Llama3 Implementation from Scratch

Project Description

What is the project about?

This project is a ground-up implementation of the Llama3 language model. It demonstrates the inner workings of the model, performing all calculations (tensor and matrix multiplications) manually, without relying on high-level deep learning libraries (except for the initial token embedding).

What problem does it solve?

The project serves primarily as an educational resource. It demystifies the complex operations within a large language model (LLM) like Llama3 by showing, step-by-step, how the model processes input and generates predictions. It allows users to understand the core mechanisms of a transformer-based LLM. It doesn't "solve" a practical problem in the traditional sense, but rather solves the problem of understanding.

What are the features of the project?

• From-Scratch Implementation: Builds Llama3 using fundamental tensor operations.
• Direct Weight Loading: Loads model weights directly from Meta's provided model files.
• Step-by-Step Explanation: Provides a detailed walkthrough of each stage, including:
  • Tokenization using tiktoken.
  • Token embedding.
  • RMS Normalization.
  • Multi-Headed Self-Attention (Query, Key, Value generation).
  • Rotary Positional Embeddings (RoPE).
  • Attention masking.
  • SwiGLU Feedforward Network.
  • Layer stacking.
  • Output decoding.
• Visualization: Includes visualizations of key matrices and processes (e.g., RoPE, attention heatmaps).

What are the technologies used in the project?

• Python: The primary programming language.
• PyTorch: Used for tensor operations and loading the pre-trained model weights. Crucially, PyTorch's higher-level nn modules are avoided for most of the implementation, except for the initial embedding layer.
• tiktoken: OpenAI's library for tokenization (specifically, Byte Pair Encoding).
• JSON: Used for loading model configuration parameters.
• Matplotlib: Used for visualizations.

What are the benefits of the project?

• Educational: Provides a deep understanding of the Llama3 architecture and transformer models in general.
• Transparency: Demystifies the "black box" nature of LLMs by showing the underlying calculations.
• Debugging/Customization: The detailed implementation makes it easier to understand potential issues and could serve as a starting point for modifications or research.
• Conceptual Clarity: Reinforces core concepts like self-attention, positional encoding, and feedforward networks.

What are the use cases of the project?

• Learning: The primary use case is for educational purposes, helping students, researchers, and developers understand LLMs.
• Research: Could be used as a basis for experimenting with modifications to the Llama3 architecture.
• Debugging: The detailed implementation can help in understanding and debugging issues in larger, more complex LLM implementations.
• Conceptual Understanding: It is a great resource for anyone who wants to understand the inner workings of a transformer model.