karpathy/ng-video-lecture
Ng Video Lecture
This project is an educational implementation of a small-scale generative pre-trained transformer designed to teach the fundamentals of neural network architecture and training. It serves as a reference implementation and tutorial for constructing a text-generating neural network from scratch.
The codebase demonstrates the mechanics of tokenization, self-attention, and the construction of a lightweight language model. It focuses on the step-by-step process of building a generative model to illustrate how large language models are constructed.
The implementation covers transformer-based architecture, including multi-head attention, feed-forward networks, and causal masking. It utilizes PyTorch for tensor computation and employs backpropagation-based learning to train the model on sequential text data.
Features
- Transformer Architectures - Implements a transformer architecture utilizing multi-head attention and feed-forward networks for text generation.
- Educational Implementations - Provides a from-scratch implementation of a generative transformer to teach neural network internals.
- Educational Neural Network Implementations - Offers a pedagogical implementation of neural network components built from first principles.
- Language Model Architectures - Provides a framework for constructing language model architectures that predict subsequent tokens.
- PyTorch Tensor Operations - Uses PyTorch for high-speed tensor operations and dynamic computational graph management.
- Causal Masking - Implements causal masking to prevent the model from attending to future tokens during training.
- Reference Models - Implements a small-scale transformer model in PyTorch to demonstrate tokenization and self-attention.
- Token Embedding Layers - Provides token embedding layers that map discrete characters to high-dimensional dense vectors.
- GPT Implementations - Provides a concrete code implementation of the Generative Pre-trained Transformer architecture.
- Generative AI Tutorials - Provides a step-by-step tutorial on creating a text-generating neural network.
- Educational Implementations - Serves as a functional, readable codebase for teaching the internal mechanics of transformer architectures.
- Backpropagation - Implements the backpropagation algorithm to update network weights by calculating loss function gradients.
- Language Model Pre-training - Implements the pre-training process for a transformer model to learn general linguistic patterns.
- Multi-Layer Architectures - Employs a multi-layer architecture by stacking identical transformer blocks to increase model depth.
- Educational Model Training - Executes a training process for a small-scale model to demonstrate machine learning convergence concepts.
- Small Language Models - Constructs a lightweight, small-scale language model for experimentation and learning.
Star history
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