The Transformer: The Life of a Token
The article provides an in-depth exploration of the internals of a modern dense transformer, specifically focusing on the Rnj 1.5 model. It discusses various components such as the forward pass, normalization layers, and attention mechanisms. The post also highlights the collaborative efforts of the team behind Rnj 1.5 and its advancements over previous versions.
- ▪Rnj 1.5 extends the context window from 32k to 160k and scores 79% on RULER with a 128k context window.
- ▪The article is structured into seven parts, covering topics like the forward pass, normalization, and multi-head self-attention.
- ▪The tokenizer used in the model maps text to a sequence of tokens, which are then processed through various stages before training.
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Inside the Transformer: The Life of a TokenA deep dive into a modern dense transformer: YaRN, hybrid attention, soft capping, QK normalization, FLOPs/token, cluster sizing, and moreMay 26, 2026In this post, I'll do a deep dive into the internals of a modern dense transformer [1]. I'll focus exclusively on the forward pass on a single GPU, as if we were about to perform a training step, while ignoring the backward pass and distributed systems details (in practice, large Transformers are sharded across multiple devices during both training and inference).As a running example, I'll use the exact architecture of Rnj 1.5 - a model I worked on with my team at Ashish Vaswani's AI Lab (Essential AI Labs).💡The team behind Rnj-1.5:Rnj 1.5 could not have happened without an amazing group of people…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Aleksagordic.
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