Llama3 From Scratch

This project is a manual reconstruction of the Llama 3 transformer architecture implemented as a PyTorch neural network. It serves as a reference for the internal mathematical structure and tensor flow of a transformer-based language model designed for next token prediction.

The implementation focuses on building the model from scratch using basic matrix operations and tensor manipulations. It demonstrates the manual construction of core components, including rotary positional embeddings, multi-head self-attention, and root mean square normalization.

The codebase covers the full inference pipeline, from text tokenization and token embedding generation to the use of gated linear units within a feed-forward network. It also includes the mechanisms for loading pre-trained model weights and configuration parameters to initialize the architecture.

The project is provided as a series of Jupyter Notebooks.

Features

Large Language Models - Implements a Llama 3 transformer architecture from scratch using fundamental tensor operations.

Transformer Language Models - Provides a manual reconstruction of the transformer-based language model architecture using PyTorch.

Attention Mechanisms - Calculates relationship scores between tokens using query and key matrix multiplication.

Neural Network Operations - Manages mathematical data flow through rotary embeddings, gated linear units, and normalization layers.

Multi-Head Attention Mechanisms - Implements multi-head attention to compute contextual relationships across parallel attention heads.

Neural Network Implementations - Implements a PyTorch-based neural network covering tokenization, rotary embeddings, and multi-head attention.

Rotary Positional Embeddings - Applies rotational shifts to query and key vectors to encode relative token positions.

Token Prediction - Predicts the most probable next token by calculating a probability distribution over the vocabulary.

Transformer Blocks - Constructs the model using sequential layers of attention and feed-forward networks via matrix operations.

Vector Embeddings - Maps discrete numerical tokens to high-dimensional continuous vectors to represent semantic meaning.

LLM - Offers a manual reconstruction of the Llama 3 architecture using basic matrix operations.

Gated Linear Units - Implements gated linear units to introduce non-linearity within the feed-forward network.

Normalization Layers - Stabilizes numerical computations using root mean square normalization layers.

Model Inference - Provides a system to load model weights and generate text predictions during inference.

Model Weight Management - Includes mechanisms to load pre-trained tensor weights and configuration parameters to initialize the network.

Neural Network Layers - Implements a SwiGLU feed-forward network using gated linear units for non-linear data processing.

RMS Normalizations - Stabilizes neural network activations by scaling tensors based on the root mean square of their elements.

Transformer Architectures - Serves as a reference for studying the internal mechanics of transformer architectures through manual implementation.

LLM - Provides a detailed reference for the internal mathematical structure and tensor flow of Llama 3.

LLM Development and Research - Implementation of a language model from basic matrix operations.

naklechallama3-from-scratch

Features

Star history