A detailed explanation of implementing trainable self-attention in LLMs, focusing on scaled dot product attention and matrix projections. The article breaks down how attention scores are calculated through query, key, and value matrices, demonstrating how five matrix multiplications can efficiently process token relationships.
A comprehensive guide detailing the implementation of Llama3 from scratch, covering model architecture, attention mechanisms, and optimization techniques like KV-Cache, with detailed code explanations and mathematical derivations.
Andrej Karpathy's deep dive into LLMs covers the complete lifecycle from pretraining to post-training, explaining tokenization, neural network architectures, and fine-tuning processes. The comprehensive guide explores how LLMs process information, handle hallucinations, and utilize reinforcement learning to improve performance and reasoning capabilities.
Large Language Models (LLMs) face significant limitations in OCR tasks due to their probabilistic nature and inability to maintain precise visual information, particularly struggling with complex layouts and tables. LLMs' vision processing architecture leads to critical errors in data extraction, including financial and medical data corruption, while also being susceptible to prompt injection vulnerabilities.