This article explores the evolution from Transformers to Large Language Models (LLMs), detailing the mechanisms of self-attention and multi-head attention, the role of position embeddings, various types of transformer models, and the training and fine-tuning processes of LLMs.
This article provides an overview of the core concepts of Large Language Models (LLMs) in LangChain, including LLM components, prompt templates, indexing, memory, chains, and agents.
This article provides an overview of Large Language Models (LLMs), LLMs evolution and the core concepts of LangChain - an open source framework for building applications based on LLMs.
