self.register_buffer("mask", torch.tril(torch.ones(1024, 1024)).view(1, 1, 1024, 1024))
Almost all state-of-the-art LLMs utilize the architecture. build a large language model from scratch pdf
import torch import torch.nn as nn import math one head focuses on syntax
Instead of performing a single attention function, we perform multiple "heads" in parallel. This allows the model to attend to different types of relationships simultaneously (e.g., one head focuses on syntax, another on semantic tone). The outputs of these heads are concatenated and projected back to the original dimension. build a large language model from scratch pdf