{"id":103338,"date":"2025-02-22T12:23:36","date_gmt":"2025-02-22T12:23:36","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/fine-tuning-a-model-on-openai-platform-for-customer-support\/"},"modified":"2025-02-22T12:23:36","modified_gmt":"2025-02-22T12:23:36","slug":"fine-tuning-a-model-on-openai-platform-for-customer-support","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=103338","title":{"rendered":"Fine-Tuning A Model on OpenAI Platform for Customer Support"},"content":{"rendered":"


\n<\/p>\n

\n

Fine-tuning large language models<\/a> (LLMs) is essential for optimizing their performance in specific tasks. OpenAI provides a robust framework for fine-tuning GPT models, allowing organizations to tailor AI behavior based on domain-specific requirements. This process plays a crucial role in LLM customization, enabling models to generate more accurate, relevant, and context-aware responses.
Fine-tuned LLMs can be applied in various scenarios such as financial analysis<\/a> for risk assessment, customer support for personalized responses, and medical research for aiding diagnostics. They can also be used in software development for code generation<\/a> and debugging, and legal assistance for contract review and case law analysis. In this guide, we\u2019ll walk through the fine-tuning process using OpenAI\u2019s platform and evaluate the fine-tuned model\u2019s performance in real-world applications.<\/p>\n

What is OpenAI Platform?<\/h2>\n

The OpenAI platform provides a web-based tool that makes it easy to fine-tune models, letting users customize them for specific tasks. It provides step-by-step instructions for preparing data, training models, and evaluating results. Additionally, the platform supports seamless integration with APIs, enabling users to deploy fine-tuned models quickly and efficiently. It also offers automatic versioning and model monitoring to ensure that models are performing optimally over time, with the ability to update them as new data becomes available.<\/p>\n

Cost of Inference<\/h3>\n

Here\u2019s how much it costs to train models on the OpenAI Platform.<\/p>\n

\n\n\n\n\n\n\n\n
Model<\/strong><\/td>\nPricing<\/strong><\/td>\nPricing with Batch API<\/strong><\/td>\nTraining Pricing<\/strong><\/td>\n<\/tr>\n
gpt-4o-2024-08-06<\/strong><\/td>\n$3.750 \/ 1M input tokens$15.000 \/ 1M output tokens<\/td>\n$1.875 \/ 1M input tokens$7.500 \/ 1M output tokens<\/td>\n$25.000 \/ 1M training\u00a0 tokens<\/td>\n<\/tr>\n
gpt-4o-mini-2024-07-18<\/strong><\/td>\n$0.300 \/ 1M input tokens$1.200 \/ 1M output tokens<\/td>\n$0.150 \/ 1M input tokens$0.600 \/ 1M output tokens<\/td>\n$3.000 \/ 1M training tokens<\/td>\n<\/tr>\n
gpt-3.5-turbo<\/strong><\/td>\n$3.000 \/ 1M training tokens$6.000 \/ 1M output tokens<\/td>\n$1.500 \/ 1M input tokens$3.000 \/ 1M output tokens<\/td>\n$8.000 \/ 1M training tokens<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

For more information, visit this page: https:\/\/openai.com\/api\/pricing\/<\/a><\/p>\n

Fine Tuning a Model on OpenAI Platform<\/h2>\n

Fine-tuning a model allows users to customize models for specific use cases, improving their accuracy, relevance, and adaptability. In this guide, we focus on more personalized, accurate, and context-aware responses to customer service interactions.<\/p>\n

By fine tuning a model on real customer queries and interactions, the businesses can enhance response quality, reduce misunderstandings, and improve overall user satisfaction.<\/p>\n

Also Read: Beginner\u2019s Guide to Finetuning Large Language Models (LLMs)<\/a><\/em><\/p>\n

Now let\u2019s see how we can train a model using the OpenAI Platform. We will do this in 4 steps:<\/p>\n

    \n
  1. Identifying the dataset<\/li>\n
  2. Downloading the dfinetuning data<\/li>\n
  3. Importing and Preprocessing the Data<\/li>\n
  4. Fine-tuning on OpenAI Platform<\/li>\n<\/ol>\n

    Let\u2019s begin!<\/p>\n

    Step 1: Identifying the Dataset<\/h3>\n

    To fine-tune the model, we first need a high-quality dataset tailored to our use case. For this fine tuning process, I downloaded the dataset from Hugging Face, a popular platform for AI datasets and models. You can find a wide range of datasets suitable for fine-tuning by visiting Hugging Face Datasets<\/a>. Simply search for a relevant dataset, download it, and preprocess it as needed to ensure it aligns with your specific requirements.<\/p>\n

    Step 2: Downloading the Dataset for Finetuning<\/h3>\n

    The customer service data for the fine tuning process is taken from Hugging Face datasets. You can access it from here<\/a>.<\/p>\n

    LLMs need data to be in a specific format for fine-tuning. Here\u2019s a sample format for GPT-4o<\/a>, GPT-4o-mini<\/a>, and GPT-3.5-turbo<\/a>.<\/p>\n

    {\"messages\": [{\"role\": \"system\", \"content\": \"This is an AI assistant for answering FAQs.\"}, {\"role\": \"user\", \"content\": \"What are your customer support hours?\"}, {\"role\": \"assistant\", \"content\": \"Our customer support is available\t1 24\/7. How else may I assist you?\"}]}<\/code><\/pre>\n
    Now in the next step we will check what our data looks like and make the necessary adjustments if it is not in the required format.<\/p>\n
    \"sample<\/figure>\n
    Step 3: Importing and Preprocessing the Data<\/h3>\n
    Now we will import the data and preprocess to to the required format.<\/p>\n
    To do this we will follow these steps:<\/p>\n
    1. Now we will load the data in the Jupyter Notebook and modify it to match the required format.<\/p>\n
    import pandas as pd\nsplits = {'train': 'data\/train-00000-of-00001.parquet', 'test': 'data\/test-00000-of-00001.parquet'}\ndf_train = pd.read_parquet(\"hf:\/\/datasets\/charles828\/vertex-ai-customer-support-training-dataset\/\" + splits[\"train\"])<\/code><\/pre>\n
    \"sample<\/figure>\n
    Here we have 6 different columns. But we need only need two \u2013\u00a0 \u201cinstruction\u201d and \u201cresponse\u201d as these are the columns that have customer queries and the relative responses in them.<\/p>\n
    Now we can use the above csv file to create a jsonl file as needed for fine-tuning.<\/p>\n
    import json\nmessages = pd.read_csv(\"training_data\")\nwith open(\"query_dataset.jsonl\", \"w\", encoding='utf-8') as jsonl_file:\n   for _, row in messages.iterrows():\n       user_content = row['instruction']\n       assintant_content = row['response']      \n       jsonl_entry = {\n           \"messages\":[\n               {\"role\": \"system\", \"content\": \"You are an assistant who writes in a clear, informative, and engaging style.\"},\n               {\"role\": \"user\", \"content\": user_content},\n               {\"role\": \"assistant\", \"content\": assintant_content}\n           ]\n       }    \n       jsonl_file.write(json.dumps(jsonl_entry) + '\\n')<\/code><\/pre>\n
    As shown above, we can iterate through the data frame to create the jsonl file.<\/p>\n
    Here we are storing our data in a jsonl file format which is slightly different from json.<\/p>\n
    json<\/b> stores data as a hierarchical structure (objects and arrays) in a single file, making it suitable for structured data with nesting. Below is an example of the json file format.<\/p>\n
    {\n \"users\": [\n   {\"name\": \"Alice\", \"age\": 25},\n   {\"name\": \"Bob\", \"age\": 30}\n ]}<\/code><\/pre>\n
    jsonl <\/b>consists of multiple json objects, each on a separate line, without arrays or nested structures. This format is more efficient for streaming, processing large datasets, and handling data line by line.Below is an example of the jsonl file format.<\/b><\/p>\n
    {\"name\": \"Alice\", \"age\": 25}\n{\"name\": \"Bob\", \"age\": 30}<\/code><\/pre>\n
    Step 4: Fine-tuning on OpenAI Platform<\/h3>\n
    Now, we will use this \u2018query_dataset\u2019 to fine-tune the GPT-4o LLM. To do this, follow the below steps.<\/p>\n
    1. Go to this website<\/a> and sign in if you haven\u2019t signed in already. Once logged in, click on \u201cLearn more<\/a>\u201d to learn more about the fine-tuning process.<\/p>\n
    \"Fine-Tuning<\/figure>\n
    2. Click on \u2018Create\u2019 and a small window will pop up.<\/p>\n
    \"Creating<\/figure>\n
    \"OpenAI<\/figure>\n
    Here is a breakdown of the hyperparameters in the above image:<\/p>\n
    Batch Size:<\/b> This refers to the number of training examples (data points) used in one pass (or step) before updating the model\u2019s weights. Instead of processing all data at once, the model processes small chunks (batches) at a time. A smaller batch size will take more time but may create better models. You have to find right balance over here. While a larger one might be more stable but much faster.<\/p>\n
    Learning Rate Multiplier:<\/b>\u00a0This is a factor that adjusts how much the model\u2019s weights change after each update. If it\u2019s set high, the model might learn faster but could overshoot the best solution. If it\u2019s low, the model will learn more slowly but might be more precise.<\/p>\n
    Number of Epochs:<\/b> An \u201cepoch\u201d is one complete pass through the entire training dataset. The number of epochs tells you how many times the model will learn from the entire dataset. More epochs typically allow the model to learn better, but too many can lead to overfitting.<\/p>\n
    3. Select the method as \u2018Supervised\u2019 and the \u2018Base Model\u2019 of your choice. I have selected GPT-4o.<\/p>\n
    \"OpenAI<\/figure>\n
    4. Upload the json file for the training data.<\/p>\n
    5. Add a \u2018Suffix\u2019 relevant to the task on which you want to fine-tune the model.<\/p>\n
    6. Choose the hyper-parameters or leave them to the default values.<\/p>\n
    7. Now click on \u2018Create\u2019 and the fine-tuning will start.<\/p>\n
    8. Once the fine-tuning is completed it will show as follows:<\/p>\n
    \"Fine-tuned<\/figure>\n
    9. Now we can compare the fine-tuned model with the pre-existing model by clicking on the \u2018Playground\u2019 in the bottom right corner.<\/p>\n
    Important Note:<\/h4>\n
    Fine-tuning duration and cost depend on the dataset size and model complexity. A smaller dataset, like 100 samples, costs significantly less but may not fine tune the model sufficiently, while larger datasets require more resources in terms of both time and money. In my case, the dataset had approximately 24K samples, so fine-tuning took around 7 to 8 hours and costed approximately $700.<\/p>\n
    Caution<\/strong><\/p>\n
    Given the high cost, it\u2019s recommended to start with a smaller dataset for initial testing before scaling up. Ensuring the dataset is well-structured and relevant can help optimize both performance and cost efficiency.<\/p>\n
    GPT-4o vs Finetuned GPT-4o Performance Check<\/h2>\n
    Now that we have fine-tuned the model, we\u2019ll compare its performance with the base GPT-4o and analyze responses from both models to see if there are improvements in accuracy, clarity, understanding, and relevance. This will help us determine if the fine-tuned model meets our specific needs and performs better in the intended tasks. For brevity i am showing you sample results of 3 prompts form both the fine tunned and standard GPT-4o model.<\/p>\n
    Query 1<\/h3>\n
    Query:<\/strong> \u201cHelp me submitting the new delivery address\u201d<\/em><\/p>\n
    Response by finetuned GPT-4o model:<\/strong><\/p>\n
    \"Fine-Tuning<\/figure>\n
    Response by GPT-4o:<\/strong><\/p>\n
    \"GPT-4o<\/figure>\n
    Comparative Analysis<\/h4>\n
    The fine-tuned model delivers a more detailed and user-centric response compared to the standard GPT-4o. While GPT-4o provides a functional step-by-step guide, the fine-tuned model enhances clarity by explicitly differentiating between adding and editing an address. It is more engaging and reassuring to the user and offers proactive assistance. This demonstrates the fine-tuned model\u2019s superior ability to align with customer service best practices. The fine-tuned model is therefore a stronger choice for tasks requiring user-friendly, structured, and supportive responses.<\/p>\n
    Query 2<\/h3>\n
    Query: <\/strong>\u201cI need assistance to change to the Account Category account\u201d<\/em><\/p>\n
    Response by finetuned GPT-4o model:<\/strong><\/p>\n
    \"Fine-Tuning<\/figure>\n
    Response by GPT-4o:<\/strong><\/p>\n
    \"GPT-4o<\/figure>\n
    Comparative Analysis<\/h4>\n
    The fine-tuned model significantly enhances user engagement and clarity compared to the base model. While GPT-4o provides a structured yet generic response, the fine-tuned version adopts a more conversational and supportive tone, making interactions feel more natural.<\/p>\n
    Query 3<\/h3>\n
    Query:<\/strong> \u201ci do not know how to update my personal info\u201d<\/em><\/p>\n
    Response by finetuned GPT-4o model:<\/strong><\/p>\n
    \"Fine-Tuning<\/figure>\n
    Response by GPT-4o:<\/strong><\/p>\n
    \"GPT-4o<\/figure>\n
    Comparative Analysis<\/h4>\n
    The fine-tuned model outperforms the standard GPT-4o by providing a more precise and structured response. While GPT-4o offers a functional answer, the fine-tuned model improves clarity by explicitly addressing key distinctions and presenting information in a more coherent manner. Additionally, it adapts better to the context, ensuring a more relevant and refined response.<\/p>\n
    Overall Comparative Analysis<\/h3>\n
    \n\n\n\n\n\n\n\n\n\n\n
    Feature<\/td>\nFine-Tuned GPT-4o<\/td>\nGPT-4o (Base Model)<\/td>\n<\/tr>\n
    Empathy & Engagement<\/td>\nHigh \u2013 offers reassurance, warmth, and a personalized touch<\/td>\nLow \u2013 neutral and formal tone, lacks emotional depth<\/td>\n<\/tr>\n
    User Support & Understanding<\/td>\nStrong \u2013 makes users feel supported and valued<\/td>\nModerate \u2013 provides clear guidance but lacks emotional connection<\/td>\n<\/tr>\n
    Tone & Personalization<\/td>\nWarm and engaging<\/td>\nProfessional and neutral<\/td>\n<\/tr>\n
    Efficiency in Information Delivery<\/td>\nClear instructions with added emotional intelligence<\/td>\nHighly efficient but lacks warmth<\/td>\n<\/tr>\n
    Overall User Experience<\/td>\nMore engaging, comfortable, and memorable<\/td>\nFunctional but impersonal and transactional<\/td>\n<\/tr>\n
    Impact on Interaction Quality<\/td>\nEnhances both effectiveness and emotional resonance<\/td>\nFocuses on delivering information without emotional engagement<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
    Conclusion<\/h2>\n
    In this case fine-tuning the models to respond better to the customer queries their effectiveness . It makes interactions feel more personal, friendly, and supportive, which leads to stronger connections and higher user satisfaction. While base models provide clear and accurate information, they can feel robotic and less engaging. Fine tuning the models through OpenAI\u2019s convenient web platform is a great way to build custom large language models for domain specific tasks.<\/p>\n
    Frequently Asked Questions<\/h2>\n
    \n
    Q1. What is fine-tuning in AI models?<\/strong> <\/p>\n
    A. Fine-tuning is the process of adapting a pre-trained AI model to perform a specific task or exhibit a particular behavior by training it further on a smaller, task-specific dataset. This allows the model to better understand the nuances of the task and produce more accurate or tailored results.<\/p>\n<\/p><\/div>\n
    Q2. How does fine-tuning improve an AI model\u2019s performance?<\/strong> <\/p>\n
    A.\u00a0 Fine-tuning enhances a model\u2019s performance by teaching it to better handle the specific requirements of a task, like adding empathy in customer interactions. It helps the model provide more personalized, context-aware responses, making interactions feel more human-like and engaging.<\/p>\n<\/p><\/div>\n
    Q3. Are fine-tuned models more expensive to use?<\/strong> <\/p>\n
    A. Fine-tuning models can require additional resources and training, which may increase the cost. However, the benefits of a more effective, user-friendly model often outweigh the initial investment, particularly for tasks that involve customer interaction or complex problem-solving.<\/p>\n<\/p><\/div>\n
    Q4. Can I fine-tune a model on my own?<\/strong> <\/p>\n
    A. Yes, if you have the necessary data and technical expertise, you can fine-tune a model using machine learning frameworks like Hugging Face, OpenAI, or others. However, it typically requires a strong understanding of AI, data preparation, and training processes.<\/p>\n<\/p><\/div>\n
    Q5. How long does it take to fine-tune a model?<\/strong> <\/p>\n
    A. The time required to fine-tune a model depends on the size of the dataset, the complexity of the task, and the computational resources available. It can take anywhere from a few hours to several days or more for larger models with vast datasets.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n
    \n
    \n
    \n                                    
    \n                                                                       \"Vipin<\/p>\n
                                    <\/a>\n                                <\/div>\n<\/p><\/div>\n
    Hello! I’m Vipin, a passionate data science and machine learning enthusiast with a strong foundation in data analysis, machine learning algorithms, and programming. I have hands-on experience in building models, managing messy data, and solving real-world problems. My goal is to apply data-driven insights to create practical solutions that drive results. I’m eager to contribute my skills in a collaborative environment while continuing to learn and grow in the fields of Data Science, Machine Learning, and NLP.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"
    Fine-tuning large language models (LLMs) is essential for optimizing their performance in specific tasks. OpenAI provides a robust framework for fine-tuning GPT models, allowing organizations to tailor AI behavior based on domain-specific requirements. This process plays a crucial role in LLM customization, enabling models to generate more accurate, relevant, and context-aware responses.Fine-tuned LLMs can be […]<\/p>\n","protected":false},"author":1,"featured_media":103340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[13092,38305,1168,11438,970,952],"dealstore":[],"offerexpiration":[],"class_list":["post-103338","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-customer","tag-finetuning","tag-model","tag-openai","tag-platform","tag-support"],"yoast_head":"\nFine-Tuning A Model on OpenAI Platform for Customer Support - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=103338\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fine-Tuning A Model on OpenAI Platform for Customer Support - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Fine-tuning large language models (LLMs) is essential for optimizing their performance in specific tasks. 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