Generative Pre-trained Transformer 1 (GPT-1)
The GPT-1 architecture is a twelve-layer decoder-only transformer, utilizing twelve masked self-attention heads, with 64-dimensional states each (for a total of 768). The model utilizes the Adam optimization algorithm, diverging from simple stochastic gradient descent; the learning rate was progressively increased from zero over the first 2,000 updates to a peak of 2.5×10−4, followed by annealing to 0 using a cosine schedule.
GPT-1 was trained on the BooksCorpus dataset, containing over 7,000 unique unpublished books, amounting to nearly 800 million words. This extensive corpus provided a diverse range of vocabulary, narrative styles, and topics, enabling the model to develop a broad understanding of language patterns and structures.
GPT-1 showcased remarkable capabilities in various natural language processing tasks, such as:
- Language modeling
- Machine translation
- Text summarization
- Question answering
The model's performance was particularly notable in tasks requiring contextual understanding and the generation of coherent, contextually relevant text.
This repository provides implementation details and resources for GPT-1. Users can utilize this model for various NLP tasks, adapting it to specific requirements and datasets.
Instructions on how to set up and run GPT-1 in your environment are provided, along with examples of usage.
Details about necessary prerequisites, including software and hardware requirements.
Step-by-step guide to installing and configuring GPT-1 on your system.
We welcome contributions from the community. Please refer to the CONTRIBUTING.md for guidelines on how to contribute.
For the versions available, see the tags on this repository.
- [Your Name] - Mind-Interfaces/GPT-1/
- Akshat Pandey - Pytorch implementation of GPT-1
- Yu Guo - GPT-1 结构的简单复现
- Sosuke Kobayashi - Homemade BookCorpus
- Acknowledgements to anyone whose resources were used
This project is licensed under MIT - see the LICENSE file for details.