Best of Transformers — February 2025

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   Article

   Data Engineer Things·1y

   10 minutes are all you need to understand how Transformers work in LLM

   Understanding how transformers work in large language models (LLMs) can be achieved quickly by breaking down the steps involved in the process. Starting from tokenization, where input data is converted into tokens, these tokens are then embedded into numerical representations understood by the model. These embeddings are processed through multiple transformer layers that use attention mechanisms to determine the importance of each token in relation to others. Finally, the processed data is projected back onto the vocabulary to predict the next token in a sequence. This foundational knowledge helps in exploring further intricacies of models like GPT-2.

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   Article

   Collections·1y

   Building a Vision Transformer from Scratch

   Vision Transformers (ViTs) are transforming computer vision by using self-attention mechanisms, enhancing tasks like image classification, object detection, and image segmentation. This guide covers the core components and practical implementation of ViTs, including image preprocessing, patch embeddings, the multi-head attention mechanism, and assembling the complete model. It also offers a comparison between ViTs and other models like CLIP and SIGP to highlight their efficiency and flexibility.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·1y

   Transformer vs. Mixture of Experts in LLMs

   Mixture of Experts (MoE) is an architecture used to enhance Transformer models by employing different 'experts' to improve performance. Transformers use feed-forward networks, while MoE models select a subset of smaller, specialized networks during inference, making operations faster. MoE faces training challenges such as some experts becoming under-trained. Solutions include adding noise to expert selection and limiting the number of tokens an expert processes. MoE models have more parameters but activate only a few during inference, leading to efficiency improvements.

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