/explore
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sigmoidprime.com | ||
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comsci.blog
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| | | | | In this tutorial, we will implement transformers step-by-step and understand their implementation. There are other great tutorials on the implementation of transformers, but they usually dive into the complex parts too early, like they directly start implementing additional parts like masks and multi-head attention, but it is not very intuitional without first building the core part of the transformers. | |
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blog.eleuther.ai
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| | | | | Rotary Positional Embedding (RoPE) is a new type of position encoding that unifies absolute and relative approaches. We put it to the test. | |
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bdtechtalks.com
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| | | | | The transformer model has become one of the main highlights of advances in deep learning and deep neural networks. | |
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harvardnlp.github.io
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| | | [AI summary] The provided code is a comprehensive implementation of the Transformer model, including data loading, model architecture, training, and visualization. It also includes functions for decoding and visualizing attention mechanisms across different layers of the model. The code is structured to support both training and inference, with examples provided for running the model and visualizing attention patterns. | ||
