LLMs
In the groundbreaking 2017 paper 8220 Attention Is All You Need 8221 Vaswani et al introduced Sinusoidal Position Embeddings to help Transformers encode positional information without recurrence or convolution This
Self attention the beating heart of Transformer architectures treats its input as an unordered set That mathematical elegance is also a curse without extra signals the model has no idea
In the evolving landscape of open source language models SmolLM3 emerges as a breakthrough a 3 billion parameter decoder only transformer that rivals larger 4 billion parameter peers on many
Developing intelligent agents using LLMs like GPT 4o Gemini etc that can perform tasks requiring multiple steps adapt to changing information and make decisions is a core challenge in AI