps
    )
    
    print('Fine-tuning job initiated:', response)

examples = [
    {"prompt": "Question: What is the capital of France?\nAnswer: Paris\n"},
    {"prompt": "Question: What is the capital of Germany?\nAnswer: Berlin\n"}
]
train_steps = 1000

fine_tune_model(examples, train_steps)

Conclusion

Fine-tuning models with the OpenAI API allows you to customize pretrained models for specific tasks or datasets. By following the steps outlined in this tutorial and using examples in JavaScript and Python, you can effectively leverage fine-tuning to improve the performance and adaptability of AI models in various applications.

Fine-Tuning Models with OpenAI API