---
license: mit
datasets:
- wikimedia/wikipedia
language:
- en
metrics:
- bleu
- rouge
library_name: adapter-transformers
pipeline_tag: reinforcement-learning
tags:
- code
---
# 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
    ```

2. Install the required packages:
    ```bash
    pip install torch numpy
    ```

## Usage

1. Prepare your dataset and vocabulary.

2. 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.