The blog post by Hafedh Hichri delves into the intricacies of tensor dimensions within transformer models, specifically focusing on a text generation model with a decoder-only architecture. The content emphasizes the importance of understanding matrix multiplication and tensor dimensionality, starting from tokenization where a sentence is converted into a tensor form and transformed through embedding layers. It explains how the embedding layer enhances semantic relationships between words and how positional encoding maintains token order. The post also details the function of masked multi-head attention in the decoder layers, ensuring each token attends to itself and preceding tokens only. The article covers tensor transformations through the attention mechanism, including the calculation of attention weights and their impact on tensor shapes, concluding with the feed-forward network's role in non-linear transformations. The final section describes how the language-model head outputs a tensor ready for softmax application, facilitating model training and generation of new tokens. The article is appreciated for its clarity in explaining complex concepts, and readers are encouraged to explore the author's portfolio for further insights.