How to Train GPT3: Everything You Need to Know

“With GPT-3, businesses can tap into the AI’s potential and make smarter decisions, leading to great success.”

Have you ever wondered how with almost no human involvement, chatbots can comprehend and reply to client queries/questions? Or, how do virtual assistants like Apple Siri and Google Assistants swiftly understand and respond to human language?

The answer lies in the integration of GPT-enabled natural language processing (NLP) and state-of-the-art language models. GPT-3, a large language model, was created by OpenAI, the research laboratory.

Let’s explore the art of fine-tuning the model, to make it perform as desired. Remember, fine-tuning the language model can reduce the frequency of generating unreliable outputs from 17% to 5%.

So, without any further delay, let’s learn how to train GPT-3, unlocking the vast potential it offers to enhance AI-powered solutions.

What are Language Models (LLMs)?

Large language models are artificial-aided intelligence tools that are trained on massive amounts of knowledge-based data. This allows them to learn complex patterns and relationships between words and phrases in the language. The generic goal of larger language models is to perform probability distribution of the next word(s) in a sequence of words. 

It is to be noted that before GPT-3, specific models used machine learning algorithms to conduct specific tasks. However, if we talk about training AI algorithms for GPT-3, which already has 170+ billion parameters, it requires huge datasets.

The understanding of the datasets or where the data is retrieved to train GPT-3 will be discussed later in this blog. The common application areas of the LLMs and natural language processing are:

• Spam detection
• Machine translation
• Text summarization
• Virtual assistants 
• Speech recognition
• Sentiment classification
• Chatbots

What is GPT-3?

Generative pre-trained transformer 3 (GPT-3) has amazed the data science industry with massive trainable parameters. This AI model has been trained on massive datasets. It directly means that the model has been exposed to a vast range of data, including books, articles, websites, and audio-video visuals. 

Data scientists all over the world consider GPT-3, as the largest language model. The word “largest” here is used because the model has been trained on the vast dataset of 175 billion parameters and 499 billion tokens.

• It has a neural network machine learning model that takes input text to understand the intent and, thereby, generates natural human language text. 
• It uses both natural language generation and natural language processing to probabilistically determine what tokens from a known vocabulary will come next.

GPT-3, powered by Open AI, is a successor of their previous language model, GPT-2. This was launched in May 2020. GPT-3 is more formatted and has succeeded in providing superior performance in comparison to GPT-2. With a staggering 175 billion parameters, the full version of OpenAI’s GPT-3 is the largest language model over other language models. 

Top Highlights of GPT-3: Key Takeaways

• The GPT-3’s performance increased exponentially as and when the model size, dataset size, and the amount of computation rises.
• GPT-3 can be a general solution for performing various natural language processing tasks, even without fine-tuning the model. 
• GPT-3 uses a single model to perform a wide variety of downstream tasks with a high level of accuracy.
• GPT-3 is capable enough to perform various NLP tasks like text generation, summarisation, text classification, question-answering, translation, etc.
• The costs of best-performing language models are increasing at an unprecedented rate of at least 10 every year. This outpaces the growth of GPU’s memory.
• Resultantly, days are not far when embarrassingly parallel or traditional parallel processing methods will come to an end, and model parallelism will become indispensable. 

GPT – 3 Model Architecture

GPT-3 is a successor to the GPT-2 model, an auto-regressive transformer (decoder) that uses the same tokenization approaches as used in GPT-2. 

The main differences between GPT-2 and GPT-3 are

• Each model has additional decoder layers, and the dataset is extensive.
• The model’s layers are all parallelized, and matrix multiplication is included.
• Using a sparse transformer (like attention matrix factorization) to control memory.

There are basically eight different-size models of GPT-3, ranging from 125M to 175B. The previous record-holder, T5-11B, is successfully outperformed by the largest GPT-3 model. The size of the smallest GPT-3 model and the BERT-Base and RoBERTa-Base are roughly equivalent. 

Let’s get started understanding the basics of these models:

• All GPT-3 models employ the same attention-based architecture as the GPT-2 generation. 
• The smallest GPT-3 model with 125M includes 12x 64-dimension heads for each of the 12 attention levels. 
• The largest GPT-3 model with 175B includes 96 attention layers, each of which heads for 96 x 128.
• It was stated that all of these models were trained on V100 GPUs with high bandwidth provided by Microsoft using the same amount of tokens (300 billion tokens).

Model Name	Parameters	Layers	nheads	dheads	Batch Size	Learning Rate
GPT-3 Small	125M	12	12	64	0.5M	6.0*10-4
GPT-3 Medium	350M	24	16	64	0.5M	3.0*10-4
GPT-3 Large	760M	24	16	96	0.5M	2.5*10-4
GPT-3 XL	1.3B	24	24	128	1M	2.0*10
GPT-3 2.7B	2.7B	32	32	80	1M	1.6*10-4
GPT-3 6.7B	6.7B	32	32	118	2M	1.2*10-4
GPT-3 13B	13.0B	40	40	128	2M	1.0*10-4
GPT-3 175B	175.0B	96	96	128	3.2M	0.6*10-4

After delving into the GPT-3 AI model, explore different types of AI models as well. These include supervised learning models, unsupervised learning models, reinforcement learning models, and deep learning models.

Understanding the Basics of GPT-3 Training Data 

The size of the dataset must scale proportionally to the size of the model because neural networks are essentially compressed representations of the training data. Since the GPT 3 has 175 billion parameters with 499 tokens, here is the breakdown of the data retrieved:

Dataset	Tokens
Common Crawl	410
WebText2	19
Book1	12
Book2	55
Wikipedia	3

A large chunk of the data is gathered via a common crawl corpus that includes text extracts, metadata, and raw web page data. To prevent any duplication, some filtering methods are utilized on top of this data at the document level. Together with the enhanced WebText collection, a high-quality curated dataset is also integrated.

Training the Model

In order to train the GPT-3 model, a process similar to the one followed in training its predecessor, GPT-2 is used. Here the model predicts the next word in a sentence. Depending on the model’s size, they adjust the batch size (amount of data processed at once) and learning rate (how quickly the model learns).

The massive 175 billion-parameter GPT-3 model required an enormous amount of computing power, equivalent to 3.14 x 10^23 floating-point operations (FLOPS) for training. Even with powerful GPUs, it would take hundreds of GPU years and millions of dollars to run the training process.

Moreover, the model’s size, with 175 billion parameters, far exceeds the memory capacity of a single GPU. To overcome this, OpenAI uses a combination of techniques called model parallelism instead of the traditional one, namely embarrassingly parallel.

Running Inference

Unlike other language models like BERT or transformerXL, which require additional adjustments for getting multiple tasks performed, GPT-3 can handle all downstream tasks with one single model. 

This is not the case with GPT-3. It’s a versatile tool that can be used for many language tasks without making big changes. Last year, OpenAI already showed GPT-2’s potential as a turn-key solution for a range of downstream NLP tasks without even fine-tuning. 

On top of it, the new generation, GPT-3, is a more formatted approach for running inference and demonstrates superior performance. It works in a specific way. You can give it no examples, just one example, or a few examples related to the task you want it to do. Then, you provide a prompt (like a question or instruction), and GPT-3 uses all this information to figure out what to say, step by step. It’s like teaching it a little bit and letting it do the rest.

We will further understand this with an example given below. Conclusively, GPT-3 is like a multi-talented model that can handle different language jobs with a bit of guidance.

Expected Outcomes

When it comes to analyzing the GPT-3’s performance, it’s a must to see how fairly it deals with the task/question or query asked. Let’s delve into the outcomes and capabilities of this language model:

• Text Generation:

GPT-3 truly outperforms in text generation ability. It possesses the remarkable ability to generate human-like quality content. As and when you provide GPT-3 with a title, subtitle, and a simple prompt like “Article:” GPT-3 can craft short articles, roughly 200-400 words in length, that often fool readers into believing they were written by a human. You can even further ask it to increase or decrease the length of the result provided.

• General NLP Tasks:

Apart from generating human-like responses, the wow feature offered by GPT models is the ability to get re-programmed without any additional fine-tuning. 

GPT-3 is capable of performing dozens of downstream tasks, ranging from the usual players such as machine translation and question and answer to unexpected new tasks such as arithmetic computation and one-shot learning of novel words.

Let’s Test the GPT-3 Model

Here is a practical example of how GPT-3 responds to the given task or assignment. Let’s practical test the model and its response generation capabilities. The main intent here is to showcase how GPT-3 performs in response to specific tasks or prompts, including an explanation of various techniques such as zero-shot, one-shot, and few-shot learning.

Zero-shot

We will explain the assignment to GPT-3 in Zero-shot before asking our query in the prompt. GPT-3 makes an effort to comprehend the description and provide the answer to the question.

• Assignment: The task assigned here is to “Translate a sentence from English to French”.  
• Prompt design: Please help
• Solution by GPT 3 would be: S’il vous plaît, aidez-moi

One shot

With One shot, you need to provide one example and the task description before asking any question or query to GPT-3.

• Assignment: The task assigned here is to “Translate a sentence from English to French”.
• One example: Please help – S’il vous plaît, aidez-moi
• Prompt design: Please help
• Solution by GPT 3 would be: S’il vous plaît, aidez-moi

Few shots

Under a few shots, we’ll provide a number of samples and a work description before any questions.

• Assignment: The task assigned here is to “Translate a sentence from English to French”.
• One example: Please help – S’il vous plaît, aidez-moi 
• Second example: I want an apple – Je veux une pomme
• Another example: I like bananas – J’aime les bananes

• Prompt design:  I want an apple 
• Solution by GPT 3 would be: Je veux une pomme

Conclusion

After going through this detailed guide on how to train GPT-3, you must be convinced that GPT-3 is a remarkable innovation. It surpasses human tendencies and boosts the capabilities of language models to an incredible level. With its advanced capabilities, anyone can unleash the full potential of GPT-3 and take their NLP applications to the next level.

So, with GPT-3, the possibilities are endless. So try today and see what new exciting ideas and breakthroughs the future holds for you.