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jayksharma/super-large-language-model

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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:

bash

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

1.Install the required packages:

bash

    pip install torch numpy

Usage

1.Prepare your dataset and vocabulary.

1.Run the training script:

bash

    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.