A Step-by-Step Guide to Fine-Tuning GPT-3.5 Turbo

Fine-tuning GPT-3.5 Turbo has become a sought-after feature since its release. This guide provides a comprehensive overview of how to fine-tune GPT-3.5 Turbo, ensuring you get the most out of this powerful model. Let's dive in!

Introduction

Since the release of GPT-3.5 Turbo, developers have eagerly awaited the ability to customize the model for unique user experiences. Fine-tuning GPT-3.5 Turbo allows for improved steerability, reliable output formatting, and custom tone adjustments. This ensures that the model aligns better with specific business requirements and expected output. 

Here’s what OpenAI says on their development blog.

This update gives developers the ability to customize models that perform better for their use cases and run these custom models at scale. Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match, or even outperform, base GPT-4-level capabilities on certain narrow tasks. As with all our APIs, data sent in and out of the fine-tuning API is owned by the customer and is not used by OpenAI, or any other organization, to train other models. — Andrew Peng, et al., OpenAI

Benefits of Fine-Tuning GPT-3.5 Turbo

Harnessing the full power of GPT-3.5 Turbo involves more than just using its default settings. By fine-tuning the model, businesses and developers can optimize its performance to cater to specific tasks and audiences. This process of customization not only refines the model's outputs but also ensures that it aligns seamlessly with the intended application. Let's have a look at the key advantages of fine-tuning the model.

Improved Steerability: One of the standout advantages of fine-tuning GPT-3.5 Turbo is its enhanced steerability. For instance, a company operating a customer support chatbot for its German-speaking clientele can ensure that the model consistently responds in fluent German, even if a user sends a query in English or another language. This ensures that the chatbot remains consistent with the company's target audience and provides a seamless experience for German-speaking users.

Reliable Output Formatting: Applications often have specific requirements when it comes to the format of responses. A tech company that has developed an API documentation generator, for example, might need the model to provide responses in a structured format, like JSON or XML. By fine-tuning GPT-3.5 Turbo, they can train the model to consistently generate responses in the desired format, ensuring that the generated documentation is both accurate and usable.

Custom Tone: Every business has a unique voice that sets it apart. With fine-tuning, GPT-3.5 Turbo can be tailored to match this voice. Imagine a playful and quirky brand like "Oreo" using the model for its social media responses. By fine-tuning the model, they can ensure that every response aligns with their brand's tone, making interactions feel more authentic and on-brand.

Steps to Fine-Tuning GPT-3.5 Turbo

Step 1: Prepare Data

When fine-tuning GPT-3.5 Turbo, it's crucial to format your data correctly. The data should be structured as a series of interactions between the system, user, and assistant. This format helps the model understand the context and flow of the conversation.

For instance, if you're using a dataset like ScienceQA, which consists of multiple-choice science questions, you can format it as follows:

def format_chat(row):
    return json.dumps({
        "messages": [
            {"role": "user", "content": row["prompt"]},
            {"role": "assistant", "content": str(row["response"])}
        ]
    })

def convert_dataset(df, file_name):
    df["conversation"] = df.apply(format_chat, axis=1)
    with open(file_name, 'w') as jsonl_file:
        for example in df["conversation"]:
            jsonl_file.write(example + '\n')

This results in a dataset where each entry looks like:

{
    "messages": [
        {"role": "user", "content": "Context: In a group of cows, ..."},
        {"role": "assistant", "content": "B"}
    ]
}

Step 2: Upload Files

Once your data is prepared, you need to upload it to OpenAI's API. This can be done using a curl command or through OpenAI's Python client. Here's a Python example:

import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")
file = openai.File.create(
    file=open("your_dataset_name.jsonl", "rb"),
    purpose='fine-tune'
)

You should receive a response with details about the uploaded file, including its ID.

Step3:  Initiate Fine-Tuning:

After uploading your data, you can initiate the fine-tuning process. This involves sending another API call with the IDs of your training and validation datasets:

train_data = "file-ID_of_training_data"
val_data = "file-ID_of_validation_data"

model = openai.FineTuningJob.create(
    model="gpt-3.5-turbo",
    training_file=train_data,
    validation_file=val_data,
    suffix="your_custom_suffix"
)

This will start the fine-tuning process, and you'll receive a response with details about the job.

Step 4: Use Fine-Tuned Model:

Once the fine-tuning process is complete, you can use the fine-tuned model by sending a request to OpenAI’s chat completions endpoint. This allows you to leverage the customizations you've made for specific tasks.

To use the fine-tuned model, you'd send a request similar to:

response = openai.Completion.create(
    model="gpt-3.5-turbo-your_custom_suffix",
    prompt="Your prompt here",
    max_tokens=150
)

This will return a response from your fine-tuned model.

Additional Tips

Fine-tuning GPT-3.5 Turbo can significantly enhance the model's performance, but to get the most out of it, consider the following tips:

Combine Techniques: Fine-tuning GPT-3.5 Turbo is most effective when combined with techniques like prompt engineering, information retrieval, and function calling. For instance, prompt engineering can help in guiding the model to produce desired outputs, while information retrieval can be used to pull specific data from large text sources.

Token Management: Fine-tuning GPT-3.5 Turbo can handle up to 4k tokens, which is double the capacity of previous fine-tuned models. This increased capacity means you can reduce prompt size by up to 90%, speeding up API calls and reducing costs. For example, if you previously had to use a lengthy prompt to guide the model, you can now embed some of that guidance directly into the fine-tuned model, resulting in shorter, more efficient prompts.

Customization for Brand Voice: A business with a recognizable brand voice can use fine-tuning to ensure the model's output aligns with their tone. For instance, a playful brand might want the model to produce fun and quirky responses, while a more formal brand might aim for concise and professional outputs.

Dataset Quality: One of the most critical steps in fine-tuning is creating the right training dataset. OpenAI recommends creating a diverse training set of conversations that are similar to the ones the model will see in production. For instance, if you're building a chatbot for a tech support service, your training data should reflect the kind of questions and issues users typically bring up.

Monitor Training and Validation Loss: The OpenAI API supports training with both a training and validation dataset. Monitoring the loss numbers on both during the course of training can provide insights into how well the model is improving. If the validation loss starts increasing while the training loss continues to decrease, it might be an indication of overfitting.

Compute Resources and Inference Code: Ensure you have reserved the right compute resources and have properly implemented the training and inference code. OpenAI’s new fine-tuning APIs make this process more accessible, but it's essential to be familiar with the requirements and best practices.

Stay Updated: While fine-tuning GPT-3.5 Turbo is now available, support for fine-tuning with function calling and gpt-3.5-turbo-16k will be released later. It's a good idea to keep an eye on OpenAI's updates to leverage new features and improvements as they roll out.