attention mechanism

There are many good resources explaining the transformer architecture online, but Rotary Position Embedding (RoPE) is commonly poorly explained or skipped entirely. RoPE was first introduced within the paper RoFormer: Enhanced Transformer with Rotary Position...

mechanism is on the core of recent day transformers. But scaling the context window of those transformers was a significant challenge, and it still is despite the fact that we're within the era...

The Attention Mechanism is commonly related to the transformer architecture, but it surely was already utilized in RNNs. In Machine Translation or MT (e.g., English-Italian) tasks, when you need to predict the following Italian...

Large Language Models (LLMs) deploying on real-world applications presents unique challenges, particularly when it comes to computational resources, latency, and cost-effectiveness. On this comprehensive guide, we'll explore the landscape of LLM serving, with a...

As transformer models grow in size and complexity, they face significant challenges by way of computational efficiency and memory usage, particularly when coping with long sequences. Flash Attention is a optimization technique that guarantees...