Rotary Positional Embeddings: Combining Absolute and Relative
7:38
Which transformer architecture is best? Encoder-only vs Encoder-decoder vs Decoder-only models
8:33
The KV Cache: Memory Usage in Transformers
14:06
RoPE (Rotary positional embeddings) explained: The positional workhorse of modern LLMs
9:50
How do Transformer Models keep track of the order of words? Positional Encoding
10:23
Grandes Modelos Conceituais (LCMs) da Meta: A Era da IA depois dos LLMs?
23:26
Rotary Position Embedding explained deeply (w/ code)
13:39
How Rotary Position Embedding Supercharges Modern LLMs
18:08