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*by GPT-4 & Crumb*
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***Note***: *this model is in the process of being re-evaluated because it was retrained.*
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### Introduction
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Transformer models have become a popular choice for natural language processing (NLP) tasks due to their ability to handle long-range dependencies and their superior performance on various NLP benchmarks. The transformer model architecture was introduced in 2017 by [Vaswani et al](https://arxiv.org/abs/1706.03762). and has since been used in many state-of-the-art models such as BERT and GPT. The decoder-only transformer model is a variant of the transformer model that has is commonly used for generative tasks in NLP. It uses masked self-attention to predict the next token in a sequence and has been shown to be powerful at predicting sequences of text.
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*by GPT-4 & Crumb*
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### Introduction
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Transformer models have become a popular choice for natural language processing (NLP) tasks due to their ability to handle long-range dependencies and their superior performance on various NLP benchmarks. The transformer model architecture was introduced in 2017 by [Vaswani et al](https://arxiv.org/abs/1706.03762). and has since been used in many state-of-the-art models such as BERT and GPT. The decoder-only transformer model is a variant of the transformer model that has is commonly used for generative tasks in NLP. It uses masked self-attention to predict the next token in a sequence and has been shown to be powerful at predicting sequences of text.
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