Generative AI - Fine-tuning GPT-3 for Custom Text Generation Tasks

Text Generation and Language Models

Fine-tuning GPT-3 for Custom Text Generation Tasks

GPT-3 is a powerful language model that OpenAI made. Fine-tuning it for jobs that require custom text output can make it work much better in certain situations. We're going to show you how to use OpenAI's API to fine-tune GPT-3.

Step 1: Setup and Import Necessary Libraries

First, we need to get the OpenAI Python package and add the tools we need.

# Install the OpenAI package
!pip install openai

# Import the library
import openai

Pip is used to install the OpenAI package.

To work with the GPT-3 model, we bring in the OpenAI package.

Step 2: Set Up API Key

To prove that our requests are real, we need to set up our OpenAI API key. OpenAI will give you an API key.

# Set up the API key


openai.api_key = 'your-api-key'

Put in your real OpenAI API key instead of "your-api-key." You can get into the GPT-3 type with this key.

Step 3: Prepare the Training Data

The training data needs to be set up in a way that GPT-3 can understand. You should use the JSONL (JSON Lines) style for the data.

# Example training data
training_data = [
    {"prompt": "Translate English to French: 'Hello, how are you?'\n\n", "completion": "Bonjour, comment ça va?\n"},
    {"prompt": "Translate English to French: 'What is your name?'\n\n", "completion": "Comment t'appelles-tu?\n"}
]

# Save the training data to a file
with open('training_data.jsonl', 'w') as f:
    for item in training_data:
        f.write(f"{json.dumps(item)}\n")

We make a list of dictionaries, and each one is a teaching case with a question and an answer.

The training data is saved in a JSONL file.

Step 4: Upload Training Data to OpenAI

We send the training data file to OpenAI so that it can be improved.

# Upload the training data file
response = openai.File.create(
  file=open('training_data.jsonl', 'rb'),
  purpose='fine-tune'
)
training_file_id = response['id']

The training data file is uploaded with the openai.File.create method.

The file is given a number that we will later use. This number is stored in the training_file_id variable.

Step 5: Fine-tune the Model

The submitted training data file is used to start the fine-tuning process.

# Fine-tune the model
response = openai.FineTune.create(training_file=training_file_id, model="davinci")
fine_tune_id = response['id']

We call the openai.FineTune.create method to start the fine-tuning process.

We tell it to use the uploaded file ID as the training_file and "davinci" as the model.

There is an ID given to the fine-tuning job, which we store in the fine_tune_id variable.

Step 6: Check Fine-tuning Status

The fine-tuning process is tracked so we know when it's done.

# Check fine-tuning status
response = openai.FineTune.retrieve(id=fine_tune_id)
status = response['status']
print(f"Fine-tuning status: {status}")

We use the openai.FineTune.retrieve method to get the status of the fine-tuning job.

To keep track of the work, we print the state.

Step 7: Use the Fine-tuned Model

The fine-tuned model can be used to make words once the fine-tuning is done.

# Generate text using the fine-tuned model
response = openai.Completion.create(
  model=fine_tune_id,
  prompt="Translate English to French: 'Good morning!'\n\n",
  max_tokens=50
)
print(response.choices[0].text.strip())

We use the finish of openai.fine-tuned model to make a method that makes text.

We give you a prompt and tell you what the model is: the fine-tuned model ID.

The length of the created text is limited by the value we set for max_tokens.

We print the words that we made.

Full Code

# Install the OpenAI package
!pip install openai

# Import the library
import openai
import json

# Set up the API key
openai.api_key = 'your-api-key'

# Example training data
training_data = [
    {"prompt": "Translate English to French: 'Hello, how are you?'\n\n", "completion": "Bonjour, comment ça va?\n"},
    {"prompt": "Translate English to French: 'What is your name?'\n\n", "completion": "Comment t'appelles-tu?\n"}
]

# Save the training data to a file
with open('training_data.jsonl', 'w') as f:
    for item in training_data:
        f.write(f"{json.dumps(item)}\n")

# Upload the training data file
response = openai.File.create(
  file=open('training_data.jsonl', 'rb'),
  purpose='fine-tune'
)
training_file_id = response['id']

# Fine-tune the model
response = openai.FineTune.create(training_file=training_file_id, model="davinci")
fine_tune_id = response['id']

# Check fine-tuning status
response = openai.FineTune.retrieve(id=fine_tune_id)
status = response['status']
print(f"Fine-tuning status: {status}")

# Generate text using the fine-tuned model
response = openai.Completion.create(
  model=fine_tune_id,
  prompt="Translate English to French: 'Good morning!'\n\n",
  max_tokens=50
)
print(response.choices[0].text.strip())