Custom Models | Advanced Concepts

Introduction

Custom models in the OpenAI API allow you to create and deploy AI models tailored to specific tasks or domains. This tutorial covers the concept of custom models, how to create and manage them using the OpenAI API, and provides examples in JavaScript and Python.

What are Custom Models?

Custom models are specialized AI models trained or fine-tuned for specific tasks or datasets. They enable developers to leverage OpenAI's powerful infrastructure while customizing models to meet unique requirements.

Using the OpenAI API for Custom Models

Creating a Custom Model

To create a custom model, you need to define its architecture, training data, and evaluation criteria through API requests.

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

{
    "model": "custom-model-1",
    "architecture": "gpt-3.5-turbo",
    "description": "Custom model for sentiment analysis",
    "training_data": [
        {"prompt": "Positive review: This product is excellent\nSentiment: Positive\n"},
        {"prompt": "Negative review: The service was terrible\nSentiment: Negative\n"}
    ],
    "evaluation_criteria": "accuracy"
}

Replace YOUR_API_KEY with your actual API key. In this example, the architecture specifies the base model, training_data provides examples for training, and evaluation_criteria defines the metric to evaluate model performance.

Managing Custom Models

Once created, you can manage custom models by updating their configuration, monitoring performance, and deploying them for inference tasks.

GET /v1/models/custom-model-1 HTTP/1.1
Host: api.openai.com
Authorization: Bearer YOUR_API_KEY

This API request retrieves details about the custom model custom-model-1 including its configuration and performance metrics.

Parameters

Here are some common parameters used when creating and managing custom models:

model: Name or ID of the custom model.
architecture: Base model architecture, such as "gpt-3.5-turbo".
description: Brief description or purpose of the custom model.
training_data: Examples used to train the custom model.
evaluation_criteria: Metric used to evaluate the model's performance.

Examples in JavaScript

Here's how you can create and manage custom models using the OpenAI API in JavaScript:

const axios = require('axios');

const apiKey = 'YOUR_API_KEY';
const endpoint = 'https://api.openai.com/v1/models';

async function createCustomModel(modelName, architecture, description, trainingData, evaluationCriteria) {
    try {
        const response = await axios.post(endpoint, {
            model: modelName,
            architecture: architecture,
            description: description,
            training_data: trainingData,
            evaluation_criteria: evaluationCriteria
        }, {
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`
            }
        });

        console.log('Custom model created:', response.data);
        return response.data;
    } catch (error) {
        console.error('Error:', error);
    }
}

const modelName = 'custom-model-1';
const architecture = 'gpt-3.5-turbo';
const description = 'Custom model for sentiment analysis';
const trainingData = [
    {"prompt": "Positive review: This product is excellent\nSentiment: Positive\n"},
    {"prompt": "Negative review: The service was terrible\nSentiment: Negative\n"}
];
const evaluationCriteria = 'accuracy';

createCustomModel(modelName, architecture, description, trainingData, evaluationCriteria);

Examples in Python

Here's how you can create and manage custom models using the OpenAI API in Python:

import openai

api_key = 'YOUR_API_KEY'
openai.api_key = api_key

def create_custom_model(model_name, architecture, description, training_data, evaluation_criteria):
    response = openai.Model.create(
        model=model_name,
        architecture=architecture,
        description=description,
        training_data=training_data,
        evaluation_criteria=evaluation_criteria
    )
    
    print('Custom model created:', response)

model_name = 'custom-model-1'
architecture = 'gpt-3.5-turbo'
description = 'Custom model for sentiment analysis'
training_data = [
    {"prompt": "Positive review: This product is excellent\nSentiment: Positive\n"},
    {"prompt": "Negative review: The service was terrible\nSentiment: Negative\n"}
]
evaluation_criteria = 'accuracy'

create_custom_model(model_name, architecture, description, training_data, evaluation_criteria)

Conclusion

Creating custom models with the OpenAI API allows you to tailor AI capabilities to specific tasks or datasets. By following the steps outlined in this tutorial and using examples in JavaScript and Python, you can effectively harness the power of customized AI models for various applications.

Creating Custom Models with OpenAI API