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 huma

Apr 28, 2023

Introduction:

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.

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Prerequisites:

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

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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:

import pandas as pd
import numpy as np

# Read the Excel file
data = pd.read_excel('your_large_excel_file.xlsx')

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:

from sklearn.model_selection import train_test_split
train_data, val_data = train_test_split(data, test_size=0.2, random_state=42)

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:

!pip install openai
import openai
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments
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2.2. Load the pre-trained model and tokenizer:


MODEL_NAME = 'gpt-4'
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_NAME)
model = GPT2LMHeadModel.from_pretrained(MODEL_NAME)

2.3. Prepare the dataset for training:


train_dataset = TextDataset(tokenizer=tokenizer, file_path='train_data.txt', block_size=128)
val_dataset = TextDataset(tokenizer=tokenizer, file_path='val_data.txt', block_size=128)

data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

2.4. Fine-tune the model:


training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    eval_steps=100,
    save_steps=100,
    warmup_steps=10,
    prediction_loss_only=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

trainer.train()

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Step 3: Generating Responses to Business Prompts

3.1. Define a function to generate responses:

def generate_response(prompt, max_length=150, num_responses=1):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')

output = model.generate(
					input_ids,
					max_length=max_length,
					num_return_sequences=num_responses,
					no_repeat_ngram_size=2,
					temperature=0.7,
					top_k=50,
					top_p=0.95,
					)
decoded_output = [tokenizer.decode(response, skip_special_tokens=True) for response in output]
return decoded_output

3.2. Test your model with a business prompt:


prompt = "What are some strategies for effective marketing in the technology industry?"
responses = generate_response(prompt, num_responses=3)

for i, response in enumerate(responses):
    print(f"Response {i+1}: {response}\n")

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Conclusion:

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:

OpenAI's official documentation: https://beta.openai.com/docs/
Hugging Face's Transformers library: https://huggingface.co/transformers/
Fine-tuning GPT-2 for text generation: https://huggingface.co/blog/how-to-generate

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