Your Guide to Fine-Tuning OpenAI’s GPT-3.5 Turbo

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    If you’re an AI developer, data scientist, machine learning practitioner, you are likely eager to harness the full power of OpenAI’s GPT-3.5 Turbo. You might ask, how can you customize this powerful language model to address specific requirements and use cases? The answer lies in fine-tuning. This guide will provide you comprehensive insights and practical steps on fine-tuning GPT-3.5 Turbo for enhanced performance and productivity.

    GPT-3.5 Turbo, among the cutting-edge models offered by OpenAI, has proven its efficacy in solving problems with a level of accuracy surpassing its predecessors. Its fine-tuning feature is a game-changer, enabling users to tailor the model to their unique needs. From improved steerability and output formatting to generating a custom tone, fine-tuning GPT-3.5 Turbo paves the way for innovative applications.

    What makes fine-tuning even more efficient is its combination with techniques like prompt engineering, function calling, and information retrieval. This amalgamation magnifies the capabilities of the model, making it more versatile and robust. Apart from these technical benefits, the importance of safety measures in fine-tuning cannot be overlooked.

    Understanding the concept of fine-tuning

    Fine-tuning, a critical aspect of machine learning, allows developers to customize an existing model like GPT-3.5 Turbo to enhance its performance for specific tasks or applications. In essence, fine-tuning is a process of refinement, where an already trained model is further tailored to improve its ability to handle specialized tasks with accuracy and efficiency.

    Let’s dive deeper to understand how fine-tuning works. Imagine you have an AI model that has been trained on a vast amount of data and has learned to perform a broad spectrum of language-related tasks. Although the model is sophisticated and versatile, it may not perfectly suit your specific need, be it translating texts in a particular domain or generating specific reports. That's where fine-tuning comes into play.

    When you fine-tune a model, you take the existing pre-trained model and train it further on a smaller dataset that is more specific to your tasks. The model, therefore, learns from the nuances of the new data and adjusts its internal parameters accordingly. As a result, the fine-tuned model performs better on the specific tasks compared to the original pre-trained model.

    In the context of GPT-3.5 Turbo, fine-tuning not only improves performance on the defined tasks but also enhances other features. For example, it can increase steerability, allowing more control over the model's responses; ensure reliable output formatting, standardizing the results for easy analysis or use; and establish a custom tone, reflecting a brand or user voice.

    The fine-tuning process generally involves several steps, beginning with the preparation of data specific to your tasks and uploading it for training. You create a fine-tuning job specifying parameters and configurations, and once it's done, you can harness the power of the fine-tuned model for your specialized tasks.

    The next sections will outline the practical steps involved in fine-tuning and provide valuable insights for successful implementation.

    Steps to Fine-Tune GPT-3.5 Turbo

    Fine-tuning GPT-3.5 Turbo is a systematic process that involves several key steps. These are steps that have been refined and tested by researchers and developers to optimize the process for a wide range of use cases. Here's a step-by-step guide on how to fine-tune GPT-3.5 Turbo:

    1. Data Preparation:

    The first step in the process is preparing your data. As with any machine learning task, the quality of your data plays a critical role in the success of your model. This data should be specific to your task or project and representative of the problem you're trying to solve. It's critical to ensure that your data is cleaned, well-curated and formatted in a way that's compatible with the GPT-3.5 Turbo model. Here is an example of the format required for an example:

    {'role': 'system', 'content': 'You are a technical writer for, writing in the tone of voice of a writer for the brand.'}
    {'role': 'user', 'content': "You are creating an article around the topic of [TOPIC]. Now write the section of the article that has the following subheading, using the tone of voice for the Sanity brand: [SUBHEADING]."}
    {'role': 'assistant', 'content': "[EXAMPLE CONTENT"}

    Once you have the data in a CSV file, you can ask ChatGPT's 'Advanced Data Analysis' plugin to convert to a .jsonl file, which is required for upload to OpenAI:

    Convert to jsonl file using ChatGPT's Advanced Data Analysis

    You can then use the following code to check the format of your file:

    import json
    import numpy as np
    from collections import defaultdict
    data_path = "YOUR DATA"
    # Load the dataset
    with open(data_path, 'r', encoding='utf-8') as f:
        dataset = [json.loads(line) for line in f]
    # Initial dataset stats
    print("Num examples:", len(dataset))
    print("First example:")
    for message in dataset[0]["messages"]:
    # Format error checks
    format_errors = defaultdict(int)
    for ex in dataset:
        if not isinstance(ex, dict):
            format_errors["data_type"] += 1
        messages = ex.get("messages", None)
        if not messages:
            format_errors["missing_messages_list"] += 1
        for message in messages:
            if "role" not in message or "content" not in message:
                format_errors["message_missing_key"] += 1
            if any(k not in ("role", "content", "name") for k in message):
                format_errors["message_unrecognized_key"] += 1
            if message.get("role", None) not in ("system", "user", "assistant"):
                format_errors["unrecognized_role"] += 1
            content = message.get("content", None)
            if not content or not isinstance(content, str):
                format_errors["missing_content"] += 1
        if not any(message.get("role", None) == "assistant" for message in messages):
            format_errors["example_missing_assistant_message"] += 1
    if format_errors:
        print("Found errors:")
        for k, v in format_errors.items():
            print(f"{k}: {v}")
        print("No errors found")

    It is likely to will encounter some issues - you can use ChatGPT to further investigate these. The easiest course of action is to ask it to remove the examples causing any issues (as long as it is a minor number).

    You can then count the number of tokens and get an estimated cost for the finetuning process:

    !pip install tiktoken
    import tiktoken # for token counting
    import numpy as np
    encoding = tiktoken.get_encoding("cl100k_base")
    # not exact!
    # simplified from
    def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):
        num_tokens = 0
        for message in messages:
            num_tokens += tokens_per_message
            for key, value in message.items():
                num_tokens += len(encoding.encode(value))
                if key == "name":
                    num_tokens += tokens_per_name
        num_tokens += 3
        return num_tokens
    def num_assistant_tokens_from_messages(messages):
        num_tokens = 0
        for message in messages:
            if message["role"] == "assistant":
                num_tokens += len(encoding.encode(message["content"]))
        return num_tokens
    def print_distribution(values, name):
        print(f"\n#### Distribution of {name}:")
        print(f"min / max: {min(values)}, {max(values)}")
        print(f"mean / median: {np.mean(values)}, {np.median(values)}")
        print(f"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}")
    # Warnings and tokens counts
    n_missing_system = 0
    n_missing_user = 0
    n_messages = []
    convo_lens = []
    assistant_message_lens = []
    for ex in dataset:
        messages = ex["messages"]
        if not any(message["role"] == "system" for message in messages):
            n_missing_system += 1
        if not any(message["role"] == "user" for message in messages):
            n_missing_user += 1
    print("Num examples missing system message:", n_missing_system)
    print("Num examples missing user message:", n_missing_user)
    print_distribution(n_messages, "num_messages_per_example")
    print_distribution(convo_lens, "num_total_tokens_per_example")
    print_distribution(assistant_message_lens, "num_assistant_tokens_per_example")
    n_too_long = sum(l > 4096 for l in convo_lens)
    print(f"\n{n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning")
    # Pricing and default n_epochs estimate
    n_epochs = TARGET_EPOCHS
    n_train_examples = len(dataset)
    if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:
        n_epochs = min(MAX_DEFAULT_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)
    elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:
        n_epochs = max(MIN_DEFAULT_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)
    n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)
    print(f"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training")
    print(f"By default, you'll train for {n_epochs} epochs on this dataset")
    print(f"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens")

    2. Upload Files to OpenAI:

    Once your data is ready, the next step is to upload it using OpenAI's platform. The data needs to be transferred securely to the system where the fine-tuning will take place. This process is facilitated through OpenAI's file upload process. It's also essential to consider the privacy and security measures during this process. Here the required Python to upload a file:

    !pip install openai
    import os
    import openai
    openai.api_key = "[YOUR API KEY]"
      file=open("[FILE LOCATION]", "rb"),

    The response from OpenAI will be something like this:

    <File file id=file-> JSON: {
      "object": "file",
      "purpose": "fine-tune",
      "filename": "file",
      "bytes": 297512,
      "created_at": 1694163069,
      "status": "uploaded",
      "status_details": null

    3. Create Fine-Tuning Job:

    After your data is uploaded, the next step is to create a fine-tuning job. Here's an example:

    openai.FineTuningJob.create(training_file="REPLACE WITH THE FILE ID OPENAI GIVES YOU AFTER YOU UPLOAD", model="gpt-3.5-turbo")

    The response will be something like:

    <FineTuningJob fine_tuning.job id=> JSON: {
      "object": "fine_tuning.job",
      "model": "gpt-3.5-turbo-0613",
      "created_at": 1694163355,
      "finished_at": null,
      "fine_tuned_model": null,
      "organization_id": "EXAMPLE",
      "result_files": [],
      "status": "created",
      "validation_file": null,
      "training_file": "YOUR FILE ID",
      "hyperparameters": {
        "n_epochs": 3
      "trained_tokens": null,
      "error": null

    OpenAI will email when your model is ready. If you want to check the status of the process, you can use the following:

    # Retrieve the state of a fine-tune
    openai.FineTuningJob.retrieve("JOB ID")

    4. Test and Evaluate the Fine-Tuned Model:

    Once your model has been fine-tuned, it's time to test it. This involves running the model on a test dataset to evaluate its performance. Testing allows you to see how well the model has learned from the fine-tuning process and whether it's ready to be deployed for its intended purpose. Here's how to use you model:

    completion = openai.ChatCompletion.create(
        {"role": "system", "content": "Your system prompt."},
        {"role": "user", "content": "Your prompt in the same format as the examples you gave it to learn"}

    To get your model ID, use the following:

    response = openai.FineTuningJob.retrieve("YOUR JOB ID")
    fine_tuned_model_id = response["fine_tuned_model"]

    5. Deploy the Model:

    The final step in the process is deploying the fine-tuned model. This involves integrating the model into your application or workload so it can start generating the specialized outputs you need.

    Remember, safety is a top priority for OpenAI, and fine-tuning data is always passed through their Moderation API. This ensures that the model learns in a secure environment, and safety standards are met.

    On the horizon, OpenAI has announced plans to launch a fine-tuning UI in the future. This will further streamline the process and give developers easier access to information about ongoing fine-tuning jobs.

    With these steps, you are now ready to fine-tune GPT-3.5 Turbo to meet your specific needs. Whether you are looking to improve output consistency, enhance instruction following, or match a specific brand style, the power to customize and enhance GPT-3.5 Turbo lies with you.

    Keep in mind that as with any process, trial and error might be involved. So be prepared to test, evaluate, and even redo your fine-tuning process until you arrive at a model that satisfactorily meets your needs. Remember the ultimate goal is to have a model that not only performs efficiently but also aligns with the specific requirements of your project.

    Best Practices for Fine-Tuning GPT-3.5 Turbo

    Successful fine-tuning of GPT-3.5 Turbo hinges on following certain best practices designed to optimize outcomes. Here are some key strategies that can help improve the fine-tuning process:

    1. Understand Your Use Case:

    Before you begin fine-tuning, it's vital to have a clear understanding of your specific use case. Knowing the problem you're aiming to solve, your audience, and the desired outcome will help guide your fine-tuning strategy.

    2. Data Quality and Relevance:

    One of the most important aspects of fine-tuning is your data. Ensure that your training data is clean, relevant, and representative of the problem you're targeting. Be mindful of biases in your data as they can influence your model's outputs.

    3. Leverage Other Techniques:

    While fine-tuning is powerful, it doesn’t work in isolation. Use it in conjunction with other techniques such as prompt engineering, information retrieval, and function calling to amplify the model's performance.

    4. Prioritize Safety:

    Always remember that safety is a priority. Fine-tuning data goes through OpenAI's moderation system to ensure compliance with safety standards.

    5. Test Iteratively:

    Once your model is fine-tuned, test it thoroughly. Apply it to a variety of scenarios within your use case to ensure that it performs as expected. Be ready to iterate on your fine-tuning job as needed.

    6. Stay Updated:

    OpenAI continuously updates its models and capabilities. Be sure to stay updated with these changes and adapt your fine-tuning strategy accordingly. For instance, the transitioning from GPT-3.5 to GPT-4 may bring enhanced features that could benefit your specific tasks.

    7. Keep an Eye on Costs:

    Remember, fine-tuning comes with costs, broken down into training and usage costs. Ensure to align your fine-tuning strategy with your budget constraints. Strategically limit your scope and select the most significant areas where fine-tuning will add the most value.

    8. Experiment and Learn:

    Fine-tuning is a continuous learning process. The more you experiment and learn from each fine-tuning job, the better you will understand the model behavior and how to optimize it for your specific use case.

    By adopting these best practices, you can significantly enhance the utility and performance of the GPT-3.5 Turbo model. With the right approach, fine-tuning can help you adapt this robust AI model to solve complex, specific tasks more accurately and efficiently than ever before. Remember, the knowledge and skills to fine-tune AI models are ever-evolving, so stay curious and keep learning to stay at the forefront of this exciting field.

    Real-world examples of successful fine-tuning with GPT-3.5 Turbo

    Fine-tuning GPT-3.5 Turbo isn't a theoretical concept confined to research papers and textbooks - it's a tried and tested strategy that many developers and businesses have successfully put into practice. Let's explore a few examples of how fine-tuning has been used to optimize GPT-3.5 Turbo for real-world applications.

    1. Enterprise Customer Service:

    In customer service, fine-tuning has proven to be remarkably effective. Businesses have used fine-tuning to match the tone and vocabulary of GPT-3.5 Turbo with their brand identity. This enables a more personalized interaction for customers, as the AI-powered chatbot is not only assisting with inquiries but doing so in a way that reflects the brand's voice and values. It enhances customer engagement and ultimately leads to higher satisfaction.

    2. Contextual Advertising:

    The field of advertising has also seen the advantage of fine-tuning GPT-3.5 Turbo. Businesses have used fine-tuning to generate branded taglines, ad copy, and social posts. The conventional AI model may churn out generic content, but a fine-tuned model can produce content that aligns with a specific brand's style and messaging. This gives marketers the ability to create personalized and targeted content on the fly, driving higher engagement and conversion rates.

    3. Superior Translation Services:

    Translation services have also benefited from fine-tuning GPT-3.5 Turbo. With fine-tuning, the general AI model can be trained on specific language pair translations, enabling it to produce more natural, human-like translations. For instance, a fine-tuned model can be developed to not only translate English to French but also to do so in a vernacular language style for a specific region of France.

    4. Domain-Specific Report Writing:

    Organizations often need to generate reports containing domain-specific terminologies and formats. A fine-tuned GPT-3.5 Turbo model has been successfully employed in such scenarios. By training the model on datasets that include a specific industry's terminology and report formats, organizations have been able to automate the generation of complex, domain-specific reports - a process that traditionally required a significant amount of manual effort and expertise.

    5. Custom Code Generation:

    Developers have used fine-tuning GPT-3.5 Turbo to match the style and conventions of a specific programming language, turning it into a proficient coding assistant. The fine-tuned model can generate code snippets or even entire programs that adhere to the best practices of the selected programming language, significantly aiding the coding process and enhancing productivity.

    6. Focused Text Summarization:

    In the media industry, fine-tuning has been used to generate text summaries focusing on critical data points, such as sports scores. By training the model on specific summarization tasks, developers have been able to create AI applications that accurately highlight the most crucial information from large articles or reports.

    These examples demonstrate that the potential uses of fine-tuning GPT-3.5 Turbo are only limited by one's imagination. From customer service to advertising, translation, report generation, code production, and text summarization, fine-tuning enables users to extend the range of GPT-3.5 Turbo beyond its generic capabilities, tailoring it to address specific needs more effectively.

    Limitations of GPT-3.5 Turbo Fine-Tuning

    While the fine-tuning feature of GPT-3.5 Turbo offers immense potential for customizing the AI model, it’s essential to understand that it is not a silver bullet. There are certain limitations and challenges users should be aware of while setting their expectations.

    1. Data Limitations:

    Fine-tuning is highly dependent on the quality and specificity of the data you are using. If the training data is not representative enough of the domain or the tasks, the fine-tuned model might not perform efficiently. Inappropriate or insufficient data can lead to underperforming models.

    2. Time and Computational Resources:

    The process of fine-tuning an AI model, especially one as complex as GPT-3.5 Turbo, can be computationally intensive and time-consuming. Depending on the scale of the project, it might require substantial computational resources and time, posing challenges for smaller teams or individuals with limited resources.

    3. Overfitting:

    While fine-tuning can enhance the model's performance, there is a risk of overfitting if not handled correctly. Overfitting happens when the model performs exceptionally well on the training data but fails to generalize well to unseen data. Balancing this requires skill and experience in machine learning practices.

    4. Costs:

    Fine-tuning GPT-3.5 Turbo comes with costs – both for training and usage – which might be substantial depending on the extent of fine-tuning required. For small businesses or individuals, these costs may be a limiting factor.

    5. Ethical and Privacy Concerns:

    Fine-tuning a language model can sometimes raise ethical and privacy concerns, especially when dealing with sensitive data. It’s crucial to ensure the data used for fine-tuning is secure, anonymized, and respects privacy regulations.

    6. Complexity of Fine-Tuning:

    Fine-tuning GPT-3.5 Turbo, like other advanced AI models, involves a learning curve. Users need to familiarize themselves with various aspects of the process, such as preparing data, arranging resources, understanding fine-tuning parameters, and assessing model performance. For those new to the process, it can seem complex and daunting.

    These limitations do not undermine the significant benefits of fine-tuning GPT-3.5 Turbo but rather provide a realistic perspective on its application. With a clear understanding of these limitations, developers and researchers can better plan their projects, allocate resources, and set realistic expectations. Ultimately, the key to successfully leveraging fine-tuning lies in balancing its benefits against these constraints and continuously learning and adapting as this field of AI continues to evolve.


    In conclusion, GPT-3.5 Turbo represents a significant leap forward in the world of language models. Its ability to be fine-tuned, opens up a multitude of possibilities in how AI can be leveraged for specific tasks, whether it's creating personalized customer interactions, crafting brand-specific advertising, generating domain-oriented reports, or even assisting in coding tasks.

    However, the power and promise of GPT-3.5 Turbo's fine-tuning do not come without their challenges. Success in fine-tuning depends heavily on the quality of the training data, computational resources, and avoiding pitfalls such as overfitting. Ethical and privacy issues are also integral considerations in shaping the fine-tuning strategy.

    Despite these challenges, the potential benefits of fine-tuning GPT-3.5 Turbo are immense. The successful real-world applications of this process, as well as the best practices that have been formulated, provide a roadmap for developers and researchers to explore and exploit this feature's potential.

    The continuous development of GPT models, combined with the fine-tuning process, symbolizes a future where AI is not just a tool but a highly adaptable solution that can be tailored to meet unique, complex needs across diverse domains.

    Richard Lawrence

    About Richard Lawrence

    Constantly looking to evolve and learn, I have have studied in areas as diverse as Philosophy, International Marketing and Data Science. I've been within the tech space, including SEO and development, since 2008.
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