Project: Build a Large Language Model (From Scratch)
What is the project about?
This project is the official code repository for the book "Build a Large Language Model (From Scratch)." It provides the code and resources to guide readers through the process of developing, pretraining, and finetuning a GPT-like Large Language Model (LLM) from the ground up.
What problem does it solve?
The project demystifies the inner workings of LLMs by providing a hands-on, step-by-step approach to building one. It addresses the lack of understanding of how LLMs function internally, enabling users to create their own LLM and understand the underlying principles.
What are the features of the project?
- Step-by-step code for building a GPT-like LLM.
- Pretraining the LLM on unlabeled data.
- Finetuning the LLM for specific tasks like text classification and instruction following.
- Loading the weights of larger pretrained models for finetuning.
- Bonus materials, including alternative implementations, performance analysis, and user interface development.
- Docker Environment Setup.
What are the technologies used in the project?
- Python
- PyTorch
- Hugging Face Transformers (optional, for loading pretrained models)
- Tiktoken
- Ollama
What are the benefits of the project?
- Educational: Provides a deep understanding of LLM architecture and training.
- Practical: Enables users to build and customize their own LLMs.
- Comprehensive: Covers all stages of LLM development, from data preparation to deployment.
- Accessible: Designed to run on conventional hardware, making it accessible to a wide audience.
What are the use cases of the project?
- Learning: Understanding the inner workings of LLMs.
- Research: Experimenting with LLM architectures and training techniques.
- Development: Building custom LLMs for specific applications.
- Prototyping: Creating small, functional models for educational or experimental purposes.
