Enhancing Your GPT-2 Project with Modern LLMs: A Step-by-Step Guide

Introduction

In this article, we will transform a basic GPT-2 project into a sophisticated large language model (LLM) setup. We will cover advanced data preprocessing, model fine-tuning, and integration of modern LLMs like GPT-3 or open-source alternatives. By the end, you’ll have a comprehensive understanding of how to enhance your language model project.

Project Structure

my_llm_projects

To organize our project efficiently, we will use the following structure:

my_llm_project/
├── data/
│   ├── raw/
│   ├── processed/
│   └── datasets/
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   ├── evaluation.ipynb
│   └── inference.ipynb
├── models/
│   ├── gpt2/
│   ├── gpt3/
│   ├── gpt_neo/
│   └── fine_tuned_models/
├── scripts/
│   ├── preprocess.py
│   ├── train.py
│   ├── evaluate.py
│   └── inference.py
├── utils/
│   ├── data_utils.py
│   ├── model_utils.py
│   └── train_utils.py
├── web/
│   ├── app.py
│   ├── templates/
│   └── static/
├── requirements.txt
├── README.md
└── .gitignore

Data Preprocessing

Data preprocessing is crucial for model performance. We’ll create a data_utils.py script to handle loading, cleaning, and splitting our data.

data_utils.py

import pandas as pd
from sklearn.model_selection import train_test_split
import re

def load_data(file_path):
    return pd.read_csv(file_path)

def preprocess_data(df, text_column):
    df[text_column] = df[text_column].fillna('')
    df[text_column] = df[text_column].apply(clean_text)
    return df

def clean_text(text):
    text = text.lower()
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def split_data(df, test_size=0.2):
    return train_test_split(df, test_size=test_size)

def save_processed_data(df, file_path):
    df.to_csv(file_path, index=False)

preprocess.py

This script uses data_utils.py to preprocess the dataset.

import argparse
import os
from utils.data_utils import load_data, preprocess_data, split_data, save_processed_data

def main(input_file, output_dir, text_column):
    df = load_data(input_file)
    df = preprocess_data(df, text_column)
    train_df, test_df = split_data(df)
    train_file_path = os.path.join(output_dir, 'train.csv')
    test_file_path = os.path.join(output_dir, 'test.csv')
    save_processed_data(train_df, train_file_path)
    save_processed_data(test_df, test_file_path)
    print(f"Data preprocessing completed. Train data saved to {train_file_path}, Test data saved to {test_file_path}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Preprocess data for model training")
    parser.add_argument('--input_file', type=str, required=True, help="Path to the input CSV file")
    parser.add_argument('--output_dir', type=str, required=True, help="Directory to save the processed data")
    parser.add_argument('--text_column', type=str, required=True, help="Name of the column containing text data")

    args = parser.parse_args()
    main(args.input_file, args.output_dir, args.text_column)

Data Preprocessing Notebook

An interactive notebook for preprocessing data.

# Title: Data Preprocessing for LLM Training

# 1. Introduction
"""
This notebook demonstrates the data preprocessing steps required for training a large language model.
"""

# 2. Import Libraries
import pandas as pd
from utils.data_utils import load_data, preprocess_data, split_data, save_processed_data

# 3. Load Data
input_file = 'data/raw/dataset.csv'
df = load_data(input_file)
df.head()

# 4. Preprocess Data
text_column = 'text'
df = preprocess_data(df, text_column)
df.head()

# 5. Split Data
train_df, test_df = split_data(df, test_size=0.2)
print(f"Training data shape: {train_df.shape}")
print(f"Test data shape: {test_df.shape}")

# 6. Save Processed Data
output_dir = 'data/processed/'
train_file_path = f'{output_dir}train.csv'
test_file_path = f'{output_dir}test.csv'
save_processed_data(train_df, train_file_path)
save_processed_data(test_df, test_file_path)
print(f"Processed data saved to {train_file_path} and {test_file_path}")

Model Training

For model training, we will utilize Hugging Face’s Transformers library. Below is an example of how to set up your train.py script.

train.py

import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

def main(model_name, train_file, output_dir, epochs, batch_size):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    
    dataset = load_dataset('csv', data_files=train_file)['train']
    dataset = dataset.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True)
    
    training_args = TrainingArguments(
        output_dir=output_dir,
        num_train_epochs=epochs,
        per_device_train_batch_size=batch_size,
        save_steps=10_000,
        save_total_limit=2,
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
    )
    
    trainer.train()

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_name', type=str, required=True)
    parser.add_argument('--train_file', type=str, required=True)
    parser.add_argument('--output_dir', type=str, required=True)
    parser.add_argument('--epochs', type=int, default=3)
    parser.add_argument('--batch_size', type=int, default=4)
    
    args = parser.parse_args()
    main(args.model_name, args.train_file, args.output_dir, args.epochs, args.batch_size)

Web Interface

A simple web interface using Flask for interacting with your model.

app.py

from flask import Flask, request, render_template, jsonify
import openai

app = Flask(__name__)
openai.api_key = 'your-api-key'

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/generate', methods=['POST'])
def generate_text():
    prompt = request.form['prompt']
    response = openai.Completion.create(
        model="text-davinci-003",
        prompt=prompt,
        max_tokens=100
    )
    generated_text = response.choices[0].text.strip()
    return jsonify({'generated_text': generated_text})

if __name__ == '__main__':
    app.run(debug=True)

Conclusion

Enhancing your GPT-2 project with modern LLMs involves careful data preprocessing, fine-tuning advanced models, and creating an interactive web interface. This guide has provided a detailed roadmap for these improvements, helping you build a robust and scalable language model application.

For continued learning and staying updated with the latest in AI and machine learning, consider following Medium publications and authors that specialize in these fields. Engaging with the community through comments and discussions can provide valuable insights and inspiration for your projects. Happy coding!