Build Your Own Large Language model (LLM) Model with OpenAI using Microsoft Excel file

Discover how to build a custom LLM model using OpenAI and a large Excel dataset for tailored business responses. This guide covers dataset preparation, fine-tuning an OpenAI model, and generating human-like responses to business prompts. Boost productivity with a powerful tool for content generation, customer support, and data analysis.


Jatin Solanki

Updated on

January 10, 2024


In recent years, large language models (LLMs) like OpenAI's GPT series have revolutionized the field of natural language processing (NLP). These models are capable of generating human-like responses to a variety of prompts, making them a valuable asset for businesses. In this article, we'll guide you through the process of building your own LLM model using OpenAI, a large Excel file, and share sample code and illustrations to help you along the way. By the end, you'll have a solid understanding of how to create a custom LLM model that caters to your specific business needs.


  1. Python programming knowledge
  2. Familiarity with NLP concepts
  3. Access to OpenAI API
  4. A large Excel file containing the dataset you want to train your model on

Step 1: Preparing the Dataset

Before we can train our model, we need to prepare the data in a format suitable for training. This involves the following steps:

1.1. Import the necessary libraries and read the Excel file:

1.2. Clean and preprocess the data:

  • Remove any unnecessary columns
  • Fill missing values or drop rows with missing data
  • Convert text data to lowercase
  • Tokenize text and remove stop words

1.3. Split the dataset into training and validation sets:


Step 2: Fine-tuning the OpenAI Model

In this step, we'll fine-tune a pre-trained OpenAI model on our dataset.

2.1. Install the OpenAI library and import necessary modules:

2.2. Load the pre-trained model and tokenizer:

2.3. Prepare the dataset for training:

2.4. Fine-tune the model:

Step 3: Generating Responses to Business Prompts

3.1. Define a function to generate responses:

3.2. Test your model with a business prompt:


In this article, we've demonstrated how to build a custom LLM model using OpenAI and a large Excel dataset. We walked you through the steps of preparing the dataset, fine-tuning the model, and generating responses to business prompts. By following this tutorial, you can create your own LLM model tailored to the specific needs of your business, making it a powerful tool for tasks like content generation, customer support, and data analysis.

For further reading, we recommend exploring the following resources:

  1. OpenAI's official documentation:
  2. Hugging Face's Transformers library:
  3. Fine-tuning GPT-2 for text generation:

Table of Contents

Read other blog articles

Grow with our latest insights

Sneak peek from the data world.

Thank you! Your submission has been received!
Talk to a designer

All in one place

Comprehensive and centralized solution for data governance, and observability.

decube all in one image