jayksharma/super-large-language-model

Super Large Language Model

This project implements a super-large language model using PyTorch. The model architecture is based on the Transformer model.

Files

• super_large_language_model.py: Contains the model architecture.
• train.py: Contains the training script.

Requirements

• Python 3.7+
• PyTorch 1.6+
• NumPy

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/super-large-language-model.git
   cd super-large-language-model
   

2. Install the required packages:

   pip install torch numpy
   

Usage

1. Prepare your dataset and vocabulary.

2. Run the training script:

   python train.py
   

Model Architecture

Type: Transformer

Style: Encoder-Decoder

The model is a Transformer-based language model. It consists of:

• An embedding layer for converting input tokens to vectors.
• Positional encoding to inject information about the position of tokens.
• A series of Transformer layers.
• A final linear layer for outputting the predictions.

Training

The training script trains the model on a dataset of texts. The dataset should be a list of strings, and the vocabulary should be a dictionary mapping characters to indices.

License

This project is licensed under the MIT License.