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How to Fine-tune GPT3.5 Turbo for Custom Use Cases

Learn about customizing GPT3.5 Turbo with fine-tuning for custom use cases to individual user experiences.

Fine-tuning GPT3.5 Turbo step by step with example


Mon Mar 18 2024


Felix Wunderlich



What is Fine-tuning?

Fine-tuning is a process where a pre-trained Large Language Model (LLM) like GPT-3.5 Turbo is further trained (or “fine-tuned”) on smaller, specialized datasets. This secondary training phase allows the model to learn the nuances of a specific domain, task, or user’s preferences.

Simply explained: Instead of “asking” a large language model what to do by prompting it, you “show it” how you want it to do be done by providing a number of examples. After a model is fine-tuned, you don’t have to give it as many examples to start with. This makes running the model cheaper, faster, and more accurate for specific tasks.

Why Fine-Tune Large Language Models?

Higher Quality Results: By fine-tuning, you guide the AI to understand not just what you’re asking for, but how you want it delivered. This leads to outcomes that are more aligned with your expectations, often surpassing the quality of general prompts.

Train on More Examples: There’s a limit to how much you can communicate with a model in a single prompt. Fine-tuning breaks through this barrier, allowing the model to learn from a much larger set of examples and understand your requirements deeply.

Token Savings: Using shorter prompts after fine-tuning means you use fewer tokens for each request. In the world of AI, where efficiency is key, saving on tokens directly translates to cost savings.

Lower Latency Requests: When a model is fine-tuned to your needs, it responds faster. This isn’t just about saving time; it’s about smoother, more efficient interactions between a user and the model, improving the overall user experience.

What are Fine-Tuning Use Cases?

Customizing Style, Tone, and Format: Whether you need a formal report, a casual blog post, or creative fiction, fine-tuning adjusts the AI’s output to match your desired style and format, ensuring consistency across all your content.

Enhancing Reliability for Outputs: Fine-tuning makes the AI more reliable in producing the specific results you’re after, reducing the guesswork and increasing your confidence in its outputs.

Correcting Complex Prompt Failures: When in-context learning is exhausted and the AI struggles with complex instructions, fine-tuning helps the model better understand and execute complicated tasks, minimizing errors.

Managing Edge Cases: Fine-tuning allows the AI to handle unique or unusual scenarios in ways that standard models can’t, ensuring comprehensive coverage of your needs.

Learning New Skills and Tasks: When introducing a new skill or task that’s hard to articulate in a simple prompt, fine-tuning is like providing a crash course to an LLM.

How to Fine-tune GPT-3.5 Turbo Step by Step

Prepare and Upload Training Data

The first step is collecting and organizing the data that mirrors your specific objectives in a fine-tuning dataset. This preparation involves selecting texts, conversations, or any content that best represents the output you desire from the AI. Whether it’s for customer service responses, creative writing, or technical advice, the dataset should encompass a wide variety of examples to cover the scope of your needs. OpenAI requires this data to be in JSON format and structured in a system, user, assistant format.

The dataset can be submitted via the OpenAI API or via platforms like FinetuneDB, that enable you to create datasets in a no-code environment and directly integrate with OpenAI for ease of use.

Dataset Management

Fine-tuning dataset management with FinetuneDB

Training and costs

Once your dataset is prepared and uploaded, the next phase is training your model. This process is managed by OpenAI and can take anywhere from 10 minutes for a small dataset of 10 examples, to several hours or even days, depending on the complexity and size of your dataset.

Cost depends heavily on the size of your dataset, learn more about the costs of fine-tuning GPT3.5 Turbo in our pricing guide.

Testing and Evaluating Results

After the training phase, it’s important to assess how well the model has adapted to your needs. Evaluation might involve testing the AI with prompts similar to real-world scenarios it will encounter. This testing phase helps identify whether the model’s responses align with your expectations, or if further adjustments are needed. Learn more about LLM output evaluation.

Testing and Evaluating

Testing by comparing standard vs. fine-tuned model in FinetuneDB Studio

Fine-Tuned Model in Production

With a thoroughly tested and fine-tuned model, you’re now ready to deploy it. Integrating the model into your workflow allows you to leverage its fine-tuned capabilities, whether for generating content that matches your brand’s voice, automating customer interactions, or providing specific kinds of information or entertainment. Monitoring LLMs in production is important to get visibility on usage and to leverage logs for further fine-tuning.

Case Study: AI LinkedIn Writer - GPT3.5 Turbo Customized

To illustrate what can be done with a personalized AI application, we developed the AI LinkedIn Writer, a tool designed to assist LinkedIn users in creating posts in their own voice. This is done by fine-tuning GPT-3.5 Turbo with the user’s previous posts to adjust the model to each user’s writing style.

How the AI LinkedIn Writer Works

Sign Up: Users connect their LinkedIn account, granting access to their posts.

Fine-tuning: The model is fine-tuned on the user’s LinkedIn posts.

User Interface: A simple interface allows users to interact with their personalized AI.

Writer Home

AI LinkedIn Writer UI

Fine-tuning AI LinkedIn Writer Step-by-Step Workflow

Data Collection

The first step involves collecting data that the model will learn from. For the LinkedIn AI Writer, this means connecting to the user’s LinkedIn account to access their posts. The tool identifies posts that have gained significant engagement, using likes, shares, and comments as metrics. This selective process ensures that the model learns from content that has already proven to resonate with the user’s network.

Sign up via LinkedIn

Sign up via LinkedIn to retrieve user’s posts.

Data Evaluation and Transformation

Once the data is collected, it must be transformed into a format suitable for training the AI model. This involves converting the raw LinkedIn posts into a structured dataset. During this phase, it’s crucial to capture not just the text of the posts but also contextual information like engagement metrics, which can help the model understand what makes a post successful. This also involves the transformation of raw text (posts) into the system, user, assistant format, and reverse engineering the input, so the model output is a LinkedIn post. Handling a high number of datasets quickly becomes challenging, so we used the FinetuneDB infrastructure to streamline this entire process.

Dataset Management Workflow

Data transformation and dataset management using FinetuneDB workflows.

Fine-Tuning GPT-3.5 Turbo LinkedIn model

With the dataset prepared, the next step is to fine-tune the GPT-3.5 Turbo model using this specialized data via the OpenAI API. This process adjusts the model’s parameters so it becomes an expert at generating LinkedIn posts that mimic the style and substance of the user’s most engaging content.

Testing and Deploying LinkedIn Writer Model

After the model has been fine-tuned and the LLM output evaluated with input variations, it’s ready for use.

Users get access and can prompt the LinkedIn AI Writer with topics and ideas for new posts. The model generates posts based on the user’s historical content patterns and engagement successes. This personalized approach helps maintain a consistent and authentic voice across posts.

AI LinkedIn Writer live

AI LinkedIn Writer in production

Fine-Tuning Challenges: Raw Data to Fine-tuning Datasets

Gathering and preparing the right kind of data remains a challenge. Usually, data is available, but it’s unstructured. Transforming raw data into a clean, structured format that an AI can learn from is a complex step that requires significant effort and expertise.

FinetuneDB addresses this challenge, streamlining the process of fine-tuning dataset management and data transformation. If you want to learn more about creating custom LLMs for your use case from unstructured data, reach out to us.

Frequently Asked Questions

What is fine-tuning in AI?

Fine-tuning in artificial intelligence is a process used to adapt a pre-trained model, like GPT-3.5 Turbo, to perform specific tasks or understand particular domains better. This involves training the model on a new dataset that is smaller but more focused on the task at hand, enhancing the model’s accuracy and efficiency for customized applications.

Why is fine-tuning important for LLMs like GPT-3.5 Turbo?

Fine-tuning is crucial for large language models (LLMs) because it allows them to go beyond their general capabilities and provide more tailored, high-quality responses. This customization makes LLMs far more valuable for specific applications, from content creation to automated customer service, ensuring they meet the unique needs of businesses and users.

Can anyone fine-tune a GPT-3.5 Turbo model?

Yes, with access to the right tools and resources, anyone can fine-tune a GPT-3.5 Turbo model. However, the process requires a basic understanding of machine learning principles, the ability to curate a relevant training dataset, and familiarity with the OpenAI API or platforms that facilitate fine-tuning, such as FinetuneDB.

How long does it take to fine-tune GPT-3.5 Turbo?

The time required to fine-tune GPT-3.5 Turbo depends on the size and complexity of the training dataset and the specific requirements of the task. Typically, it can range from a few minutes with 10+ examples to several hours with a 100+ examples, and even days with massive datasets of 10k+. Platforms like OpenAI offer guidance on optimization to reduce training time without compromising model performance.

What are examples of fine-tuned GPT-3.5 Turbo applications?

Fine-tuned GPT-3.5 Turbo applications are diverse and include personalized content generation, targeted customer support chatbots, language translation services tailored to specific industries, and educational tools designed to mimic a certain teaching style. Each application benefits from the model’s ability to understand and produce content that aligns closely with user expectations.

How does fine-tuning improve performance?

Fine-tuning improves AI model performance by optimizing the model’s parameters for a specific set of tasks or data, resulting in more accurate, relevant, and context-aware responses. This targeted training process enables the model to better understand the nuances of the data it processes, significantly enhancing its effectiveness for specialized applications.

Can fine-tuning GPT-3.5 Turbo improve content creation?

Absolutely. Fine-tuning GPT-3.5 Turbo can significantly improve content creation by customizing the model to produce content that aligns with a specific tone, style, or format. Whether for blogs, reports, or creative writing, fine-tuning ensures consistency and relevance in the content generated.

What are the steps to fine-tune GPT-3.5 Turbo?

The steps include preparing a targeted dataset that reflects the desired output, training the model on this dataset to adjust its understanding and generation capabilities, evaluating its performance and making necessary adjustments, and finally, deploying the fine-tuned model for practical use in custom applications.

Where can I find resources for fine-tuning GPT-3.5 Turbo?

Resources for fine-tuning GPT-3.5 Turbo models are available on the OpenAI website, including detailed documentation and tutorials. Additionally, platforms like FinetuneDB offer tools and services designed to simplify the fine-tuning process, making it accessible even for those with limited technical expertise.