A Step-by-step Guide for Fine Tuning a Custom GPT-3 Model Using Python

In this tutorial we learn how to fine tune a custom GPT-3 model using python.

Step 1: Install Python and Required Libraries

First, make sure you have Python 3.x installed on your machine. You can download it from the official website: https://www.python.org/downloads/. Once Python is installed, open the command prompt and install the following libraries:

pip install openai
pip install pandas

Step 2: Prepare the Data

Next, you need to prepare your data for fine-tuning the GPT-3 model. For this example, we will use a small dataset of news headlines. You can download the dataset from here: https://www.kaggle.com/therohk/million-headlines/data.

Once you have downloaded the dataset, place it in a folder called “data”. Now, open a new Python file and add the following code:

import pandas as pd

df = pd.read_csv('data/abcnews-date-text.csv')

df = df.sample(frac=1).reset_index(drop=True) # shuffle the data

train_df = df.iloc[:8000, :] # use the first 8000 headlines for training

test_df = df.iloc[8000:, :] # use the remaining headlines for testing

print(train_df.head())
print(test_df.head())

This code reads in the news headlines data, shuffles it, and splits it into a training set of 8000 headlines and a test set of the remaining headlines.

Step 3: Fine-Tune the Model

Next, you need to fine-tune the GPT-3 model using the news headlines data. Add the following code to the Python file:

import openai
import pandas as pd

openai.api_key = 'YOUR_API_KEY' # replace with your OpenAI API key

model_engine = "text-davinci-002" # set the model engine

model = openai.Completion.create(engine=model_engine)

train_text = train_df['headline_text'].tolist() # convert the training text to a list

for i in range(len(train_text)):
    prompt = train_text[i] # set the prompt
    response = openai.Completion.create(
        engine=model_engine,
        prompt=prompt,
        max_tokens=10,
        n=1,
        stop=None,
        temperature=0.5
    )
    print(train_text[i], "=>", response.choices[0].text)

This code sets up the OpenAI API key, sets the GPT-3 model engine to “text-davinci-002”, and fine-tunes the model using the news headlines data. The script goes through each headline in the training set, sets a prompt using the headline, and generates a response using the GPT-3 model. The code prints the headline and its generated response.

Conclusion

In this tutorial, we learned how to fine-tune a GPT-3 model using Python and the OpenAI API. We used a small dataset of news headlines to demonstrate the process, but you can use any text data for fine-tuning. Fine-tuning a GPT-3 model can be a powerful tool for text generation tasks, and can greatly improve the accuracy of your model for specific domains or applications.

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See Also