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What Is a Transformer Model?
A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.
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The Transformer Model
We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus to the details of the Transformer architecture itself to discover how self-attention can be implemented without relying on the use of recurrence and convolutions.
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How Transformers Work: A Detailed Exploration of Transformer Architecture
Explore the architecture of Transformers, the models that have revolutionized data handling through self-attention mechanisms.
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Transformer Explainer
Transformer Explainer features a live GPT-2 (small) model running directly in the browser. This model is derived from the PyTorch implementation of GPT by Andrej Karpathy's nanoGPT project and has been converted to ONNX Runtime for seamless in-browser execution.
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A Guide to Transformer Architecture
In this guide, we take an in-depth look at the transformer architecture, including its core components, what distinguishes it from its predecessors, and how it works.
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A Gentle Introduction to Transformer Architecture and Relevance to Generative AI
This post is about Transformer Architecture, its relevance to Generative AI, tips and guidance on customizing your interaction with the large language models.