Build Your Own GPT Text Generator
Table of Contents
- Introduction
- Building a Generative Pre-trained Transformer
- Setting Up the Environment
- Downloading the Model and Tokenizer
- Loading and Preprocessing the Data
- Building and Training the Model
- Evaluating the Model's Performance
- Generating Text with the Trained Model
- Conclusion
- Future Directions
Building a Generative Pre-trained Transformer
Welcome to this series of three videos on building your own generative pre-trained Transformers (GPTS). In this video, we will set ourselves the ambitious goal of building and then training a functioning GPT model in around 24 hours, using modest hardware - a 2020 MacBook Pro with only eight gigabytes of RAM. You will see that we can achieve incredibly impressive results in such a short amount of time and on modest hardware.
The purpose of this video series is to give You practical experience in developing a language model, guiding you through the same steps you would follow in an industrial setting where you'd have access to more suitable hardware than a single notebook computer.
In the next video, we'll discuss how to customize your model to take AdVantage of more compute power and how to build instrumentation to monitor your model's learning progress. This will allow you to experiment with different model hyperparameters for optimal deployment on more powerful hardware.
Throughout this series, we'll occasionally examine Python code mainly to demonstrate the simplicity of using libraries like Hugging Face and PyTorch to build these models. However, don't worry if you're not a programmer - this is not a Python coding tutorial. If you are interested in deep learning Python tutorials, please consider joining Lucidate as a member to gain access to members-only videos covering the practical aspects of building AI using tools like PyTorch and TensorFlow.
In the final video in this series of three, we'll move beyond the limitations of a single computer and explore what it takes to build an industrial-Scale generative pre-trained Transformer. As Lucidate is primarily a capital markets consulting company, we'll use Bloomberg GPT as our prototype and examine the requirements for building a 50 billion parameter model. While you may not be able to train such a model without access to large-scale computing resources, you will learn about the design decisions faced by developers of some of the world's largest and most successful AI models.
For the remainder of this video, we'll cover the steps of bringing a GPT model to life, loading data and splitting it into training and validation sets, tokenizing the training corpus, building and training the model, and finally testing our trained model.
Setting Up the Environment
Before we begin, we need to set up our environment. We'll be using Python and several libraries, including Hugging Face and PyTorch. If you don't have these installed, you can follow the instructions on their respective websites to install them.
Downloading the Model and Tokenizer
We'll be using the small GPT2 decoder-only model from Hugging Face and the GPT2 tokenizer from the Hugging Face library. This is a subword tokenizer. Subword tokenization is advantageous because it strikes a balance between character-level and word-level tokenization, enabling the model to handle rare words and out-of-vocabulary tokens more effectively.
We'll instantiate our model with the configuration parameters we've downloaded from Hugging Face. Next, we'll download the WikiText-2 data set, a collection of Wikipedia articles. We can also get this from Hugging Face, which has already split the data into training and validation sets for us. The training set will be used to train the model, while the validation set will evaluate the model's training performance.
The TextDataset class is very helpful here. It reads in the text file, tokenizes it using the provided tokenizer, and processes the data into a format suitable for training a language model. You'll see later that we pass the tokens into a data collator class. The TextDataset class understands the format that the collator wants, so we don't have to worry about formatting.
Loading and Preprocessing the Data
Next, we take our tokens and turn them into tensors that can be passed into our model. Here we use the DataCollatorForLanguageModeling class. This class can take our tokenized vocabulary and produce the tensors in the format that the model requires. Once again, the heavy lifting is done for us. We don't need to understand the details of the format of tensors that the model requires.
GPT2 is a decoder-only model. It doesn't use masked language modeling, and therefore we set the MLM flag to false.
Building and Training the Model
With our tokenized data, we can now build and train a Transformer model. We'll use the GPT2 architecture, a decoder-only model designed for text generation. GPT2 features a multi-layer Transformer architecture consisting of self-Attention layers, positional encoding, and layer normalization. In this example, we're using a small version of GPT2, but you can scale up to larger models if you have more computational resources.
When training the model, hyperparameters like the learning rate, batch size, and the number of training epochs play a crucial role in determining the model's performance. Experimenting with these hyperparameters can help you optimize your model for best results. Don't be afraid to fiddle with the dials. We'll Delve deeper into this topic in the next video.
We pass the model the tokenized data, the collated tensors, and the training parameters to a training class, and once again, Hugging Face takes care of all of the coordination. We send a message to the training class to train the model, and once that's done, we can retrieve the results from the trainer object with the evaluation function.
Evaluating the Model's Performance
On my hardware and with these parameters, training takes around 24 hours. Once complete, we can compute the model's perplexity on the validation set. Perplexity is a measure of how well the model can predict the next token in the sequence. Lower perplexity values indicate better performance. In the next video, we'll look at further measures beyond perplexity.
Generating Text with the Trained Model
Finally, we'll use the trained model to generate text Based on user-provided input. This will demonstrate how our model can be used for creative text generation tasks.
So how does it work? Frankly, not at all bad. Now let's be clear - with only 24 hours training on such modest hardware, you shouldn't expect to be competing with OpenAI's chat GPT. But the results are grammatically correct and reasonably coherent. If you play around with some of these hyperparameters, you'll get dramatically varied results. Generally speaking, with low numbers, you tend to get more predictable, somewhat vanilla output. But if you crank these numbers up, you can get some wildly unpredictable and frankly flamboyant responses.
Conclusion
In this video, we've shown you how to build a hobby-scale Transformer for text generation using the Hugging Face library and PyTorch. To Recap without the code, the Hugging Face libraries do a lot of the heavy lifting for us. We can get a default model configuration and an off-the-shelf tokenizer. We can retrieve a blank GPT Transformer architecture and configure it with a standard config. There are even some cleaned data sets to use, which we can tokenize and then repurpose the tokenized data into tensors suitable to train the model. We can provide some training parameters and then train and test the model.
Future Directions
In future videos, we'll explore how to use these principles to Create industrial-scale Transformers that require more data and computational resources. Stay tuned and don't forget to like, comment, and subscribe or hit the join button to explore Lucidate's membership tiers for more tutorials on Transformers and machine learning.
Highlights
- Generative pre-trained Transformers (GPTs) have become a cornerstone in the field of AI, enabling impressive results in various natural language processing tasks.
- In this video series, we aim to give you practical experience in developing a language model, guiding you through the same steps you would follow in an industrial setting.
- We use the Hugging Face library and PyTorch to build a hobby-scale Transformer for text generation.
- We Show you how to download the model and tokenizer, load and preprocess the data, build and train the model, and evaluate its performance.
- We also demonstrate how to generate text with the trained model.
- In future videos, we'll explore how to create industrial-scale Transformers that require more data and computational resources.
FAQ
Q: What is a GPT model?
A: A GPT (Generative Pre-trained Transformer) model is a type of language model that uses deep learning to generate human-like text.
Q: What is the purpose of this video series?
A: The purpose of this video series is to give you practical experience in developing a language model, guiding you through the same steps you would follow in an industrial setting.
Q: What is perplexity?
A: Perplexity is a measure of how well the model can predict the next token in the sequence. Lower perplexity values indicate better performance.
Q: Can I use the Hugging Face library and PyTorch to build my own language model?
A: Yes, you can use the Hugging Face library and PyTorch to build your own language model. This video series will guide you through the process.
Q: What are some future directions for this video series?
A: In future videos, we'll explore how to create industrial-scale Transformers that require more data and computational resources.
