# Summary: The History of Open Source LLMs

## Introduction
This article explores the evolution and key components of Open Source Large Language Models (LLMs). It delves into the structural aspects, mechanics, and the role of transformers in LLMs, with a focus on the encoder, decoder, attention mechanisms, and embeddings.

## The Language Modeling Objective
Language models are trained to predict the next word in a sequence, a process known as the language modeling objective. This foundational concept underpins the training and functioning of LLMs.

## Structure and Mechanics of LLMs

### Transformers
Transformers have revolutionized the development of LLMs. They consist of an encoder-decoder architecture that processes sequences of text data.

#### Encoder
- **Role:** The encoder's task is to process the input text sequence and generate a context-aware representation.
- **Structure:** It is composed of multiple layers, each containing self-attention and feed-forward neural networks.
- **Self-Attention Mechanism:** This mechanism allows the model to weigh the significance of different words in a sequence, considering their relationships.

#### Decoder
- **Role:** The decoder generates the output text sequence, one word at a time, based on the encoder's representations and previously generated words.
- **Structure:** Like the encoder, the decoder is built with multiple layers incorporating self-attention and feed-forward networks.
- **Masked Self-Attention:** This variant ensures that the model only attends to previous words in the sequence, maintaining the causality in word prediction.

### Attention Mechanisms
Attention mechanisms are crucial for handling long-range dependencies in text data. They enable the model to focus on relevant parts of the input sequence, enhancing the contextual understanding.

#### Multi-Head Attention
- **Function:** It allows the model to jointly attend to information from different representation subspaces at different positions.
- **Implementation:** Multiple attention heads operate in parallel, providing diverse perspectives on the input data.

### Embeddings
Embeddings convert words into dense vectors that capture semantic meanings. These vectors are essential for the input representation in both the encoder and decoder.

- **Word Embeddings:** Pre-trained embeddings like Word2Vec or GloVe are often used, but LLMs can also learn embeddings during training.
- **Positional Encodings:** Since transformers do not inherently understand the order of words, positional encodings are added to embeddings to incorporate sequence information.

## Conclusion
The development of Open Source LLMs has been significantly influenced by the transformer architecture. Understanding the roles of the encoder, decoder, attention mechanisms, and embeddings provides insight into how these models achieve their remarkable performance in natural language processing tasks.