Fine-tune a pre-trained ChatGPT model in Python

Here is an example of how you might fine-tune a pre-trained ChatGPT model in Python:

First, you will need to install the openai library and pandas using the following commands:

pip install openai
pip install pandas

Next, you will need to prepare your dataset for fine-tuning. This will typically involve formatting the data as a CSV file with columns for the input text and the corresponding response. You can use the pandas library to load and process the data:

import pandas as pd

# Load the dataset
data = pd.read_csv("my_dataset.csv")

# Extract the input text and response columns
input_texts = data["input"]
responses = data["response"]

# Convert the input texts and responses to lists
input_texts = input_texts.tolist()
responses = responses.tolist()

Once you have prepared your dataset, you can use the openai library to fine-tune a pre-trained ChatGPT model on the data. Here is an example of how you might do this:

import openai

# Load the pre-trained ChatGPT model
model = openai.Model.load("openai/chatbot")

# Set the model configuration
model_config = """
model_type = "text-davinci-002"
"""

# Set the fine-tuning configuration
fine_tuning_config = """
batch_size = 16
learning_rate = 1e-4
num_epochs = 10
"""

# Fine-tune the model on the dataset
model.finetune(input_texts, responses, config=fine_tuning_config)

# Save the fine-tuned model
model.save("my_fine_tuned_model")

This code will load the pre-trained ChatGPT model and fine-tune it on the input texts and responses in your dataset. The model will be fine-tuned using the specified batch size, learning rate, and number of epochs. Once the fine-tuning is complete, the model will be saved to the specified file so that it can be used later.

Keep in mind that this is just a simple example, and there are many other options and settings that you can use to customize the fine-tuning process. You can find more information about these options in the OpenAI API documentation.