Technology
Building GPT: From Principle to Implementation.
Generative pre-trained transformer (GPT) is a neural network structure that has made great achievements in the field of natural language processing. Recently, a developer shared a hands-on guide to building GPT from scratch with numpy**. This article will delve into the key concepts of GPT principles, input and output processing, text generation methods, sampling techniques, model training, and transfer learning, and through detailed explanations and examples, it aims to help readers understand GPT deeply and stimulate their enthusiasm for the field of natural language processing
1.Introducing GPT
1.1 Definition of GPT.
GPT stands for Generative Pre-trained Transformer, which is based on the Transformer neural network structure. The article will elaborate on the concepts of generative, pre-trained, and transformer, revealing the basic principles of this model.
1.2 Features of GPT.
GPT's features include text generation, pre-training, and a Transformer decoder. These characteristics make GPT excellent in natural language generation tasks, becoming a pioneer in a variety of tasks.
2.Input-output processing.
2.1 Input Processing.
GPT accepts a series of integer tokens as input. This section will explain how to map text to a sequence of tokens represented by integers, and how tokens are mapped to integers via tokenizers
2.2 Output processing.
The output of the model is a two-dimensional array representing the model's probability for each token. The article will detail how to decode these outputs to get the next token that generates the text.
3.Generate text.
3.1 Greedy decoding.
Greedy decoding is a simple way to generate text, choosing the token with the highest probability as the token. This section will show how to generate text using greedy decoding.
3.2 Autoregressive generation.
Autoregressive generation is an iterative method of generating text by repeatedly taking the next token from the model and appending it back to the input sequence。This process of generating text will be described in detail.
4.Sampling techniques.
4.1 Random sampling.
Random sampling is a method that introduces randomness by randomly selecting tokens from a probability distribution so that the generated text has diversity. In addition, the article will introduce how to combine technologies such as TOP-K, TOP-P, and temperature to improve the output quality
5.Model training.
5.1 Loss function.
In model training, the cross-entropy loss of the language modeling task is used as the optimization goal. The article will explain how to build a loss function and how to train it with gradient descent
5.2 Self-supervised learning.
Self-supervised learning is a key step in GPT training, extending the training data by generating input label pairs from raw text。The article will explain the advantages and implementation methods of self-supervised learning.
6.Transfer learning.
6.1 Pre-training.
GPT adopts the method of pre-training, which is first pre-trained on large-scale data, so that the model can learn rich language knowledge。The process and benefits of pre-training will be described in detail in the article.
6.2 Fine-tuning.
Fine-tuning is the process of tuning a model for a specific task on the basis of pre-training. This section will explain how to fine-tune to suit specific mission needs.
6.3 Transfer Learning Strategies.
The article will elaborate on a combination of pre-training and fine-tuning, i.e., transfer learning strategies. This strategy allows the model to excel in multiple tasks.
7.Practical implementation.
7.1 **Structure.
The article will cover the actual structure, including the encoderpy、utils.py、gpt2.py and gpt2 picopy。These files are the basis for implementing GPT from scratch.
7.2 **Demo.
Through a hands-on demonstration, the reader will learn how to load tokenizers, model weights, and hyperparameters, as well as how to use a CLI application for GPT generation
8.In-depth analysis of GPT
8.1 Model performance analysis.
Perform an in-depth analysis of GPT model performance,** its performance on different tasks, and compare the impact of different model sizes on performance.
8.2 Application field exploration.
Explore the application of GPT in the field of natural language processing, including text generation, dialogue systems, summary generation, etc., and demonstrate its wide applicability.
Epilogue. Through the in-depth study of this article, readers will have a clearer understanding of the construction process of GPT。From principle to implementation, the power and flexibility of GPT are demonstrated in the article. It is hoped that this article will provide readers with a wealth of knowledge and stimulate their innovative thinking in the field of natural language processing. May this article be a useful guide to learning and understanding GPT, leading readers deep into the vastness of the world of artificial intelligence.